It's that time of year again -- when those of us in the U.S. must deal with numbered forms and lettered schedules. In this light, I wish to share a recent piece of correspondence:
Dear Dr. Robison:
After great difficulty (must your handwriting be so atrocious?) I have reviewed the accounts at your business enterprise. I regret to inform you that two of your accounts, with ATP Corp and NAD(P)H Ltd, are grossly out of balance. While you are running a deficit with the former and a surplus with the latter, as we have discussed previously these separate accounts cannot be merged. Your enterprise is doomed to failure (and I think it goes without saying that some sort of Madoffian scheme will not be countenanced by me). You must bring these into balance or your enterprise would fail, never mind the horror of trying to explain this in an audit.
I realize I am not qualified to comment on the technical aspects of your effort. However, may I suggest you get out of the lab more and get some fresh air? Perhaps some oxygen would stimulate your activity in a most productive way?
Sincerely,
Colin Escherich, C.P.A.
Saturday, February 28, 2009
Sunday, February 08, 2009
Any Genome Sequence You Want, As Long As It's Human
It's been interesting reading dispatches coming from bloggers Dan Kobolt and Daniel MacArthur who are attending the Marco Island conference, the big yearly confab on bleeding edge sequencing technology. How have I resisted this conference for so long, especially with the climate draw???
One company that is again receiving a lot of attention is Complete Genomics, which is proposing to build a set of sequencing centers to sequence human genomes at $5K a pop. What is striking is that their business model is to sequence only human genomes and nothing else, which particularly surprised Daniel MacArthur at Genetic Futures.
As a biologist and someone fascinated with all genomes, such a policy is not a welcome thought. But, as someone who has worked in an industrial high-throughput production facility, I think I can reverse engineer the logic pretty well (I have no connections to or inside information from the company).
Why would you want to do this? Simplicity. By focusing on only a single genome, all sorts of simplifications are created. Complexity costs significant money & time, and it is often what seems trivial that ends up being very costly. Just allowing a second genome in the door creates all sorts of additional work on the software side, and if that second source requires different sample prep that's an additional headache on the lab side.
Having only one genome kicking around also creates some interesting opportunities for quality control both for each sample and for the whole factory (which is what they are talking about building: a sequencing factory). One genome means only one reference sequence to compare against & one set of pathological problems for their assembly algorithm to be fortified against. One genome also means that if you see another genome in your data, you know something is wrong -- and if you see the same one genome repeatedly you may have a factory-wide problem.
"Any color you want so long as it is black" got Ford to the top of the U.S. automotive heap, but it didn't keep them there -- I believe that GM's offering colors helped push them into first. So will the market support Complete's vision? I think it can.
Complete is apparently talking about running a million genomes per year. At $5K each, that would be $5 billion, some serious cash flow. I don't know if they've estimated the market correctly, but it doesn't seem ridiculous. If a large fraction of the world's wealthy decide to sequence their genomes (and their children's too) and if sequencing tumors becomes semi-routine, a few million human genomes a year doesn't seem totally ridiculous. Of course, Complete would have to fight with all the other players for a share.
That implies a question: what comparable markets are they giving up? I'd love to see broader "zoonomics", where we go through the living world sequencing everything, but that's all going to be grant funded. Smaller genomes may also be completely mismatched with this sort of technology -- without some sort of multiplexing (complexity!). Similarly, it's not easy to see some big commercial market for metagenomics -- it will remain fascinating & there's no end to the ecological niches to explore, but who in the private sector is going to pony up major money for it? Oncogenic mouse models will supply lots of tumors for sequencing, but again probably not a big private sector activity.
The one area I can almost envision is sequencing valuable livestock or agricultural lines to understand their complete makeup. If this were done not only for parentals but for offspring in breeding programs, then perhaps a big market would be generated. But, is it really worth sequencing to completion or will some cheaper technology for skimming the surface suffice? If there is a market, then a logical business direction for Complete might be to do a joint venture or spinout focusing on alternate genomes -- but either the prize would need to be big or the one genome business model failing for that to be worth diverting attention.g
One company that is again receiving a lot of attention is Complete Genomics, which is proposing to build a set of sequencing centers to sequence human genomes at $5K a pop. What is striking is that their business model is to sequence only human genomes and nothing else, which particularly surprised Daniel MacArthur at Genetic Futures.
As a biologist and someone fascinated with all genomes, such a policy is not a welcome thought. But, as someone who has worked in an industrial high-throughput production facility, I think I can reverse engineer the logic pretty well (I have no connections to or inside information from the company).
Why would you want to do this? Simplicity. By focusing on only a single genome, all sorts of simplifications are created. Complexity costs significant money & time, and it is often what seems trivial that ends up being very costly. Just allowing a second genome in the door creates all sorts of additional work on the software side, and if that second source requires different sample prep that's an additional headache on the lab side.
Having only one genome kicking around also creates some interesting opportunities for quality control both for each sample and for the whole factory (which is what they are talking about building: a sequencing factory). One genome means only one reference sequence to compare against & one set of pathological problems for their assembly algorithm to be fortified against. One genome also means that if you see another genome in your data, you know something is wrong -- and if you see the same one genome repeatedly you may have a factory-wide problem.
"Any color you want so long as it is black" got Ford to the top of the U.S. automotive heap, but it didn't keep them there -- I believe that GM's offering colors helped push them into first. So will the market support Complete's vision? I think it can.
Complete is apparently talking about running a million genomes per year. At $5K each, that would be $5 billion, some serious cash flow. I don't know if they've estimated the market correctly, but it doesn't seem ridiculous. If a large fraction of the world's wealthy decide to sequence their genomes (and their children's too) and if sequencing tumors becomes semi-routine, a few million human genomes a year doesn't seem totally ridiculous. Of course, Complete would have to fight with all the other players for a share.
That implies a question: what comparable markets are they giving up? I'd love to see broader "zoonomics", where we go through the living world sequencing everything, but that's all going to be grant funded. Smaller genomes may also be completely mismatched with this sort of technology -- without some sort of multiplexing (complexity!). Similarly, it's not easy to see some big commercial market for metagenomics -- it will remain fascinating & there's no end to the ecological niches to explore, but who in the private sector is going to pony up major money for it? Oncogenic mouse models will supply lots of tumors for sequencing, but again probably not a big private sector activity.
The one area I can almost envision is sequencing valuable livestock or agricultural lines to understand their complete makeup. If this were done not only for parentals but for offspring in breeding programs, then perhaps a big market would be generated. But, is it really worth sequencing to completion or will some cheaper technology for skimming the surface suffice? If there is a market, then a logical business direction for Complete might be to do a joint venture or spinout focusing on alternate genomes -- but either the prize would need to be big or the one genome business model failing for that to be worth diverting attention.g
Wednesday, February 04, 2009
Trading off an argument from Scrubs
I was watching Scrubs (My New Role) last night & there was an exchange that I think should be a discussion point for everyone involved in medicine, though it wasn't the point the script writers really hammered on.
The setup is that a nurse was trying to get a doctor to change the antibiotic for a patient. The nurse's argument was that azithromycin required once daily dosing and would free her up for doing other things, where as the doctor's selection of clindamycin meant 4 times daily dosing. The doctor replied in a condescending way that she had gone to med school, the nurse hadn't, and therefore the script would stand as written.
Now, the theme of the episode was this sort of professional interaction -- where someone higher on the professional totem pole disrespects someone lower. An important issue, to be sure. But I think, especially in these days when we are more than ever concerned about the cost of healthcare & how to deliver effective healthcare economically, the specific argument deserves more attention.
Now, I'll confess I haven't gone to med school & I have no particular expertise in antibiotics, other than practical experience. For example, my wife is allergic to huge numbers, TNG broke out with Augmentin, doxycycline gives me a stomachache if I try to take it on an empty stomach & penicillin is mostly excreted, not metabolized & you'll notice this in the bathroom once it has cleared the infection from your nasal passages. But I can't reasonably discuss azithromycin vs clindamycin on actual facts, so I'll use them as proxies for some hypotheticals.
Suppose, for example, that there was absolutely no clinical difference between the two. They both had the same spectrum of treatable bacteria, the same risk of similar side effects, no contraindications in this patient and both had the same cost. Then clearly the nurse is right and the doctor wrong, as that once-a-day dosing frees a valuable resource (the nurse). In other words, under these conditions the drug choice for a patient is neutral for that patient but has important ramifications for other patients at the hospital.
But what about the less clear cases. For example, suppose all of the above conditions were met except equal cost; the once daily med is significantly more expensive (e.g. azithromycin before it went off patent). On the one hand, my argument still holds unless it is a huge cost difference -- several minutes of a nurses' time is worth quite a bit (like most hospitals, the one on Scrubs is portrayed as being cash strapped & short on nurses). However, that more convenient drug costs real money, whereas the nurse's saving is in opportunity cost: an accountant browsing the budget is likely to see the one but not the other even if both are real.
Now let's muddy the water further. Suppose they two drugs are clinically not precisely comparable but similar -- imagine if clindamycin is slightly broader spectrum or has a slightly lower risk of side effects. Now it becomes a really sticky wicket -- what additional risk to this patient is acceptable in order to reduce the risks to other patients (due to getting better nursing care).
That last one is the sort that really is troublesome. We never like explicitly to risk one person to help multiple others, but we are often less troubled when we do it implicitly. I won't claim to be an ethics expert, so I'll leave it at that. But I think these scenarios embody real situations which will be faced, such as sometimes an expensive drug is better than a cheaper one & (not to say this is always or even often true, just that it isn't always false). Or more generally: health care reform will be complex, because health care is complex.
The setup is that a nurse was trying to get a doctor to change the antibiotic for a patient. The nurse's argument was that azithromycin required once daily dosing and would free her up for doing other things, where as the doctor's selection of clindamycin meant 4 times daily dosing. The doctor replied in a condescending way that she had gone to med school, the nurse hadn't, and therefore the script would stand as written.
Now, the theme of the episode was this sort of professional interaction -- where someone higher on the professional totem pole disrespects someone lower. An important issue, to be sure. But I think, especially in these days when we are more than ever concerned about the cost of healthcare & how to deliver effective healthcare economically, the specific argument deserves more attention.
Now, I'll confess I haven't gone to med school & I have no particular expertise in antibiotics, other than practical experience. For example, my wife is allergic to huge numbers, TNG broke out with Augmentin, doxycycline gives me a stomachache if I try to take it on an empty stomach & penicillin is mostly excreted, not metabolized & you'll notice this in the bathroom once it has cleared the infection from your nasal passages. But I can't reasonably discuss azithromycin vs clindamycin on actual facts, so I'll use them as proxies for some hypotheticals.
Suppose, for example, that there was absolutely no clinical difference between the two. They both had the same spectrum of treatable bacteria, the same risk of similar side effects, no contraindications in this patient and both had the same cost. Then clearly the nurse is right and the doctor wrong, as that once-a-day dosing frees a valuable resource (the nurse). In other words, under these conditions the drug choice for a patient is neutral for that patient but has important ramifications for other patients at the hospital.
But what about the less clear cases. For example, suppose all of the above conditions were met except equal cost; the once daily med is significantly more expensive (e.g. azithromycin before it went off patent). On the one hand, my argument still holds unless it is a huge cost difference -- several minutes of a nurses' time is worth quite a bit (like most hospitals, the one on Scrubs is portrayed as being cash strapped & short on nurses). However, that more convenient drug costs real money, whereas the nurse's saving is in opportunity cost: an accountant browsing the budget is likely to see the one but not the other even if both are real.
Now let's muddy the water further. Suppose they two drugs are clinically not precisely comparable but similar -- imagine if clindamycin is slightly broader spectrum or has a slightly lower risk of side effects. Now it becomes a really sticky wicket -- what additional risk to this patient is acceptable in order to reduce the risks to other patients (due to getting better nursing care).
That last one is the sort that really is troublesome. We never like explicitly to risk one person to help multiple others, but we are often less troubled when we do it implicitly. I won't claim to be an ethics expert, so I'll leave it at that. But I think these scenarios embody real situations which will be faced, such as sometimes an expensive drug is better than a cheaper one & (not to say this is always or even often true, just that it isn't always false). Or more generally: health care reform will be complex, because health care is complex.
Bacteria can mobilize a fifth column
I recently had to deal with a bacterial upper respiratory infection. Something to ponder about such problems is that not only did the little nasty have to gain a foothold on my immune system, but it also had to elbow a lot of other bacteria out of the way. After all, my respiratory tract is open to the air and is far from sterile; there is a whole ecosystem of bugs which generally get along with me. For an infection to take hold, either one of the regular residents has to go bad or the newcomers must steal some space.
A recent abstract in PNAS (alas, not an open access paper) provides a fascinating window on how that elbowing takes place. Staphylococcus aureus (aka the home front) is a standard resident of the respiratory tract (which, of course, can be nasty on its own if it gets through the skin) which Streptococcus pneumoniae (charming moniker! aka the invaders) must push aside. It turns out that one weapon the invaders use is hydrogen peroxide (H2O2), a staple of many home medicine cabinets -- though not mine growing up; Dad still favors tincture of iodine (curiously, cuts & scrapes often went unreported!).
Okay, that seems straightforward. Well, except the question of why the invaders themselves don't suffer some blowback. But it actually gets more interesting, because it turns out the H2O2 dose is sub-lethal. Huh? The invaders come in with flame throwers but set them to warm & cozy?
But sub-lethal doesn't mean physiologically irrelevant. The dose is enough for the home front to worry, as H2O2 can cause all sorts of damage. Indeed, the dose is strong enough to set off the SOS system, a DNA damage response.
The SOS system has an interesting side angle. Many bacteria carry dormant viruses, better known as lysogenic phage, within their genome. These viral genomes are integrated within their hosts' DNA and generally keep quiet, getting a free replication ride every time their host divides. However, that free ride isn't much good if your host dies with you in it, so these phage listen to the SOS response -- and when they hear it they go into their lytic phase, pumping out lots of virus and generally killing their host on the way out.
So now we have a picture: spook the home front enough that a fifth column of phage rises within and destroys them. Nifty.
Except, we're back to the blowback problem -- unless the invaders are also free of lysogenic phage they're going to have the same problem. However, it turns out that H2O2 does not activate the SOS response in the invaders, because they apparently are resistant to H2O2's DNA-damaging effects.
Understanding that resistance is a next area for work. Potentially, disabling it would offer an interesting antibiotic angle -- an antibiotic that was specific for the invaders by letting them blow themselves up. That's a big stretch (and the economics of antibiotic development are horrendous -- hence very few companies try it or stay in it) so don't hold your breath (or cough) waiting for it -- but it is a fun aspect to ponder.
A recent abstract in PNAS (alas, not an open access paper) provides a fascinating window on how that elbowing takes place. Staphylococcus aureus (aka the home front) is a standard resident of the respiratory tract (which, of course, can be nasty on its own if it gets through the skin) which Streptococcus pneumoniae (charming moniker! aka the invaders) must push aside. It turns out that one weapon the invaders use is hydrogen peroxide (H2O2), a staple of many home medicine cabinets -- though not mine growing up; Dad still favors tincture of iodine (curiously, cuts & scrapes often went unreported!).
Okay, that seems straightforward. Well, except the question of why the invaders themselves don't suffer some blowback. But it actually gets more interesting, because it turns out the H2O2 dose is sub-lethal. Huh? The invaders come in with flame throwers but set them to warm & cozy?
But sub-lethal doesn't mean physiologically irrelevant. The dose is enough for the home front to worry, as H2O2 can cause all sorts of damage. Indeed, the dose is strong enough to set off the SOS system, a DNA damage response.
The SOS system has an interesting side angle. Many bacteria carry dormant viruses, better known as lysogenic phage, within their genome. These viral genomes are integrated within their hosts' DNA and generally keep quiet, getting a free replication ride every time their host divides. However, that free ride isn't much good if your host dies with you in it, so these phage listen to the SOS response -- and when they hear it they go into their lytic phase, pumping out lots of virus and generally killing their host on the way out.
So now we have a picture: spook the home front enough that a fifth column of phage rises within and destroys them. Nifty.
Except, we're back to the blowback problem -- unless the invaders are also free of lysogenic phage they're going to have the same problem. However, it turns out that H2O2 does not activate the SOS response in the invaders, because they apparently are resistant to H2O2's DNA-damaging effects.
Understanding that resistance is a next area for work. Potentially, disabling it would offer an interesting antibiotic angle -- an antibiotic that was specific for the invaders by letting them blow themselves up. That's a big stretch (and the economics of antibiotic development are horrendous -- hence very few companies try it or stay in it) so don't hold your breath (or cough) waiting for it -- but it is a fun aspect to ponder.
Monday, February 02, 2009
A Fatally Flawed Paper
I like to review manuscripts but don't do so very often. When I started this blog I thought I might often use it to play "If I had been the reviewer", but I haven't done that much. However, a paper came to my attention that I can't stop thinking about until I tackle it here.
As an aside, I find papers I review to fall into three categories. The first are very solid papers that I can find little to comment on; I might make a suggestion or two (often about data visualization), but if the core is solid there isn't much for the reviewer to do. The second category is the most frustrating: when I feel the paper is on the edges of my expertise & I start to question whether I should have agreed to review it (which is done after seeing an abstract). The third category is the one I can really dig into: seriously flawed papers. I think one of my reviews of a paper was approaching the length of the manuscript; the paper was badly flawed but there was a thread of substance that with a lot of work could be turned into something decent.
Anyway, I noticed this paper in the BioMedCentral Table of Contents extract which emailed to me weekly.
Sometimes when there has been some accident, a review of the circumstances leading up to it will reveal many opportunities for recognizing that a bad situation had been set up: the engineer ignored a stop signal or the dispatcher should have noticed the switch was set incorrectly. This paper, particularly one of its centerpiece findings, has that feel to it: there were many warning flags that something was amiss, but unfortunately the authors and the reviewers failed to see them.
When I first planned this critique, I was going to detail several examples. However, that would seem to lead to a very long post, so I will pick a few examples and claim that it is representative. If anyone wishes to challenge that claim, then I'll flesh out some more. Also, I feel the first example is particularly apropos because it is a bit of a centerpiece; it gets a lot of space (including a special figure) in the text.
It was this bit of text that caused me to raise my eyebrows as far as they could go (I wish I could do the Spock single-eyebrow raise, but I can't). The bolding is mine to emphasize the big surprises.
The first huge surprise is to find a kinase with so little sequence identity to its closest human counterpart. The DNA identity of human and chimp is routinely cited in the high 90 percent (how exactly you calculate it affects the final value) and they are our closest relatives. Finding a human-mouse ortholog identity of less than 31% would be stunning; for human-chimp it would be indescribably surprising. The second huge surprise is the claim of a hybrid Polo-CK1 kinase. The Polo box is a domain which recognizes phosphorylated peptides and is important in the activation & substrate recognition by Polo kinases. It is the signature of the Polo subfamily and has not been reported to be found on any other protein. The third surprise is in the dendrogram; it is claimed that this kinase has an affinity to CK1-type kinaess, but in their rooted dendrogram (source of rooting not explained, a serious error) this kinase is an outgroup to all of the other presented kinases! Without some true outgroups (ideally representatives of other key families), how can we tell what it is most similar to?
Now, a strong criticism of mine of this paper is that it relies too much on Ensembl-derived sequences and annotation. Ensembl is a great system & I have high respect for it, but it is also trying to do the very complex job of integrating a lot of other data with genomic sequences of varying quality and we are not scientists if we fully trust it to always be correct. It is much better to have a more definitive reference point; why rely on someone's hand sketched map if you have a USGS topographic section available? And for a solid anchor database, it is hard to beat the RefSeq human protein dataset. So, we take the sequence from their figure for this ORF
and our top hit is
That resolves all these questions: it's a straightforward ortholog of PLK3 (which explains the Polo boxes), not some noteworthy hybrid and the sequence identity is 90+% -- and that score is dropped a lot by some iffy regions like this
What's going on there? Well, most likely this is underlining the draft nature of the chimpanzee genome. I checked with TBLASTN, and there aren't ESTs around this region -- the chimp PLK3 is pretty much a pure gene prediction model -- a tough problem that has been tackled well but never perfectly. Plus, the underlying genomic data is, well, draft quality. Another TBLASTN search revealed that although this Ensembl prediction is from the middle of a large contig, the N-terminus of human PLK3 has a great match on another contig -- but from the same chromosome.
Okay, maybe that's a fluke. So here's another chimp kinase highlighted in the text
Again, the first thing to do is to search the ORF
Okay, so the human protein has been described previously: it is human protein kinase C zeta. Has the PB1 domain in PKCzeta and its implications been previously discussed? A quick PubMed search turned up two papers from earlier this decade (in Molecular Cell & JBC) which actually demonstrated the dimerization potential of the PKCzeta PB1 domain. So the PKC domain with a PB1 domain is not novel & noteworthy. What about the missing diacylglycerol-binding domain (that first big gap) in the chimp kinase? That could be interesting, so let's see what whether we can find any EST evidence to support it. Alas, the only EST evidence refutes it and identifies the gap as spurious(and both of these ESTs were deposited in October 2007 and the paper submitted in March 2008, so they are not an unfair criticism)
I checked in detail one more note (about the chimp protein ENSPTRP00000001185 and its human ortholog) about a domain architecture claimed to be unique to human & chimp due to a missing domain. Again, the RefSeq protein search revealed that the chimp protein is nearly identical to a known human kinase (MARK2) albeit greatly truncated -- and the missing domain is beyond the truncation point.
I haven't checked every kinase in the paper, but seeing the same classes of mistakes repeatedly doesn't give much hope. Comparing the human & chimp kinomes (or any other well-defined subset of genes) is a worthwhile enterprise -- so long as it is kept in mind that the chimp genome is a very rough draft and all appropriate computational controls are used. This paper, unfortunately, shows no awareness of either of these principles.
What irks me most about this sort of paper is that it gives all of us a bit of a black eye. Someone who saw the abstract & got excited would be in for a big letdown. It's hard enough to earn the respect of bench biologists without it being tossed away with poorly done analyses.
So what are the positive lessons to be learned? Here are a few tips
P.S. One way to put reviewers in a bad mood is to not supply your sequences. The supplementary materials for this paper do not have all of the ORFs; I pulled some out from their alignments with a custom script. Elsewhere via Google I found a collection linked to the work -- but with the whole predicted chimp proteome in it! Very unwieldy & slow to download!
P.P.S. For anyone interested in exploring further, here are the other sequences from the alignment in additional file 3. The number in the header was added by my script to indicate which alignment within that file the sequence was taken from.
As an aside, I find papers I review to fall into three categories. The first are very solid papers that I can find little to comment on; I might make a suggestion or two (often about data visualization), but if the core is solid there isn't much for the reviewer to do. The second category is the most frustrating: when I feel the paper is on the edges of my expertise & I start to question whether I should have agreed to review it (which is done after seeing an abstract). The third category is the one I can really dig into: seriously flawed papers. I think one of my reviews of a paper was approaching the length of the manuscript; the paper was badly flawed but there was a thread of substance that with a lot of work could be turned into something decent.
Anyway, I noticed this paper in the BioMedCentral Table of Contents extract which emailed to me weekly.
Comparative kinomics of human and chimpanzee reveals unique kinship and functional diversity generated by new domain combinations.. Now, back at MLNM I had for a while specialized in protein kinases, so it is a field of some interest. I hadn't kept up with the status of the chimpanzee genome sequencing, but there is a longstanding familial interest in this species so that was another angle of interest.
Sometimes when there has been some accident, a review of the circumstances leading up to it will reveal many opportunities for recognizing that a bad situation had been set up: the engineer ignored a stop signal or the dispatcher should have noticed the switch was set incorrectly. This paper, particularly one of its centerpiece findings, has that feel to it: there were many warning flags that something was amiss, but unfortunately the authors and the reviewers failed to see them.
When I first planned this critique, I was going to detail several examples. However, that would seem to lead to a very long post, so I will pick a few examples and claim that it is representative. If anyone wishes to challenge that claim, then I'll flesh out some more. Also, I feel the first example is particularly apropos because it is a bit of a centerpiece; it gets a lot of space (including a special figure) in the text.
It was this bit of text that caused me to raise my eyebrows as far as they could go (I wish I could do the Spock single-eyebrow raise, but I can't). The bolding is mine to emphasize the big surprises.
For example, a chimpanzee kinase classified as casein kinase 1 (ENSPTRP00000001150) on the basis of significant sequence similarity (31%) of the catalytic domain and excellent e-value (2e-16) with the casein kinase 1 from human. However this chimp kinase has a POLO BOX tethered to the kinase catalytic domain.
Thus this chimp kinase represents a hybrid CK1_POLO kinase. Interestingly ENSEMBL reports that ENSPTRP00000001150 has a high similarity with the human kinase ENSP00000361275. However, according to our classification protocol ENSP00000361275 is classified as a POLO kinase on the basis of 52% sequence identity with classical POLO kinases and excellent e-value of e-112. Figure 1 shows the dendrogram of the CK1 sub-family of kinases and it highlights the significant divergence of chimp homologue from its counterparts in other organisms
The first huge surprise is to find a kinase with so little sequence identity to its closest human counterpart. The DNA identity of human and chimp is routinely cited in the high 90 percent (how exactly you calculate it affects the final value) and they are our closest relatives. Finding a human-mouse ortholog identity of less than 31% would be stunning; for human-chimp it would be indescribably surprising. The second huge surprise is the claim of a hybrid Polo-CK1 kinase. The Polo box is a domain which recognizes phosphorylated peptides and is important in the activation & substrate recognition by Polo kinases. It is the signature of the Polo subfamily and has not been reported to be found on any other protein. The third surprise is in the dendrogram; it is claimed that this kinase has an affinity to CK1-type kinaess, but in their rooted dendrogram (source of rooting not explained, a serious error) this kinase is an outgroup to all of the other presented kinases! Without some true outgroups (ideally representatives of other key families), how can we tell what it is most similar to?
Now, a strong criticism of mine of this paper is that it relies too much on Ensembl-derived sequences and annotation. Ensembl is a great system & I have high respect for it, but it is also trying to do the very complex job of integrating a lot of other data with genomic sequences of varying quality and we are not scientists if we fully trust it to always be correct. It is much better to have a more definitive reference point; why rely on someone's hand sketched map if you have a USGS topographic section available? And for a solid anchor database, it is hard to beat the RefSeq human protein dataset. So, we take the sequence from their figure for this ORF
>3|Chimp|ENSPTRP00000001150
SLAHIWKARHTLLEPEVRYYLRQILSGLKYLHQRGILHRDLKLGNFFITENMELKVGDF
GLAARLEPPEQRKKTICGTPNYVAPEVLLRQGHGPEADVWSLGCVMYTLLCGSPPFETA
DLKETYRCIKQVHYTLPASLSLPARQLLAAILRASPRDRPSIDQILRHDFFTKGYTPDR
LPISSCVTVPDLTPPNPARSLFAKVTKSLFGRKKKKSKNHAQESDEVSGLVSGLMRTSV
GHQDARPEAPAASGPAPVSLVETAPEDSSPRGTLASSGDGFEEGLTVATVVESALCALR
NCVAFMPPAEQNPAPLAQPEPLVWVSKWVDYGGDLPSVEEVEVPAPPLLLQWVKTDQAL
LMLFSDGTVQVNFYGDHTKLILSGWEPLLVTFVARNRSACTYLASHLRQLGCSPDLRQRLRYALRLLRDRSPA
and our top hit is
GENE ID: 1263 PLK3 | polo-like kinase 3 (Drosophila) [Homo sapiens]
(Over 10 PubMed links)
Score = 663 bits (1710), Expect = 0.0, Method: Compositional matrix adjust.
Identities = 328/362 (90%), Positives = 335/362 (92%), Gaps = 14/362 (3%)
That resolves all these questions: it's a straightforward ortholog of PLK3 (which explains the Polo boxes), not some noteworthy hybrid and the sequence identity is 90+% -- and that score is dropped a lot by some iffy regions like this
Query 301 MPPAEQNPAPLAQPEPLVWVSKWVDYGGDLPSVEEVEVPAPPLLLQWVKTDQALLMLFSD 360
MPPAEQNPAPLAQPEPLVWVSKWVDY + + + + +LF+D
Sbjct 445 MPPAEQNPAPLAQPEPLVWVSKWVDYSNKFG-------------FGYQLSSRRVAVLFND 491
Query 361 GT 362
GT
Sbjct 492 GT 493
Score = 176 bits (446), Expect = 1e-43, Method: Compositional matrix adjust.
Identities = 83/91 (91%), Positives = 87/91 (95%), Gaps = 0/91 (0%)
Query 319 WVSKWVDYGGDLPSVEEVEVPAPPLLLQWVKTDQALLMLFSDGTVQVNFYGDHTKLILSG 378
++ + + GGDLPSVEEVEVPAPPLLLQWVKTDQALLMLFSDGTVQVNFYGDHTKLILSG
Sbjct 538 YMEQHLMKGGDLPSVEEVEVPAPPLLLQWVKTDQALLMLFSDGTVQVNFYGDHTKLILSG 597
Query 379 WEPLLVTFVARNRSACTYLASHLRQLGCSPD 409
WEPLLVTFVARNRSACTYLASHLRQLGCSPD
Sbjct 598 WEPLLVTFVARNRSACTYLASHLRQLGCSPD 628
What's going on there? Well, most likely this is underlining the draft nature of the chimpanzee genome. I checked with TBLASTN, and there aren't ESTs around this region -- the chimp PLK3 is pretty much a pure gene prediction model -- a tough problem that has been tackled well but never perfectly. Plus, the underlying genomic data is, well, draft quality. Another TBLASTN search revealed that although this Ensembl prediction is from the middle of a large contig, the N-terminus of human PLK3 has a great match on another contig -- but from the same chromosome.
Okay, maybe that's a fluke. So here's another chimp kinase highlighted in the text
A protein (ENSPTRP00000000076), classified under PKC subfamily, is composed of a PB1 domain followed by the protein kinase domain which is followed by a protein kinase C terminal domain (Figure 3a1). The PB1 domain is present in many eukaryotic cytoplasmic signalling proteins and is responsible, although not systematically, in the formation of PB1 dimers [25]. It thus serves as a molecular recognition module. This architecture is known so far only in an atypical PKC of Phallusia mammilata, a sea squirt. Our analysis identified two chimpanzee PKCs and a human PKC with a similar architecture, in which a phorbol esters/diacylglycerol binding domain is inserted between the PB1 and the protein kinase domain. The presence of the phorbol esters/diacylglycerol binding domain in combination with the protein kinase and a PKC terminal domain indicates that it is probably responsible for the recruitment of diacylglycerol, which in turns might be involved in activation of the kinase. The deletion of this domain in chimpanzee PKC (ENSPTRP00000000076) implies that the recruitment of diacylglycerol might be achieved by an external interacting module.
Again, the first thing to do is to search the ORF
against human RefSeq to get our bearings.
>1|Chimp|ENSPTRP00000000076
MPSRTGPKMEGSGGRVRLKAHYGGDIFITSVDAATTFEELCEEVRDMCRLHQQHPL
TLKWVDSEGDPCTVSSQMELEEAFRLARQCRDEGLIIHVFPSTPEQPGLPCPGEDK
SIYRRGARRWRKLYCANGHLFQAKRFNRDSVMPSQEPPVDDKNEDADLPSEETDGI
AYISSSRKHDSIKDDSEDLKPVIDGMDGIKISQGLGLQDFDLIRVIGRGSYAKVLL
VRLKKNDQIYAMKVVKKELVHDDETTSRLFLVIEYVNGGDLMFHMQRQRKLPEEHA
RFYAAEICIALNFLHERGIIYRDLKLDNVLLDADGHIKLTDYGMCKEGLGPGDTTS
TFCGTPNYIAPEILRGEEYGFSVDWWALGVLMFEMMAGRSPFDIITDNPDMNTEDY
LFQVILEKPIRIPRFLSVKASHVLKGFLNKDPKERLGCRPQTGFSDIKSHAFFRSI
DWDLLEKKQALPPFQPQITDDYGLDNFDTQFTSEPVQLTPDDEDAIKRIDQSEFEG
FEYINPLLLSTEESV
GENE ID: 5590 PRKCZ | protein kinase C, zeta [Homo sapiens]
(Over 100 PubMed links)
Score = 1031 bits (2665), Expect = 0.0, Method: Compositional matrix adjust.
Identities = 518/592 (87%), Positives = 518/592 (87%), Gaps = 73/592 (12%)
Query 1 MPSRTGPKMEGSGGRVRLKAHYGGDIFITSVDAATTFEELCEEVRDMCRLHQQHPLTLKW 60
MPSRTGPKMEGSGGRVRLKAHYGGDIFITSVDAATTFEELCEEVRDMCRLHQQHPLTLKW
Sbjct 1 MPSRTGPKMEGSGGRVRLKAHYGGDIFITSVDAATTFEELCEEVRDMCRLHQQHPLTLKW 60
Query 61 VDSEGDPCTVSSQMELEEAFRLARQCRDEGLIIHVFPSTPEQPGLPCPGEDKSIYRRGAR 120
VDSEGDPCTVSSQMELEEAFRLARQCRDEGLIIHVFPSTPEQPGLPCPGEDKSIYRRGAR
Sbjct 61 VDSEGDPCTVSSQMELEEAFRLARQCRDEGLIIHVFPSTPEQPGLPCPGEDKSIYRRGAR 120
Query 121 RWRKLYCANGHLFQAKRFNR---------------------------------------- 140
RWRKLY ANGHLFQAKRFNR
Sbjct 121 RWRKLYRANGHLFQAKRFNRRAYCGQCSERIWGLARQGYRCINCKLLVHKRCHGLVPLTC 180
Query 141 ----DSVMPSQEPPVDDKNEDADLPSEETDGIAYISSSRKHDSIKDDSEDLKPVIDGMDG 196
DSVMPSQEPPVDDKNEDADLPSEETDGIAYISSSRKHDSIKDDSEDLKPVIDGMDG
Sbjct 181 RKHMDSVMPSQEPPVDDKNEDADLPSEETDGIAYISSSRKHDSIKDDSEDLKPVIDGMDG 240
Query 197 IKISQGLGLQDFDLIRVIGRGSYAKVLLVRLKKNDQIYAMKVVKKELVHDDE-------- 248
IKISQGLGLQDFDLIRVIGRGSYAKVLLVRLKKNDQIYAMKVVKKELVHDDE
Sbjct 241 IKISQGLGLQDFDLIRVIGRGSYAKVLLVRLKKNDQIYAMKVVKKELVHDDEDIDWVQTE 300
Query 249 ---------------------TTSRLFLVIEYVNGGDLMFHMQRQRKLPEEHARFYAAEI 287
TTSRLFLVIEYVNGGDLMFHMQRQRKLPEEHARFYAAEI
Sbjct 301 KHVFEQASSNPFLVGLHSCFQTTSRLFLVIEYVNGGDLMFHMQRQRKLPEEHARFYAAEI 360
Query 288 CIALNFLHERGIIYRDLKLDNVLLDADGHIKLTDYGMCKEGLGPGDTTSTFCGTPNYIAP 347
CIALNFLHERGIIYRDLKLDNVLLDADGHIKLTDYGMCKEGLGPGDTTSTFCGTPNYIAP
Sbjct 361 CIALNFLHERGIIYRDLKLDNVLLDADGHIKLTDYGMCKEGLGPGDTTSTFCGTPNYIAP 420
Query 348 EILRGEEYGFSVDWWALGVLMFEMMAGRSPFDIITDNPDMNTEDYLFQVILEKPIRIPRF 407
EILRGEEYGFSVDWWALGVLMFEMMAGRSPFDIITDNPDMNTEDYLFQVILEKPIRIPRF
Sbjct 421 EILRGEEYGFSVDWWALGVLMFEMMAGRSPFDIITDNPDMNTEDYLFQVILEKPIRIPRF 480
Query 408 LSVKASHVLKGFLNKDPKERLGCRPQTGFSDIKSHAFFRSIDWDLLEKKQALPPFQPQIT 467
LSVKASHVLKGFLNKDPKERLGCRPQTGFSDIKSHAFFRSIDWDLLEKKQALPPFQPQIT
Sbjct 481 LSVKASHVLKGFLNKDPKERLGCRPQTGFSDIKSHAFFRSIDWDLLEKKQALPPFQPQIT 540
Query 468 DDYGLDNFDTQFTSEPVQLTPDDEDAIKRIDQSEFEGFEYINPLLLSTEESV 519
DDYGLDNFDTQFTSEPVQLTPDDEDAIKRIDQSEFEGFEYINPLLLSTEESV
Sbjct 541 DDYGLDNFDTQFTSEPVQLTPDDEDAIKRIDQSEFEGFEYINPLLLSTEESV 592
Okay, so the human protein has been described previously: it is human protein kinase C zeta. Has the PB1 domain in PKCzeta and its implications been previously discussed? A quick PubMed search turned up two papers from earlier this decade (in Molecular Cell & JBC) which actually demonstrated the dimerization potential of the PKCzeta PB1 domain. So the PKC domain with a PB1 domain is not novel & noteworthy. What about the missing diacylglycerol-binding domain (that first big gap) in the chimp kinase? That could be interesting, so let's see what whether we can find any EST evidence to support it. Alas, the only EST evidence refutes it and identifies the gap as spurious(and both of these ESTs were deposited in October 2007 and the paper submitted in March 2008, so they are not an unfair criticism)
>dbj|DC524857.1| DC524857 chimpanzee brain cDNA library PflB Pan troglodytes verus
cDNA clone PflB8010 5', mRNA sequence.
Length=404
Score = 108 bits (270), Expect(2) = 3e-31, Method: Composition-based stats.
Identities = 57/102 (55%), Positives = 58/102 (56%), Gaps = 44/102 (43%)
Frame = +2
Query 112 KSIYRRGARRWRKLYCANGHLFQAKRFNR------------------------------- 140
+SIYRRGARRWRKLYCANGHLFQAKRFNR
Sbjct 38 ESIYRRGARRWRKLYCANGHLFQAKRFNRRAYCGQCSERIWGLARQGYRCINCKLLVHKR 217
Query 141 -------------DSVMPSQEPPVDDKNEDADLPSEETDGIA 169
DSVMPSQEPPVDDKNEDADLPSEETDGIA
Sbjct 218 CHGLVPLTCRKHMDSVMPSQEPPVDDKNEDADLPSEETDGIA 343
Score = 42.7 bits (99), Expect(2) = 3e-31, Method: Compositional matrix adjust.
Identities = 20/22 (90%), Positives = 21/22 (95%), Gaps = 0/22 (0%)
Frame = +3
Query 168 IAYISSSRKHDSIKDDSEDLKP 189
+ YISSSRKHDSIKDDSEDLKP
Sbjct 339 LLYISSSRKHDSIKDDSEDLKP 404
>dbj|DC519886.1| DC519886 chimpanzee brain cDNA library PccB Pan troglodytes verus
cDNA clone PccB0482 5', mRNA sequence.
Length=612
Score = 114 bits (284), Expect = 3e-26, Method: Compositional matrix adjust.
Identities = 63/107 (58%), Positives = 63/107 (58%), Gaps = 44/107 (41%)
Frame = +2
Query 113 SIYRRGARRWRKLYCANGHLFQAKRFNR-------------------------------- 140
SIYRRGARRWRKLYCANGHLFQAKRFNR
Sbjct 290 SIYRRGARRWRKLYCANGHLFQAKRFNRRAYCGQCSERIWGLARQGYRCINCKLLVHKRC 469
Query 141 ------------DSVMPSQEPPVDDKNEDADLPSEETDGIAYISSSR 175
DSVMPSQEPPVDDKNEDADLPSEETDGIAYISSSR
Sbjct 470 HGLVPLTCRKHMDSVMPSQEPPVDDKNEDADLPSEETDGIAYISSSR 610
I checked in detail one more note (about the chimp protein ENSPTRP00000001185 and its human ortholog) about a domain architecture claimed to be unique to human & chimp due to a missing domain. Again, the RefSeq protein search revealed that the chimp protein is nearly identical to a known human kinase (MARK2) albeit greatly truncated -- and the missing domain is beyond the truncation point.
I haven't checked every kinase in the paper, but seeing the same classes of mistakes repeatedly doesn't give much hope. Comparing the human & chimp kinomes (or any other well-defined subset of genes) is a worthwhile enterprise -- so long as it is kept in mind that the chimp genome is a very rough draft and all appropriate computational controls are used. This paper, unfortunately, shows no awareness of either of these principles.
What irks me most about this sort of paper is that it gives all of us a bit of a black eye. Someone who saw the abstract & got excited would be in for a big letdown. It's hard enough to earn the respect of bench biologists without it being tossed away with poorly done analyses.
So what are the positive lessons to be learned? Here are a few tips
- Always try to find meaningful biological names for your sequences. Use them in your figures & search them in the literature like a bloodhound.
- Always check genomic predictions against EST & cDNA databases.
- Always try to root your phylogenetic trees, unless you have a really good reason not to do so. And, if your tree is rooted, you must explain how you rooted it
- If your results sound amazing, take a deep breath & think of several tests that could debunk them. Then do those ten tests. If they survive, go to bed & think of another batch of tests.
P.S. One way to put reviewers in a bad mood is to not supply your sequences. The supplementary materials for this paper do not have all of the ORFs; I pulled some out from their alignments with a custom script. Elsewhere via Google I found a collection linked to the work -- but with the whole predicted chimp proteome in it! Very unwieldy & slow to download!
P.P.S. For anyone interested in exploring further, here are the other sequences from the alignment in additional file 3. The number in the header was added by my script to indicate which alignment within that file the sequence was taken from.
>2|Chimp|ENSPTRP00000019171
MSAEVRLRRLQQLVLDPGFLGLEPLLDLLLGVHQELGASELAQDKYVADFLQWAEPIVVRL
KEVRLQRDDFEILKVIGRGAFSEVAVVKMKQTGQVYAMKIMNKWDMLKRGEVSCFREERDV
LVNGDRRWITQLHFAFQDENYLYLVMEYYVGGDLLTLLSKFGERIPAEMARFYLAEIVMAI
DSVHRLGYVHRDIKPDNILLDRCGHIRLADFGSCLKLRADGTVRSLVAVGTPDYLSPEILQ
AVGGGPGTGSYGPECDWWALGVFAYEMFYGQTPFYADSTAETYGKIVHYKEHLSLPLVDEG
VPEEARDFIQRLLCPPETRLGRGGAGDFRTHPFFFGLDWDGLRDSVPPFTPDFEGATDTCN
FDLVEDGLTAMVSGGGETLSDIREGAPLGVHLPFVGYSYSCMALRDSEVPGPTPMELEAEQ
LLEPHVQAPSLEPSVSPQDETAEVAVPAAVPAAEAEAEVTLRELQEALEEEVLTRQSLSRE
MEAIRTDNQNFASQLREAEARNRDLEAHVRQLQERMELLQAEGATAVTGVPSPRATDPPSH
VPWPGLSXALSLLLFAVVLSRAAALGCLGLVAPAGXLXAVWRRPGAARAPX
>4|Chimp|ENSPTRP00000011569
MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQ
LMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRS
GSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKK
EVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERV
FSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGAT
MKTFCGTSEYLAPRLSPPFKPQVTSETDTRYFDEEFTAQMITITPP
DQDDSMECVDSERRPHFPQFSYSASGTA
Wednesday, January 28, 2009
Remembering the 27th, 28th & 1st
When I was a junior in high school, on a day much like today, I wanted to stay home and watch TV a bit, so I was hoping the wintry weather would generate a snow day. I didn't often wish for this, as my childhood love of snow had subsided substantially (though I would sometimes ski through my yard), but on this day I wanted to be home. Winter and the superintendent, however, did not cooperate and we had only a delayed opening, and hooky was out of the question in my family so off I went.
And so I was sitting in Mr. Schmidt's chemistry class that morning. He was a nice man, but that class did very little to prepare me for a life on the periphery of chemistry, except that he did an excellent job of outlining the early 20th century revolution in chemistry & physics. I do not remember what he was talking about that morning when Mrs. Kurtz, the Biology II teacher, came in and commented on a news event. We all nodded, given we expected the news -- but then she restated herself as we had not heard her, and Mr. Schmidt got out the TV in his closet and I found myself watching TV that morning -- exactly what I had hoped to watch on a snow day but also nothing I had ever imagined or could have remotely hoped to watch. For that restatement was: "No, the space shuttle blew up!".
When my boy was three we were going one weekend to take him to the Boston Children's Museum, a wonderful place for a child of that age to explore and run around and have fun. As a bonus, we would ride the subway there and oh how he loves to ride trains. It was again a winter day and I drove the usual route to Boston & there is a spot on I-93 where you come out of the relatively untouched beauty of the Middlesex Fells and the skyline of Boston suddenly appears. It was in that spot that I heard the report on radio whose meaning became instantly clear, and I semi-silently cried "No!" -- an extended loss of radio contact with a space shuttle could not ever end happily.
We are in the midst of that grim week of anniversaries for NASA; yesterday marked the 42nd anniversary of Apollo 1, today the 23rd anniversary of the loss of Challenger and Sunday is 6th anniversary of the loss of Columbia. Only one of those events has any obvious connection to this time of year.
For as long as I can remember the space program has had an outsized influence on my imagination. My career path did not take me in a good direction to go to space, but I still think about it almost daily. In some ways these three disasters are completely removed from what I do, but in other ways they are not. I do subscribe to Edward Tufte's argument that poor data visualization helped enable the Challenger disaster, and while my plots do not carry such weighty implications I still must be ready in case they ever do. All three of these were hardware failures, and I do software, but software failures have caused unmanned probes to be lost and manned missions to go awry.
But of all else, it is important to remember those who pushed the limits and did not return. We must remember who they were and why they died, as they died doing important things and they died because humans make mistakes. Grissom, White & Chaffee were doomed by a design from which escape was impossible and fire likely. Smith, Scobee, McNair, Onizuka, McAuliffe, Jarvis & Resnik died when a machine was run far outside its normal operating regime. Brown, Husband, Clark, Chawla, Anderson, McCool and Ramon died from a design which was not well matched to the materials used to construct it.
We recently learned some more details of the Columbia accident: how the astronauts never realized the disaster approaching them, but how pilot McCool worked calmly to deal with systematic failure just before it killed him. I wish I could have such coolness under stress.
And so I was sitting in Mr. Schmidt's chemistry class that morning. He was a nice man, but that class did very little to prepare me for a life on the periphery of chemistry, except that he did an excellent job of outlining the early 20th century revolution in chemistry & physics. I do not remember what he was talking about that morning when Mrs. Kurtz, the Biology II teacher, came in and commented on a news event. We all nodded, given we expected the news -- but then she restated herself as we had not heard her, and Mr. Schmidt got out the TV in his closet and I found myself watching TV that morning -- exactly what I had hoped to watch on a snow day but also nothing I had ever imagined or could have remotely hoped to watch. For that restatement was: "No, the space shuttle blew up!".
When my boy was three we were going one weekend to take him to the Boston Children's Museum, a wonderful place for a child of that age to explore and run around and have fun. As a bonus, we would ride the subway there and oh how he loves to ride trains. It was again a winter day and I drove the usual route to Boston & there is a spot on I-93 where you come out of the relatively untouched beauty of the Middlesex Fells and the skyline of Boston suddenly appears. It was in that spot that I heard the report on radio whose meaning became instantly clear, and I semi-silently cried "No!" -- an extended loss of radio contact with a space shuttle could not ever end happily.
We are in the midst of that grim week of anniversaries for NASA; yesterday marked the 42nd anniversary of Apollo 1, today the 23rd anniversary of the loss of Challenger and Sunday is 6th anniversary of the loss of Columbia. Only one of those events has any obvious connection to this time of year.
For as long as I can remember the space program has had an outsized influence on my imagination. My career path did not take me in a good direction to go to space, but I still think about it almost daily. In some ways these three disasters are completely removed from what I do, but in other ways they are not. I do subscribe to Edward Tufte's argument that poor data visualization helped enable the Challenger disaster, and while my plots do not carry such weighty implications I still must be ready in case they ever do. All three of these were hardware failures, and I do software, but software failures have caused unmanned probes to be lost and manned missions to go awry.
But of all else, it is important to remember those who pushed the limits and did not return. We must remember who they were and why they died, as they died doing important things and they died because humans make mistakes. Grissom, White & Chaffee were doomed by a design from which escape was impossible and fire likely. Smith, Scobee, McNair, Onizuka, McAuliffe, Jarvis & Resnik died when a machine was run far outside its normal operating regime. Brown, Husband, Clark, Chawla, Anderson, McCool and Ramon died from a design which was not well matched to the materials used to construct it.
We recently learned some more details of the Columbia accident: how the astronauts never realized the disaster approaching them, but how pilot McCool worked calmly to deal with systematic failure just before it killed him. I wish I could have such coolness under stress.
Monday, January 26, 2009
Next, exploding DNA packs at the banks
I use gmail for my personal mail & actually tend to enjoy the sidebar ads. Yes, most are silly or uninteresting, but once in a while there are some odd or amusing ones. There are also some patterns -- email from my one brother often brings up inane creationist sites (which I click through to -- I figure I'd rather Google have their money than them), as we are often talking about chimps -- and that is clearly one of their buzzwords.
So here's a use for DNA that would have never occurred to me: tagging burglars with it. Or more importantly, threatening to tag them with it. All sorts of claims are made that the appearance of surveillance is nearly as useful as actual surveillance for deterring property crime, so I guess this is in that bucket.
Will it work? Will some enterprising criminal start marketing DNase spray? When will it show up on CSI?
So here's a use for DNA that would have never occurred to me: tagging burglars with it. Or more importantly, threatening to tag them with it. All sorts of claims are made that the appearance of surveillance is nearly as useful as actual surveillance for deterring property crime, so I guess this is in that bucket.
Multiple SelectaDNA Spray heads can be fitted at the entry points of premises and on activation emit a burst of SelectaDNA solution onto the offenders. The solution contains a UV tracer and a unique DNA code, linking them irrefutably to the crime scene. The DNA Spray can be armed by a panic button and/or linked to an existing intruder alarm system. As the DNA fear-factor amongst criminals is high, it is likely that sprayed intruders will flee the crime scene before stealing any goods.
Will it work? Will some enterprising criminal start marketing DNase spray? When will it show up on CSI?
Sunday, January 25, 2009
Are the old lessons being forgotten?
Okay, first I feel like I have to have a bit of preamble. This, and another post I'm doing the homework on, are pretty critical. Downright negative. I'm not turning into a curmudgeon or planning to turn this space into a rant-a-thon. It's just that both are topics I think are important & have pushed the right buttons.
Also, this isn't meant to be high-and-mighty-and-spotless-expert calling calumny on the great unwashed masses. If I look down at my metaphorical foot I find many tightly spaced patterns of scars, sometimes nearly concentric. We all make mistakes, and often we repeat those of the past. We think we've covered bases that have always been covered or deceive ourselves that safety mechanisms which were needed in the past are no longer necessary.
A bit ago at work I was doing some exploring of a standard a backbone and became curious just how taxonomically widespread pieces of the backbone might be found naturally. So naturally, I pumped the sequence into the NCBI BLASTN server & pointed it at the RefSeq genomes. As expected, a bunch of bacterial plasmids popped up. What was unsettling, though, was a bunch of provisional genomic RefSeqs for eukaryotic chromosomes. Indeed, one project had apparently deposited every chromosome with a pUC-type vector sequence at one end. YIKES!
The other day I got curious again & tried searching the non-redundant DNA and protein databases but with the species filter set to eukaryote. Again, a bunch of hits -- and the shocking part was many were very recently deposited sequences -- even human ones. In some cases, the entire deposited sequence was vector-derived (e.g. the non-human "putative reverse transcriptases" ABK60177.1, CAD59768.1, CAD59767.1 & CAL37000.1).
For example, AK302803.1 is a 1352 nucleotide sequence deposited in 2008; from 888 on is clearly vector -- and the coding region is annotated as 1 to 1275! CAH85743 is a "Plasmodium" protein which is entirely vector derived; again deposited in 2008. PIR (is anybody still curating this?) has a number of vector-derived proteins (e.g. the 231 amino acid "NZ-3 antigen" JC7702; S.pombe beta-lactamase (!) T51301); I was surprised to even find a SwissProt entry that looks like it has pUC-derived sequence
Even the RefSeq mRNA section has some very provisional mammalian predicted cDNAs (from chimp) which appear to be polylinker-type sequences from vector (selected restriction sites are marked)
Contamination of various sorts has plagued genome projects from the get-go. Perhaps the most notorious was a large deposition of human ESTs which were donated to the public with great fanfare (as a counterpoint to private EST efforts), only to be found later to be rich in yeast sequences. The solution is to run filters -- search everything you do against vectors, E.coli and other common contaminants. In addition, especially in this day-and-age, if your "human" mRNA sequence doesn't match the genome, you've got some 'splaining to do.
What's the harm? Well, when it comes to databases I don't like mess. You always need to check your data, but it's always a nuisance when you actually have to clean it a bunch. Miss something, and some experiment is dirty or worse ruined. Plus, and this is a bit of the theme to my proto-post, some folks haven't yet figured this out & the results are truly ugly. Even worse, these are the obvious problems since bacterial vectors in a eukaryotic sequence truly stick out. Now I'm wondering about all the pUC-like sequences I found in bacterial sources -- can I trust them either?
So, let's all make a it's-still-a-pretty-new-year resolution to recheck our sequencing pipelines. Deliberately throw pUC19 and the E.coli genome through it & see what comes out.
Also, this isn't meant to be high-and-mighty-and-spotless-expert calling calumny on the great unwashed masses. If I look down at my metaphorical foot I find many tightly spaced patterns of scars, sometimes nearly concentric. We all make mistakes, and often we repeat those of the past. We think we've covered bases that have always been covered or deceive ourselves that safety mechanisms which were needed in the past are no longer necessary.
A bit ago at work I was doing some exploring of a standard a backbone and became curious just how taxonomically widespread pieces of the backbone might be found naturally. So naturally, I pumped the sequence into the NCBI BLASTN server & pointed it at the RefSeq genomes. As expected, a bunch of bacterial plasmids popped up. What was unsettling, though, was a bunch of provisional genomic RefSeqs for eukaryotic chromosomes. Indeed, one project had apparently deposited every chromosome with a pUC-type vector sequence at one end. YIKES!
The other day I got curious again & tried searching the non-redundant DNA and protein databases but with the species filter set to eukaryote. Again, a bunch of hits -- and the shocking part was many were very recently deposited sequences -- even human ones. In some cases, the entire deposited sequence was vector-derived (e.g. the non-human "putative reverse transcriptases" ABK60177.1, CAD59768.1, CAD59767.1 & CAL37000.1).
For example, AK302803.1 is a 1352 nucleotide sequence deposited in 2008; from 888 on is clearly vector -- and the coding region is annotated as 1 to 1275! CAH85743 is a "Plasmodium" protein which is entirely vector derived; again deposited in 2008. PIR (is anybody still curating this?) has a number of vector-derived proteins (e.g. the 231 amino acid "NZ-3 antigen" JC7702; S.pombe beta-lactamase (!) T51301); I was surprised to even find a SwissProt entry that looks like it has pUC-derived sequence
>sp|Q63661.2|MUC4_RAT RecName: Full=Mucin-4; Short=MUC-4; AltName: Full=Pancreatic
adenocarcinoma mucin; AltName: Full=Testis mucin; AltName: Full=Ascites
sialoglycoprotein; Short=ASGP; AltName: Full=Sialomucin
complex; AltName: Full=Pre-sialomucin complex; Short=pSMC;
Contains: RecName: Full=Mucin-4 alpha chain; AltName:
Full=Ascites sialoglycoprotein 1; Short=ASGP-1; Contains: RecName:
Full=Mucin-4 beta chain; AltName: Full=Ascites sialoglycoprotein
2; Short=ASGP-2; Flags: Precursor
Length=2344
GENE ID: 303887 Muc4 | mucin 4, cell surface associated [Rattus norvegicus]
(Over 10 PubMed links)
Score = 46.6 bits (109), Expect = 0.006
Identities = 22/35 (62%), Positives = 25/35 (71%), Gaps = 3/35 (8%)
Frame = -3
pUC19 1427 CCLQTKKPPLPAVVCLPDQELPTLFPKVTGFSRAQ 1323
CCLQTKKPPLPAVVCLPD P+ P + S+ Q
Sbjct 1051 CCLQTKKPPLPAVVCLPD---PSSVPSLMHSSKPQ 1082
Even the RefSeq mRNA section has some very provisional mammalian predicted cDNAs (from chimp) which appear to be polylinker-type sequences from vector (selected restriction sites are marked)
=XbaI= =PstI=
=BamHI =SalI= =PaeI
pUC19 415 GGGGATCCTCTAGAGTCGACCTGCAGGCATG 444
XM_001160101.1 56 GGGGATCCTCTAGAGTCGACCTGCAGGCAT 85
XM_001146903.1 439 GGATCCTCTAGAGTCGACCTGCAGGCATG 467
XM_001141474.1 1503 GGGATCCTCTAGAGTCGACCTGCAGGCA 1530
XM_001141395.1 922 GGGATCCTCTAGAGTCGACCTGCAGGCA 949
Contamination of various sorts has plagued genome projects from the get-go. Perhaps the most notorious was a large deposition of human ESTs which were donated to the public with great fanfare (as a counterpoint to private EST efforts), only to be found later to be rich in yeast sequences. The solution is to run filters -- search everything you do against vectors, E.coli and other common contaminants. In addition, especially in this day-and-age, if your "human" mRNA sequence doesn't match the genome, you've got some 'splaining to do.
What's the harm? Well, when it comes to databases I don't like mess. You always need to check your data, but it's always a nuisance when you actually have to clean it a bunch. Miss something, and some experiment is dirty or worse ruined. Plus, and this is a bit of the theme to my proto-post, some folks haven't yet figured this out & the results are truly ugly. Even worse, these are the obvious problems since bacterial vectors in a eukaryotic sequence truly stick out. Now I'm wondering about all the pUC-like sequences I found in bacterial sources -- can I trust them either?
So, let's all make a it's-still-a-pretty-new-year resolution to recheck our sequencing pipelines. Deliberately throw pUC19 and the E.coli genome through it & see what comes out.
Saturday, January 24, 2009
Earning the right ot put "DNA" in your address
GenomeWeb had an item about real estate developers putting "DNA" in their property names; I had spotted the DNA Lofts in Dorchester but hadn't gotten around to blogging about them (annoying to be scooped, but that's procrastination for you).
However, as far as I can tell the DNA Lofts are just a catchy name, with no actual tie-in. It would be a convenient Red Line ride from the nearby Savin Hill station to the biotech areas of Cambridge. Which is tres disappointing. Surely they could do better by picking something off this list to truly earn a DNA tie-in:
Of course, the best of all -- but quite ambitious -- would be to use a synthetic biology approach to construct the building!
However, as far as I can tell the DNA Lofts are just a catchy name, with no actual tie-in. It would be a convenient Red Line ride from the nearby Savin Hill station to the biotech areas of Cambridge. Which is tres disappointing. Surely they could do better by picking something off this list to truly earn a DNA tie-in:
- Rehabbing space relevant to the history of biotech ("These walls are still contaminated with phage from seminal experiments...")
- Subtle decorative motifs, such as floors tiled with the genetic code table
- Major architectural elements. Double-helical staircases are an obvious one, but how about pyrimidine & purine-shaped windows?
- Under-the counter thermocyclers in the kitchens (and -80 compartments in the freezers), washing machines built by Sorvall, etc.
- Themed common areas: The Topoisomerase Lounge (where you can unwind). The Proteasome recycling center.
Of course, the best of all -- but quite ambitious -- would be to use a synthetic biology approach to construct the building!
Friday, January 23, 2009
Forgetting Occam's Razor
As I've confessed before, one of my recreational vices is the TV show House. It's entertaining enough & Hugh Laurie is really good in the title role and it just relaxes me a bit. I always thought it was harmless, but now I'm wondering.
There is a saying in medicine which has become quite well known thanks to medical shows: If you hear hoof beats, think horses not zebras. In other words, consider the most common cause for a symptom before marching off to explore some rare disease which could cause it. The thing about House is that it doesn't just feature zebras, but giant carnivorous purple-and-orange Martian zebras. Plots either revolve around very unusual diseases or more commonly not so unusual diseases with totally bizarre presentation.
Some nasty GI bug, or perhaps a gang of them, latched onto me last week and while I was much better this week I couldn't quite seem to kick it. So I was off to my internist yesterday in hopes of getting an antibiotic scrip. TNG was along for the ride, also in the process of shaking off a bug. He at least brought some reading material (the apropos, in a macabre fashion, The Hostile Hospital), but I had not. So I was scanning through the waiting room magazines & lo and behold: a copy of New England Journal of Medicine (and recent too!).
I don't regularly read NEJM for the simple reason that most of the articles aren't really in my field: they rarely publish molecular medicine studies, though when they do show up they tend to be huge splashes. So I started skimming the ToC for something interesting & spotted an intriguing headline.
Then it hit me: only a House fan would have parsed that title that way. There was nothing that bizarre going on. One twin: healthy. The other twin: not-healthy. Duh!
There is a saying in medicine which has become quite well known thanks to medical shows: If you hear hoof beats, think horses not zebras. In other words, consider the most common cause for a symptom before marching off to explore some rare disease which could cause it. The thing about House is that it doesn't just feature zebras, but giant carnivorous purple-and-orange Martian zebras. Plots either revolve around very unusual diseases or more commonly not so unusual diseases with totally bizarre presentation.
Some nasty GI bug, or perhaps a gang of them, latched onto me last week and while I was much better this week I couldn't quite seem to kick it. So I was off to my internist yesterday in hopes of getting an antibiotic scrip. TNG was along for the ride, also in the process of shaking off a bug. He at least brought some reading material (the apropos, in a macabre fashion, The Hostile Hospital), but I had not. So I was scanning through the waiting room magazines & lo and behold: a copy of New England Journal of Medicine (and recent too!).
I don't regularly read NEJM for the simple reason that most of the articles aren't really in my field: they rarely publish molecular medicine studies, though when they do show up they tend to be huge splashes. So I started skimming the ToC for something interesting & spotted an intriguing headline.
Hypogonadism Due to Pituicytoma in an Identical TwinBut as I read the short article I became increasingly puzzled as I read it repeatedly: how exactly was the Pituicytoma in one twin causing the hypogonadism in the other twin?
Then it hit me: only a House fan would have parsed that title that way. There was nothing that bizarre going on. One twin: healthy. The other twin: not-healthy. Duh!
Wednesday, January 21, 2009
Where did those gene count estimates come from anyway?
When mentally reviewing what I wrote yesterday about the great human genome gold rush, I realized I hadn't really touched on one of the most curious bits of that. Indeed, it was GenomeWeb's Daily Scan headline on an entry summarizing mine & Derek Lowe's pieces that reminded me of it: All those varying estimates for human gene count.
When the human genome was only partially sequenced, one of my colleagues at Millennium tried to dig through the literature and figure out the best estimate for the number of human genes. Many textbooks & reviews seem to put the number in the 50,000-75,000 range -- my 2nd edition of Alberts et al, Molecular Biology of the Cell from junior year states
The other pre-sequencing methodology that was often cited was DNA reassociation kinetics, an experimental approach which can estimate the fraction of DNA in a genome which is unique and what fraction is repeated. If we assume that genes are only in the unique regions, then knowing the size of the genome and the unique fraction could estimate the amount of space left over for genes.
What my colleague was unable to find, strangely, was any paper which actually declared a gene count as an original result. As far as he could tell, the human genome estimate had popped into being like a quantum particle in a vacuum, and then was repeated. I think it would be a great challenge for someone (or a whole class!) at a university with a good (and still accessible!) collection of the older journals to try to find that first paper, if it does exist.
Now the whole reason for this is that it was useful to have a ballpark figure. For example, if we thought we could find 20K human genes and somebody had a database of 200K human genes, then maybe we were missing out on 75% of the valuable genes -- and should consider buying into a database. Or, if we thought we could find them on our own, it made a difference what we might try to negotiate. If we thought 1% of the genes would fall into classical drug target categories, a 4X difference in gene count could really alter how we would structure deals.
MLNM wasn't a great trafficker in human gene numbers, but many other companies were -- and generally seemed to one-up each other. If Incyte claimed their data showed 150K genes, then HGS might claim 175K and Hyseq 200K (I don't remember precisely who claimed which, though these three were big traffickers in numbers).
So my colleague tried a new approach, which I think was to say: we have a few percent of the human genome sequences (albeit mostly around genes of interest and not randomly sampled). How many genes have been found? And what would that extrapolate out to for the whole genome.
His conclusion was so shocking I admit I refused to believe it at first, and never quite bought into it. I think it was about 25-30K. How could the textbooks be off by 2X-3X? I could believe the other genomics companies might be optimistic in interpreting their data, but could they really be deluding themselves that much??
But, the logic was hard to assault. In order for his estimate to be low by a lot, you would have to posit that the genomic regions sequenced to date were unusually gene poor -- and that the rest of the genome was packed.
Lo and behold, when the genome came in his estimate was shown to be prescient. The textbook numbers were based on very crude techniques, and couldn't really be traced down to an original source to verify the methods or check the various inputs. But, what about all those other companies?
I've never heard any of the high estimaters explain themselves, other than the brief bit of "yeah, the genome's out but y'all missed a lot of stuff" which followed the genome announcements. I have some general guesses, however, based on what I saw in our own work. In general, though, it gets down to all the ways you can be fooled looking solely (or primarily) at EST data.
First, there is the contamination/mistracking problem: some of the DNA in your database isn't what it is supposed to be. The easiest is contamination: some bits of environmental stuff get into your sequencing libraries. The simplest is E.coli and early on there was a scandalous amount of yeast in some public EST libraries, but all sorts of other stuff will show up. One public library had traces of Lactobacillus in it -- which I joked was due to the technician eating yogurt with one hand while preparing the library with the other. I saw at least once a library contaminated with tobacco sequences. Now, many of these were probably mistracking of samples at a facility which processed many different sorts of DNA -- indeed, there was a strong correlation between the type of junk found in an EST library and which facility had made it -- and the junk usually corresponded to another project.
But even stranger laboratory-generated wierdness could result. We had one case at MLNM where nearly every gene in a whole library seemed to be fused to a particular human gene. The most likely explanation we came up with is that the common gene had been sequenced, as a short PCR product, and somehow samples had been mixed or contamination left behind in a well. The strong signal from the PCR product swamped out the EST traces -- until the end of the PCR product was reached & the other signal could now be seen.
Still other wierd artifacts were certainly created during the building of the library -- genomic contamination, ligation of bits of DNA to create chimaeras, etc.
Deeper still, bits of the genome sometimes get transcribed or the transcripts spliced in odd ways. We would find ESTs or EST read pairs (one read from each end of the molecule) which would suggest some strange transcript -- but never be able to detect the transcript by RT-PCR. Now, that doesn't prove it never exists, but it does leave open the possibility that the EST was a one-time wonder.
All of these are rare events, but look through enough data and you will see them. So, my best guess for those overestimates was that everything in these companies database's was fed into a clustering algorithm & every unique cluster was called a gene. Given the perceived value of claiming a bigger database, none of them pushed on their Informatics groups to get error bounds or provide a conservative estimate.
Of course, once the genome showed up the evidence was there to rule out a lot of stuff. Even when the genome was quite unfinished, one of my pet projects was to try to clean the junk out of our database. So, once we started trying to align all our human ESTs (which included public ESTs and Incyte's database) to the genome I started asking: what is the remaining stuff. Some could never be figured out, but more than a little mapped to some other genome -- mouse, rat, fly, worm, E.coli, etc. Some stuff mapped to the human genome -- but onto two different chromosomes or too far apart to make sense. Yes, there could be some interesting stuff there (indeed, someone else did realize this was a way to find interesting stuff), but for our immediate needs we just wanted to toss.
If anyone from one of the other genomics companies would like to dispute what I've written here, I invite them to do so -- I think it is a fascinating part of history which should be captured before it is all forgotten.
When the human genome was only partially sequenced, one of my colleagues at Millennium tried to dig through the literature and figure out the best estimate for the number of human genes. Many textbooks & reviews seem to put the number in the 50,000-75,000 range -- my 2nd edition of Alberts et al, Molecular Biology of the Cell from junior year states
no mammal (or any other organism) is likely to be constructed from more than perhaps 60,000 essential proteins (ignoring for the moment the important consequences of alterative RNA splicing) Thus, from a genetic point of view, humans are unlikely to be more than about 10 times more complex than the fruit fly Drosophila, which is estimate to have about 5000 essential genes.. The argument laid out in this textbook is one based on population genetics & mutation rates, and is basically an upper bound given observed DNA mutation rates and the size of the genome.
The other pre-sequencing methodology that was often cited was DNA reassociation kinetics, an experimental approach which can estimate the fraction of DNA in a genome which is unique and what fraction is repeated. If we assume that genes are only in the unique regions, then knowing the size of the genome and the unique fraction could estimate the amount of space left over for genes.
What my colleague was unable to find, strangely, was any paper which actually declared a gene count as an original result. As far as he could tell, the human genome estimate had popped into being like a quantum particle in a vacuum, and then was repeated. I think it would be a great challenge for someone (or a whole class!) at a university with a good (and still accessible!) collection of the older journals to try to find that first paper, if it does exist.
Now the whole reason for this is that it was useful to have a ballpark figure. For example, if we thought we could find 20K human genes and somebody had a database of 200K human genes, then maybe we were missing out on 75% of the valuable genes -- and should consider buying into a database. Or, if we thought we could find them on our own, it made a difference what we might try to negotiate. If we thought 1% of the genes would fall into classical drug target categories, a 4X difference in gene count could really alter how we would structure deals.
MLNM wasn't a great trafficker in human gene numbers, but many other companies were -- and generally seemed to one-up each other. If Incyte claimed their data showed 150K genes, then HGS might claim 175K and Hyseq 200K (I don't remember precisely who claimed which, though these three were big traffickers in numbers).
So my colleague tried a new approach, which I think was to say: we have a few percent of the human genome sequences (albeit mostly around genes of interest and not randomly sampled). How many genes have been found? And what would that extrapolate out to for the whole genome.
His conclusion was so shocking I admit I refused to believe it at first, and never quite bought into it. I think it was about 25-30K. How could the textbooks be off by 2X-3X? I could believe the other genomics companies might be optimistic in interpreting their data, but could they really be deluding themselves that much??
But, the logic was hard to assault. In order for his estimate to be low by a lot, you would have to posit that the genomic regions sequenced to date were unusually gene poor -- and that the rest of the genome was packed.
Lo and behold, when the genome came in his estimate was shown to be prescient. The textbook numbers were based on very crude techniques, and couldn't really be traced down to an original source to verify the methods or check the various inputs. But, what about all those other companies?
I've never heard any of the high estimaters explain themselves, other than the brief bit of "yeah, the genome's out but y'all missed a lot of stuff" which followed the genome announcements. I have some general guesses, however, based on what I saw in our own work. In general, though, it gets down to all the ways you can be fooled looking solely (or primarily) at EST data.
First, there is the contamination/mistracking problem: some of the DNA in your database isn't what it is supposed to be. The easiest is contamination: some bits of environmental stuff get into your sequencing libraries. The simplest is E.coli and early on there was a scandalous amount of yeast in some public EST libraries, but all sorts of other stuff will show up. One public library had traces of Lactobacillus in it -- which I joked was due to the technician eating yogurt with one hand while preparing the library with the other. I saw at least once a library contaminated with tobacco sequences. Now, many of these were probably mistracking of samples at a facility which processed many different sorts of DNA -- indeed, there was a strong correlation between the type of junk found in an EST library and which facility had made it -- and the junk usually corresponded to another project.
But even stranger laboratory-generated wierdness could result. We had one case at MLNM where nearly every gene in a whole library seemed to be fused to a particular human gene. The most likely explanation we came up with is that the common gene had been sequenced, as a short PCR product, and somehow samples had been mixed or contamination left behind in a well. The strong signal from the PCR product swamped out the EST traces -- until the end of the PCR product was reached & the other signal could now be seen.
Still other wierd artifacts were certainly created during the building of the library -- genomic contamination, ligation of bits of DNA to create chimaeras, etc.
Deeper still, bits of the genome sometimes get transcribed or the transcripts spliced in odd ways. We would find ESTs or EST read pairs (one read from each end of the molecule) which would suggest some strange transcript -- but never be able to detect the transcript by RT-PCR. Now, that doesn't prove it never exists, but it does leave open the possibility that the EST was a one-time wonder.
All of these are rare events, but look through enough data and you will see them. So, my best guess for those overestimates was that everything in these companies database's was fed into a clustering algorithm & every unique cluster was called a gene. Given the perceived value of claiming a bigger database, none of them pushed on their Informatics groups to get error bounds or provide a conservative estimate.
Of course, once the genome showed up the evidence was there to rule out a lot of stuff. Even when the genome was quite unfinished, one of my pet projects was to try to clean the junk out of our database. So, once we started trying to align all our human ESTs (which included public ESTs and Incyte's database) to the genome I started asking: what is the remaining stuff. Some could never be figured out, but more than a little mapped to some other genome -- mouse, rat, fly, worm, E.coli, etc. Some stuff mapped to the human genome -- but onto two different chromosomes or too far apart to make sense. Yes, there could be some interesting stuff there (indeed, someone else did realize this was a way to find interesting stuff), but for our immediate needs we just wanted to toss.
If anyone from one of the other genomics companies would like to dispute what I've written here, I invite them to do so -- I think it is a fascinating part of history which should be captured before it is all forgotten.
Tuesday, January 20, 2009
Ah, them gold rush days!
Derek Lowe had a nice piece yesterday looking back on the genomics bubble. I might quibble with his benchmarking of the end of the insanity -- the stock market bubble would not peak until just before the 2000 elections, but it's a fine piece & pretty accurate.
I should know -- I was there. I was more than just there, I was a significant part of it. No, I didn't think it up & I won't try to exaggerate my importance, but for what is perhaps the poster child of genomics excess (and if not that, certainly in the Pantheon of genomanic deities).
When I got to Millennium they were still largely focused on the positional cloning of disease genes. But, they had started throwing sequencing capacity at ESTs, small bits of genetic message which serve as toeholds to larger ones. The catch was that the sequencing analysis software had been designed for positional cloning work & not ESTs, and it's a very different ballgame. When sequencing genomic DNA seeing anything which looked like a gene was interesting. But when sequencing stuff that is almost nothing but genes, the challenge was to sort the wheat from the chaff. Lots of scientists spent mind-numbing hours scanning BLAST reports for things of interest, and often found things. But this is a lousy technique -- not only might eyes glaze over (or neurons croak) from monotony, but a really interesting match might not be obvious -- what if the top hit was "Uncharacterized protein X" but the 3rd match down was "TotalPharmaceuticalGold"? Or worse, that BLAST couldn't even find a useable match? Plus, was that a match or an identity -- did you find something new or just rediscover a lousy fragment of the old? More mind numbing staring.
Enter a cocky recent Ph.D. After building up some expertise and some more refined tools (which in their embryonic form nailed me the one gene patent of mine perhaps worth something), I had built a system which churned through all the ESTs and crudely organized them by what made things interesting (and tried to ignore all the boring stuff). Ion channels -- look on this web page. GPCRs -- that's over here. Possible secreted proteins, look at this analysis. Furthermore, it also attempted to amalgamate all the different ESTs into a view which was higher quality, longer and more compact -- and tell you which things were already described as proteins and which might be novel. Plus, more sensitive algorithms than BLAST were used to pull things into families.
Now in all honesty, it wasn't nearly perfect. Some of the mind-numbing review had shifted to me -- the early versions in particular had every homology approved (and named!) by me. The semi-automatically generated names were ugly. Various EST artifacts could join webs of unrelated genes into a horrible tangle. But, now there could be reviews of consolidated, pre-analyzed data (though also in fairness nobody ever totally trusted it, so the manual sequence-by-sequence reviews often continued).
Of course, if you have a mountain of loot you probably want to protect it. Enter the lawyers. Millennium had always filed on their discoveries; now they had lots of discoveries to protect. But protect from what? Well, the paranoia was a loss of "Freedom to Operate", usually known as FTO. Nobody knew what would stand up as a patent -- but there were instructive examples from the early biotech era of business plans sunk by a loss of FTO -- and expensive lawsuits that clearly marked that loss. So the patenting engine took off -- an expensive insurance policy against an unpredictable future.
Of course, what the lawyers wanted for the filing was as much info as possible -- and the automated analyses provided lots for them. But, they had been designed to be viewed in a web browser individually, not printed out en masse. Worse yet, by this time Informatics & Legal were in separate buildings -- one of my least pleasant Millennium memories was trying to script the printing a raft of analyses on a printer located in the other building. Plus, if there were inventions then somebody had to have invented them -- such as the person who wrote the code to find them & then reviewed the initial output. And so, I started having dates with the paralegals, an hour of hand-cramping signing of document after document. At one point, there were somewhere between 120-140 patent applications where I was sole or co-inventor.
This was the late 90's and the hype was getting thick -- we were guilty but so were others. Millennium wasn't a big pusher of high gene counts -- at least in the terms of the day (but that's another whole story), but certainly we started selling all those genes we had & the ones we extrapolated were still out there. A key part of the business model was to sell the genes many times -- if we could sell the same gene to Lilly for cardiovascular & Roche for metabolic and AstraZeneca for inflammation, all the better. Not that anything underhanded went on; we'd present the case to each company & most of the deals had exclusivity only within a therapeutic area.
How much did we believe our own Kool Aid? It varied. There was one day where I got in a blue mood because I convinced myself that once MLNM found all the genes we'd put ourselves out of work! But that was an extreme ( and what I hope is the height of my own personal stupidity); most of the time we thought we might be right or we might be overestimating a bunch -- but that our partners were intelligent adults who could make the same calculations. Never did I see an attitude that we were fleecing the suckers.
In particular, I remember one of my colleagues making a comment when the Bayer deal was about to be signed. A premise of that deal is that Millennium would identify proteins which could be easily screened, associate them by multiple means with a plausible role in disease, configure an HTS assay for them -- and then Bayer would quickly get hits from their libraries. Those hits in turn would be used to finish determining whether the protein of interest really played a role in disease. MLNM's (over)confidence in genomics matched by Bayer's (over)confidence in chemistry. My colleague said it was one thing to think up such an idea -- and another to 'go over the cliff' -- and he was nervously surprised that someone else was joining us. He was one of the most sober minded fellows around & wasn't making allusions to
Bayer being foolhardy -- just that we were both taking the leap together. Alas, I didn't think to laugh & reply "The fall will kill you".
The genomics rush, alas, did not end with a huge rush of new drug candidates. We thought we'd get a huge leap in biology -- and we did, but not as big as we thought. Traditional drug development & biology had cleaned out the easy stuff; there weren't tons of hidden gems. The chemical biology concept pretty much disappeared from the Bayer collaboration -- turned out it was long-and-painful to configure all those assays (though we did get them done).
BUT, I will admit to being only a partially reformed genomics fan. We got oversold, and it hurt. Much effort was wasted, and just think of the savings if the patent office had declared that you had to have actual causal function to patent a gene! But, much of what we proposed doing still is worth doing -- or has been done. In some sense the genomics companies were just too early for their own good (though the late entrants such as DeCode haven't fared much better). There are no genomics companies -- yet genomics is everywhere. Basic biology fueled by the genome or the technologies pushed by genomics permeate the drug industry (based on the 2 large pharmas I interviewed at in the year MLNM laid me off & what I can read; constructive dissent on this point is welcomed). Probably no novel small molecule drug development history will be directly pinned back to a 1990's genomics effort -- but also virtually no drugs going forward will have their development unaffected by the knowledge of the genome. Everything is tangled up & confused & merged.
The genomics gold rush was insane & wasteful -- but they were fun times!
I should know -- I was there. I was more than just there, I was a significant part of it. No, I didn't think it up & I won't try to exaggerate my importance, but for what is perhaps the poster child of genomics excess (and if not that, certainly in the Pantheon of genomanic deities).
When I got to Millennium they were still largely focused on the positional cloning of disease genes. But, they had started throwing sequencing capacity at ESTs, small bits of genetic message which serve as toeholds to larger ones. The catch was that the sequencing analysis software had been designed for positional cloning work & not ESTs, and it's a very different ballgame. When sequencing genomic DNA seeing anything which looked like a gene was interesting. But when sequencing stuff that is almost nothing but genes, the challenge was to sort the wheat from the chaff. Lots of scientists spent mind-numbing hours scanning BLAST reports for things of interest, and often found things. But this is a lousy technique -- not only might eyes glaze over (or neurons croak) from monotony, but a really interesting match might not be obvious -- what if the top hit was "Uncharacterized protein X" but the 3rd match down was "TotalPharmaceuticalGold"? Or worse, that BLAST couldn't even find a useable match? Plus, was that a match or an identity -- did you find something new or just rediscover a lousy fragment of the old? More mind numbing staring.
Enter a cocky recent Ph.D. After building up some expertise and some more refined tools (which in their embryonic form nailed me the one gene patent of mine perhaps worth something), I had built a system which churned through all the ESTs and crudely organized them by what made things interesting (and tried to ignore all the boring stuff). Ion channels -- look on this web page. GPCRs -- that's over here. Possible secreted proteins, look at this analysis. Furthermore, it also attempted to amalgamate all the different ESTs into a view which was higher quality, longer and more compact -- and tell you which things were already described as proteins and which might be novel. Plus, more sensitive algorithms than BLAST were used to pull things into families.
Now in all honesty, it wasn't nearly perfect. Some of the mind-numbing review had shifted to me -- the early versions in particular had every homology approved (and named!) by me. The semi-automatically generated names were ugly. Various EST artifacts could join webs of unrelated genes into a horrible tangle. But, now there could be reviews of consolidated, pre-analyzed data (though also in fairness nobody ever totally trusted it, so the manual sequence-by-sequence reviews often continued).
Of course, if you have a mountain of loot you probably want to protect it. Enter the lawyers. Millennium had always filed on their discoveries; now they had lots of discoveries to protect. But protect from what? Well, the paranoia was a loss of "Freedom to Operate", usually known as FTO. Nobody knew what would stand up as a patent -- but there were instructive examples from the early biotech era of business plans sunk by a loss of FTO -- and expensive lawsuits that clearly marked that loss. So the patenting engine took off -- an expensive insurance policy against an unpredictable future.
Of course, what the lawyers wanted for the filing was as much info as possible -- and the automated analyses provided lots for them. But, they had been designed to be viewed in a web browser individually, not printed out en masse. Worse yet, by this time Informatics & Legal were in separate buildings -- one of my least pleasant Millennium memories was trying to script the printing a raft of analyses on a printer located in the other building. Plus, if there were inventions then somebody had to have invented them -- such as the person who wrote the code to find them & then reviewed the initial output. And so, I started having dates with the paralegals, an hour of hand-cramping signing of document after document. At one point, there were somewhere between 120-140 patent applications where I was sole or co-inventor.
This was the late 90's and the hype was getting thick -- we were guilty but so were others. Millennium wasn't a big pusher of high gene counts -- at least in the terms of the day (but that's another whole story), but certainly we started selling all those genes we had & the ones we extrapolated were still out there. A key part of the business model was to sell the genes many times -- if we could sell the same gene to Lilly for cardiovascular & Roche for metabolic and AstraZeneca for inflammation, all the better. Not that anything underhanded went on; we'd present the case to each company & most of the deals had exclusivity only within a therapeutic area.
How much did we believe our own Kool Aid? It varied. There was one day where I got in a blue mood because I convinced myself that once MLNM found all the genes we'd put ourselves out of work! But that was an extreme ( and what I hope is the height of my own personal stupidity); most of the time we thought we might be right or we might be overestimating a bunch -- but that our partners were intelligent adults who could make the same calculations. Never did I see an attitude that we were fleecing the suckers.
In particular, I remember one of my colleagues making a comment when the Bayer deal was about to be signed. A premise of that deal is that Millennium would identify proteins which could be easily screened, associate them by multiple means with a plausible role in disease, configure an HTS assay for them -- and then Bayer would quickly get hits from their libraries. Those hits in turn would be used to finish determining whether the protein of interest really played a role in disease. MLNM's (over)confidence in genomics matched by Bayer's (over)confidence in chemistry. My colleague said it was one thing to think up such an idea -- and another to 'go over the cliff' -- and he was nervously surprised that someone else was joining us. He was one of the most sober minded fellows around & wasn't making allusions to
Bayer being foolhardy -- just that we were both taking the leap together. Alas, I didn't think to laugh & reply "The fall will kill you".
The genomics rush, alas, did not end with a huge rush of new drug candidates. We thought we'd get a huge leap in biology -- and we did, but not as big as we thought. Traditional drug development & biology had cleaned out the easy stuff; there weren't tons of hidden gems. The chemical biology concept pretty much disappeared from the Bayer collaboration -- turned out it was long-and-painful to configure all those assays (though we did get them done).
BUT, I will admit to being only a partially reformed genomics fan. We got oversold, and it hurt. Much effort was wasted, and just think of the savings if the patent office had declared that you had to have actual causal function to patent a gene! But, much of what we proposed doing still is worth doing -- or has been done. In some sense the genomics companies were just too early for their own good (though the late entrants such as DeCode haven't fared much better). There are no genomics companies -- yet genomics is everywhere. Basic biology fueled by the genome or the technologies pushed by genomics permeate the drug industry (based on the 2 large pharmas I interviewed at in the year MLNM laid me off & what I can read; constructive dissent on this point is welcomed). Probably no novel small molecule drug development history will be directly pinned back to a 1990's genomics effort -- but also virtually no drugs going forward will have their development unaffected by the knowledge of the genome. Everything is tangled up & confused & merged.
The genomics gold rush was insane & wasteful -- but they were fun times!
Tuesday, January 06, 2009
Watson's solo discovery of DNA
Well, my memory must be truly failing. No offense to Honest Jim, but I always thought he had a partner in finding the structure of DNA. And didn't some third guy share in the Nobel also? Plus, isn't there some experimentalist that people grouse should have gotten some credit?
But, I stand corrected:
Now, some might warn that the Internet doesn't always have reliable information, but this is from a .edu site (and not some student's personal page either), so it must be right, right?
But, I stand corrected:
In the last 50 years since Watson first discovered the structure of DNA, many advances have been made to enable researchers to study and dissect this macromolecule.
Now, some might warn that the Internet doesn't always have reliable information, but this is from a .edu site (and not some student's personal page either), so it must be right, right?
Tuesday, December 02, 2008
A few questions for Governor Palin
It's hard to believe that it's been a full month since the historic election. Well, depends on how you count a month, but today is the first Tuesday after the first Monday in December.
I was more of a political junkie in my youth, but I haven't sworn off the habit. Only in the last few days was I attempting to handicap the electoral college. TNG was a huge Obama fan, asking every adult in sight whether they would be voting for him. On the flip side, the other ticket had Miss Amanda quite charged up -- the idea of a Canino-American being one heartbeat from the presidency was too much to resist (though she has declared she will nip any groomer who attempts to apply lipstick to her!). Her disappointment that night was quickly salved by Obama's first major policy declaration in his celebratory speech. Alas, her closest kin have not been mentioned as in the running for the White House staff position.
Speaking of Governor Palin, it seems she will not be fading from the limelight. No, indeed it looks like her personal Iditarod will be going for the nomination in 2012. Alaska's chief executive made a number of comments during the campaign which induced consternation in the scientific community. Granted, the fruit fly remark was specifically about research on a totally different bug than Drosophila in a completely agriculturally-targeted setting, but it didn't endear her to the fans of Morgan & Bridges. Given she has four years to prepare, it wouldn't hurt to start now. And, in the spirit of reuse, should she not run it would seem the majority of these queries would apply to the majority of other Republicans who went for the high office this year.
1) You have publically taken stands that some views held by a minority (or less) of the scientific community should be accepted and used as the basis for policy decisions (e.g. the existance and/or cause of global warming trends) and/or taught in public schools as viable alternatives to the majority view (e.g. creationism). How do you choose which 'maverick' scientific theories have merit and which do not?
2) Which of the following maverick theories, relevant to major issues in this country today, should be taught in public schools or used to guide policy:
2.1) Healthcare (research priorities, Medicare/Medicaid reimbursement policy)
2.1.1) Childhood vaccines cause autism
2.1.2) AIDS can be treated more effectively with vitamin combinations than antiretrovirals
2.1.3) AIDS is caused by lifestyle factors and not the virus HIV
2.1.4) High cholesterol levels do not cause heart disease; cholesterol lowering using drugs risks cancer & depression
2.2) Physical sciences
2.2.1) Petroleum is not a limited supply of fossil remains of ancient lifeforms but rather is constantly created by processes deep in the earth (clearly an area where Ms. Palin has declared as in her sphere of expertise)
2.2.2) Manned space travel through the van Allen belts is guaranteed to be lethal; funding an attempt to land on the moon should be cancelled.
2.2.3) Einstein's Theory of Relativity is clearly wrong, as the concept of time dilation is so opposed to normal experience as to be laughable.
3) Should the U.S. government ever fund research outside its borders? Under what conditions should such operations be funded, if ever?
4) To what degree should non-expert politicians alter the research funding priorities set by experts in the field?
5) What, if any, useful science has come from studying fruit flies? Should the U.S. fund any further research? What other organisms do you also feel are not worth researching?
This is just a draft; readers are invited to submit further questions via the comments
I was more of a political junkie in my youth, but I haven't sworn off the habit. Only in the last few days was I attempting to handicap the electoral college. TNG was a huge Obama fan, asking every adult in sight whether they would be voting for him. On the flip side, the other ticket had Miss Amanda quite charged up -- the idea of a Canino-American being one heartbeat from the presidency was too much to resist (though she has declared she will nip any groomer who attempts to apply lipstick to her!). Her disappointment that night was quickly salved by Obama's first major policy declaration in his celebratory speech. Alas, her closest kin have not been mentioned as in the running for the White House staff position.
Speaking of Governor Palin, it seems she will not be fading from the limelight. No, indeed it looks like her personal Iditarod will be going for the nomination in 2012. Alaska's chief executive made a number of comments during the campaign which induced consternation in the scientific community. Granted, the fruit fly remark was specifically about research on a totally different bug than Drosophila in a completely agriculturally-targeted setting, but it didn't endear her to the fans of Morgan & Bridges. Given she has four years to prepare, it wouldn't hurt to start now. And, in the spirit of reuse, should she not run it would seem the majority of these queries would apply to the majority of other Republicans who went for the high office this year.
1) You have publically taken stands that some views held by a minority (or less) of the scientific community should be accepted and used as the basis for policy decisions (e.g. the existance and/or cause of global warming trends) and/or taught in public schools as viable alternatives to the majority view (e.g. creationism). How do you choose which 'maverick' scientific theories have merit and which do not?
2) Which of the following maverick theories, relevant to major issues in this country today, should be taught in public schools or used to guide policy:
2.1) Healthcare (research priorities, Medicare/Medicaid reimbursement policy)
2.1.1) Childhood vaccines cause autism
2.1.2) AIDS can be treated more effectively with vitamin combinations than antiretrovirals
2.1.3) AIDS is caused by lifestyle factors and not the virus HIV
2.1.4) High cholesterol levels do not cause heart disease; cholesterol lowering using drugs risks cancer & depression
2.2) Physical sciences
2.2.1) Petroleum is not a limited supply of fossil remains of ancient lifeforms but rather is constantly created by processes deep in the earth (clearly an area where Ms. Palin has declared as in her sphere of expertise)
2.2.2) Manned space travel through the van Allen belts is guaranteed to be lethal; funding an attempt to land on the moon should be cancelled.
2.2.3) Einstein's Theory of Relativity is clearly wrong, as the concept of time dilation is so opposed to normal experience as to be laughable.
3) Should the U.S. government ever fund research outside its borders? Under what conditions should such operations be funded, if ever?
4) To what degree should non-expert politicians alter the research funding priorities set by experts in the field?
5) What, if any, useful science has come from studying fruit flies? Should the U.S. fund any further research? What other organisms do you also feel are not worth researching?
This is just a draft; readers are invited to submit further questions via the comments
Saturday, October 18, 2008
Cilantronomics
We had dinner last night in one of favorite local eateries, a wonderful little Mexican place in a neighboring town. When I sat down, my eye was drawn immediately to my sort of dish -- one with a rich sauce combining the tang of tomatillos with the zing of cilantro. I picked well.
I really do love cilantro. Despite an extensive garden growing up, it was only in my adult life that I encountered this herb. I've been making up ever since. It works well in so many situations, not only in Mexican but also a lot of Asian cooking. The excellent Tibetan buffet in Central Square uses it extensively, particularly in a salad that works equally well before the meal as after, with the bite of cilantro contrasting with sweet cherry tomatoes and mango chunks. I even have a pot of it on my desk, which I share with my neighboring cilantrophiles.
However, not everyone loves cilantro. And it isn't just some folks might not like that little edge -- no, for some it tastes awful. Rather than some herbal bite, they taste soap. Or weirder. What other herb has its own http://www.ihatecilantro.com/?
Is it genetic? Alas, there has been a dearth of research on cilantro tasting -- indeed, it doesn't seem to rate an OMIM entry. There is a compound called PTC which is known to untastable by some, including this correspondent, and is genetically linked (my father can taste it; haven't surveyed the rest of the clan). With the help of some research by one of my office neighbors (and fellow cilantro fan), I did learn that 23andMe includes cilantro taste in their questionnaire. It isn't clear whether the other public and private genome projects are tracking this key phenotype.
Okay, I jest a bit. But while the ability to taste cilantro, or PTC, or the host of other innocuous traits which are staples of grade school genetics labs (e.g. widow's peak, hitchhiker's thumb, attached earlobes, etc) aren't exactly critical to understand, they will be interesting to understand. Widow's peak doesn't change someone's life, but to understand it is to understand a bit more about how patterns are laid out. The sciences of smell and taste have advanced tremendously over my lifetime; a whole new taste was found! Identification of smell receptors (recognized by a Nobel) and taste receptors have given great insights -- but we still understand very little.
Are there practical applications for smell & taste research? Of course. But to me the most interesting part is to figure out how it works. PTC doesn't seem so complicated, as the test paper doesn't have any flavor other than paper. But cilantro seems like a much more complicated, and interesting, question. Why does it taste bad rather than just not taste?
Is there an underlying soapiness which I just don't taste? In this case, tasters have a receptor for the magic compound (which is what?) and non-tasters simply lack it. Or does a different receptor bind the compound in tasters, in which case they have a gain-of-function mutation? Or, perhaps they have a partial loss of function -- there are a number of known compounds with concentration-dependent odor, probably due to differential binding to different receptors. In other words, at low concentrations these compounds bind to high-affinity receptors (yielding one perception) and at high concentrations some additional one Or, perhaps a partial gain of function in the non-tasters -- the same model could apply.
No, I wouldn't recommend basing an R01 application on the science of cilantro taste. Nor is it likely to tease a few million from some VCs as the core of a business plan. Cilantro haters will probably never have the option of genetic therapy to alter their perception. But it is still an interesting scientific question, and I look forward to personal genomics shedding some light on it.
I really do love cilantro. Despite an extensive garden growing up, it was only in my adult life that I encountered this herb. I've been making up ever since. It works well in so many situations, not only in Mexican but also a lot of Asian cooking. The excellent Tibetan buffet in Central Square uses it extensively, particularly in a salad that works equally well before the meal as after, with the bite of cilantro contrasting with sweet cherry tomatoes and mango chunks. I even have a pot of it on my desk, which I share with my neighboring cilantrophiles.
However, not everyone loves cilantro. And it isn't just some folks might not like that little edge -- no, for some it tastes awful. Rather than some herbal bite, they taste soap. Or weirder. What other herb has its own http://www.ihatecilantro.com/?
Is it genetic? Alas, there has been a dearth of research on cilantro tasting -- indeed, it doesn't seem to rate an OMIM entry. There is a compound called PTC which is known to untastable by some, including this correspondent, and is genetically linked (my father can taste it; haven't surveyed the rest of the clan). With the help of some research by one of my office neighbors (and fellow cilantro fan), I did learn that 23andMe includes cilantro taste in their questionnaire. It isn't clear whether the other public and private genome projects are tracking this key phenotype.
Okay, I jest a bit. But while the ability to taste cilantro, or PTC, or the host of other innocuous traits which are staples of grade school genetics labs (e.g. widow's peak, hitchhiker's thumb, attached earlobes, etc) aren't exactly critical to understand, they will be interesting to understand. Widow's peak doesn't change someone's life, but to understand it is to understand a bit more about how patterns are laid out. The sciences of smell and taste have advanced tremendously over my lifetime; a whole new taste was found! Identification of smell receptors (recognized by a Nobel) and taste receptors have given great insights -- but we still understand very little.
Are there practical applications for smell & taste research? Of course. But to me the most interesting part is to figure out how it works. PTC doesn't seem so complicated, as the test paper doesn't have any flavor other than paper. But cilantro seems like a much more complicated, and interesting, question. Why does it taste bad rather than just not taste?
Is there an underlying soapiness which I just don't taste? In this case, tasters have a receptor for the magic compound (which is what?) and non-tasters simply lack it. Or does a different receptor bind the compound in tasters, in which case they have a gain-of-function mutation? Or, perhaps they have a partial loss of function -- there are a number of known compounds with concentration-dependent odor, probably due to differential binding to different receptors. In other words, at low concentrations these compounds bind to high-affinity receptors (yielding one perception) and at high concentrations some additional one Or, perhaps a partial gain of function in the non-tasters -- the same model could apply.
No, I wouldn't recommend basing an R01 application on the science of cilantro taste. Nor is it likely to tease a few million from some VCs as the core of a business plan. Cilantro haters will probably never have the option of genetic therapy to alter their perception. But it is still an interesting scientific question, and I look forward to personal genomics shedding some light on it.
Wednesday, October 15, 2008
The Blue Bus Grows Up
A striking characteristic of the Cambridge biotech scene is how it is concentrated in an urban setting. While there are a lot of biotechs elsewhere in Massachusetts, the Hub's hub is clearly a 2+ mile long zone. One challenge this offers is getting to work via Boston's transportation system.
Boston doesn't have an awful transportation network, but it isn't golden either. The transit system is decent, but the routes still largely follow a radial design, with routes that have changed little in the last half century (I kid not; I've seen a map that old & it takes a careful eye to find the differences). An extensive network of commuter rail feeds the downtown, but is split between two termini separated by a mile. The highway network has a number of gaping gaps, due to a mass cancellation of uncompleted highways in the early 1970's. However, this wasn't necessarily bad for biotech; I've spent half my career in offices that would literally be in the middle of the road should those highways have been built (for example, this very different vision for 640 Memorial Drive than a genomics-based pharmaceutical company).
The commuter rail option presents a particular challenge. One station, South Station, is connected to the Red Line subway which has two stops (Kendall & Central) proximal to many biotechs. The other station, North Station, has terrible connections to Cambridge, other than the perhaps future expansion of the zone into the Cambridge-Charlestown-Somerville interzone. But, if you live north of town it's either deal with getting from North Station to Cambridge or brave I-93. So, by multiple subway connections or a tortuous pedestrian path through Mass General's campus to the Red Line, a not tiny cohort of biotechies has made their commute this way.
Then about five years ago a new option appeared. Little blue buses promising a single seat ride from North Station to Cambridge, with a twisting route designed to be near nearly every major employer in the zone. Called EZRide, for $1 anyone can ride, but better yet the larger employers offer ride-all-year stickers.
The service was a bit slow starting up & went through a few hiccups, but over time it has been impressive. If memory serves, the initial frequency was every 30 minutes; this has been steadily dropped so that now a bus shows up every 8 minutes during commute time. However, demand has grown even faster; during core commuting times the bus is at its legal limit, with only ~30 seats and less than a dozen legal standees.
But a couple of weeks ago the announcement came out: a new vendor would be running full size buses on the route. And last week they showed up. On the one hand, there are more seats -- but not as many as one might think due to the layout. On another, more legal standees and less of the EZRide shuffle -- having to exit the bus at the early stops to let people off, as if you were standing you were a cork in aisle of the old buses. The longer buses don't handle the tight turns as well but do away with the most unpleasant aspect of the old buses: their short wheelbase combined with Cambridge's potholes yielded an amusement-park quality bumpy ride (particularly unpleasant if you made the mistake of leaning against the wheelchair lift).
EZRide isn't run by the transit system, but rather by a quasi-public entity called CRTMA which is charged with improving transit into Cambridge. The director, Jim Gascoigne, is energetic and personable and often on the scene, particularly when weather or accidents snarl require emergency re-routing.
In some ways the EZRide highlights issues in Boston. The T does an okay job, but it apparently never occurred to them in several decades that a market existed for a route from North Station to Cambridge. I've also seen the truly surreal quality of Boston from the blue bus: due to some construction, one Boston police officer directed the bus to stop in a new location, where the driver was promptly berated & ticketed by a second Boston officer. This is the town where the mayor had major apoplexy when a nearby airport added Boston to their name; transportation issues are about turf battles as much as moving people around. The planners also have a fondness for expensive megaprojects (the current shopping list can be found here). Several of these would have important benefits for the biotech zone -- for example, the proposed Urban Ring would run right through it & connect the zone to the Longwood Medical Area.
However, perhaps what is more realistic are more EZRide-like services, perhaps connecting to the south (Brookline, Brighton/Allston) that have surprisingly poor connections, or to the large transit hubs to the north (Wellington, Anderson). A direct connection to Charlestown wouldn't be a bad concept either.
In the meantime, I'll keep riding EZRide. And anxiously awaiting my train line(s) getting the free WiFi service a few lucky commuters have gotten to pilot. One more good reason to stay off the road and out of my car!
Boston doesn't have an awful transportation network, but it isn't golden either. The transit system is decent, but the routes still largely follow a radial design, with routes that have changed little in the last half century (I kid not; I've seen a map that old & it takes a careful eye to find the differences). An extensive network of commuter rail feeds the downtown, but is split between two termini separated by a mile. The highway network has a number of gaping gaps, due to a mass cancellation of uncompleted highways in the early 1970's. However, this wasn't necessarily bad for biotech; I've spent half my career in offices that would literally be in the middle of the road should those highways have been built (for example, this very different vision for 640 Memorial Drive than a genomics-based pharmaceutical company).
The commuter rail option presents a particular challenge. One station, South Station, is connected to the Red Line subway which has two stops (Kendall & Central) proximal to many biotechs. The other station, North Station, has terrible connections to Cambridge, other than the perhaps future expansion of the zone into the Cambridge-Charlestown-Somerville interzone. But, if you live north of town it's either deal with getting from North Station to Cambridge or brave I-93. So, by multiple subway connections or a tortuous pedestrian path through Mass General's campus to the Red Line, a not tiny cohort of biotechies has made their commute this way.
Then about five years ago a new option appeared. Little blue buses promising a single seat ride from North Station to Cambridge, with a twisting route designed to be near nearly every major employer in the zone. Called EZRide, for $1 anyone can ride, but better yet the larger employers offer ride-all-year stickers.
The service was a bit slow starting up & went through a few hiccups, but over time it has been impressive. If memory serves, the initial frequency was every 30 minutes; this has been steadily dropped so that now a bus shows up every 8 minutes during commute time. However, demand has grown even faster; during core commuting times the bus is at its legal limit, with only ~30 seats and less than a dozen legal standees.
But a couple of weeks ago the announcement came out: a new vendor would be running full size buses on the route. And last week they showed up. On the one hand, there are more seats -- but not as many as one might think due to the layout. On another, more legal standees and less of the EZRide shuffle -- having to exit the bus at the early stops to let people off, as if you were standing you were a cork in aisle of the old buses. The longer buses don't handle the tight turns as well but do away with the most unpleasant aspect of the old buses: their short wheelbase combined with Cambridge's potholes yielded an amusement-park quality bumpy ride (particularly unpleasant if you made the mistake of leaning against the wheelchair lift).
EZRide isn't run by the transit system, but rather by a quasi-public entity called CRTMA which is charged with improving transit into Cambridge. The director, Jim Gascoigne, is energetic and personable and often on the scene, particularly when weather or accidents snarl require emergency re-routing.
In some ways the EZRide highlights issues in Boston. The T does an okay job, but it apparently never occurred to them in several decades that a market existed for a route from North Station to Cambridge. I've also seen the truly surreal quality of Boston from the blue bus: due to some construction, one Boston police officer directed the bus to stop in a new location, where the driver was promptly berated & ticketed by a second Boston officer. This is the town where the mayor had major apoplexy when a nearby airport added Boston to their name; transportation issues are about turf battles as much as moving people around. The planners also have a fondness for expensive megaprojects (the current shopping list can be found here). Several of these would have important benefits for the biotech zone -- for example, the proposed Urban Ring would run right through it & connect the zone to the Longwood Medical Area.
However, perhaps what is more realistic are more EZRide-like services, perhaps connecting to the south (Brookline, Brighton/Allston) that have surprisingly poor connections, or to the large transit hubs to the north (Wellington, Anderson). A direct connection to Charlestown wouldn't be a bad concept either.
In the meantime, I'll keep riding EZRide. And anxiously awaiting my train line(s) getting the free WiFi service a few lucky commuters have gotten to pilot. One more good reason to stay off the road and out of my car!
Tuesday, October 14, 2008
Panda genome arrives
China announced over the weekend the completion of the giant panda genome.
For the benefit of presidential candidates who can't conceive of the value of scientific research on bears I'll suggest a few questions worth exploring in the panda genome (beyond the obvious direction of weapons development)
First, the panda genome is one more mammalian genome to add to the zoo. For comparative purposes you can never have too many. Since other carnivore genomes are done (first & foremost the dog, but cat as well), this is an important step towards understanding genome evolution within this important group. It is the first bear genome, but with the price of sequencing falling it is likely that the other bears will not be in the extremely distant future (with the possible exception of Ursa theodoris).
Second, completion of a genome gives a rich resource of potential genetic variants. In the case of an endangered wildlife species such as panda, these will be useful for developing denser genetic maps which can be used to better understand the wild population structure and the gene flow within that structure. Again, if you are running for president please read this carefully: this has nothing to do with paternity suits. If you want to manage wildlife intelligently and make intelligent decisions about the state of a species, you want to know this information.
Third, pandas have many quirks. That bambooitarian diet for starters. Since they once were carnivores, it is likely that their digestive systems haven't fully adapted to the bamboo lifestyle. Comparisons with other carnivores and with herbivores may reveal digestive tract genes at various steps in the route from meat-eater to plant-eater.
Fourth, as the press release points out, there are many questions critical to preserving the species which (with a lot of luck) the genome sequence may give clues to. First among these: why is panda fertility so low? U.S. zoos have been doing amazingly well in this century, but that's only 4 breeding pairs. The Chinese zoos have many more pandas & many more babies, but it's going to take a lot more to save the species.
For the benefit of presidential candidates who can't conceive of the value of scientific research on bears I'll suggest a few questions worth exploring in the panda genome (beyond the obvious direction of weapons development)
First, the panda genome is one more mammalian genome to add to the zoo. For comparative purposes you can never have too many. Since other carnivore genomes are done (first & foremost the dog, but cat as well), this is an important step towards understanding genome evolution within this important group. It is the first bear genome, but with the price of sequencing falling it is likely that the other bears will not be in the extremely distant future (with the possible exception of Ursa theodoris).
Second, completion of a genome gives a rich resource of potential genetic variants. In the case of an endangered wildlife species such as panda, these will be useful for developing denser genetic maps which can be used to better understand the wild population structure and the gene flow within that structure. Again, if you are running for president please read this carefully: this has nothing to do with paternity suits. If you want to manage wildlife intelligently and make intelligent decisions about the state of a species, you want to know this information.
Third, pandas have many quirks. That bambooitarian diet for starters. Since they once were carnivores, it is likely that their digestive systems haven't fully adapted to the bamboo lifestyle. Comparisons with other carnivores and with herbivores may reveal digestive tract genes at various steps in the route from meat-eater to plant-eater.
Fourth, as the press release points out, there are many questions critical to preserving the species which (with a lot of luck) the genome sequence may give clues to. First among these: why is panda fertility so low? U.S. zoos have been doing amazingly well in this century, but that's only 4 breeding pairs. The Chinese zoos have many more pandas & many more babies, but it's going to take a lot more to save the species.
Thursday, September 18, 2008
Great Galloping Gerbils!
An item on CNN mentioned that satellite technology will be employed to monitor the endangered California Kangaroo Rat. This reminded me of a Nature paper this summer I meant to mention, because the image blew me away (plus it's the first time I've seen Google Earth used as a source for a scientific paper!).
The paper is about models of disease spread (these gerbils are reservoirs for plague), but the thing which jumped out was the Google Earth image; the two images above are from around the same region of Kazakhstan. The gerbils clear vegetation from around their burrows, and these burrows are in huge complexes. The more zoomed in image above is several kilometers wide and yet is packed with gerbil burrows. If you have Google Earth and look around 44.766991 76.449699 you can zoom way out and still see the gerbil complexes. I saw some huge prairie dog towns out west when I was a boy, but nothing on this scale!
How many animals leave traces which can be seen from 30+Km up (the image quality is uneven for this region of the world in Google Earth -- clearly shots are merged from different seasons and resolutions, but 30Km is a conservative estimate)? Human activity obviously. When I think of animal-built structures I generally jump to beaver dams or termite mounds, but they aren't nearly this extensive.
Monday, August 25, 2008
The Joys of DIY Dynamic Programming
For nearly the first decade of my bioinformatics career I carried around a dirty little secret -- well, at least at times I felt it was one. I had coded many things, I could explain many algorithms, but I had never coded a dynamic programming alignment algorithm -- the core to so much I did. I had slightly hacked one version (just to have it do an all-all comparison of a database, doing each possible pairing only once). Finally, for a bunch of reasons, I sat down and did it -- my very own Smith-Waterman implementation.
I'm reminded of this because a couple of weeks ago I rolled back my sleeves and knocked one out again. Now, just the fact I did this reveals a bit about me. I did find at least two freely available C# implementations on-line (e.g. the C# version of JAlign) and there is a plethora of C implementations. There is also Ewan Birney's magnificent Dynamite, pretty much the catch-all for the field (Dynamite is a programming kit for doing this; in effect a programming language for dynamic programming). But, partly as a point of pride & partly because I saw I'd need to hack the one C# copy I looked at in detail, I did it. I even wrote a schmancy version -- a simple cDNA to genomic sequence aligner with two classes of gaps (one being an intron, with a really trivial model of a splice junction -- I think it used dinucleotides) All coded in Perl -- no speed demon, but it solved the problem where we needed it.
Now, it took me a good few hours to do it -- better than the few days of the first time, but not instant. I can claim that this time I didn't fall back on any study aids, such as the many online descriptions or Eddy & Durbin's & co. very well written book.
The implementation says a lot about me too. I thought of many ways to code it and finally settled on one. For example, there is the question of how to represent the alignment matrix; I used a two-dimensional array scheme (actually implemented using dictionaries -- a holdover from my Perl-centric days) but I could have also made it a graph of nodes. There is also the actual thrashing through the matrix -- the algorithm is inherently recursive, but following familial idiosyncracies I wrote the code to use loops -- well, actually I completely waffled and implemented so it can use recursion, but actually loops through! The applications I'm considering are going to be short alignments, so I didn't worry about memory efficiency (who wants to be that will bite me back!) nor did I fixate on speed (care to double the bet?) -- indeed, I wrote it to allow all sorts of baroque variations, such as different penalties for opening gaps in the two different sequences & for basic profile-to-sequence alignments. Plus it is either Smith-Waterman (local) or Needleman-Wunsch-Sellers (global), with a simple toggle.
So now the pitch: If you are a bioinformatics programmer & you haven't written one, I urge you to do it. It's great practice & nothing illustrates an algorithm like trying to implement it. If you don't consider yourself a programmer, guess what? It's perhaps not the obviously easy first start, but just thinking about it will stretch your mind. Plus, you get a free bioinformatics Rorschach test from your implementation choices!
One last thought: who can think up (and execute) the most comically baroque -- but functional -- implementation of S-W/NWS? Has it already been done in PostScript? How about in a relational database (I've written some pretty baroque SQL this year, but I doubt I could tackle this)? S-W as an Excel spreadsheet? Coded with glider guns? A full description for a true Turing machine? Of course, the grand prize winner would clearly either be to build a DNA computer to compute an alignment -- but perhaps that could even be topped by implementing the algorithm with living cells as the alignment cells!
I'm reminded of this because a couple of weeks ago I rolled back my sleeves and knocked one out again. Now, just the fact I did this reveals a bit about me. I did find at least two freely available C# implementations on-line (e.g. the C# version of JAlign) and there is a plethora of C implementations. There is also Ewan Birney's magnificent Dynamite, pretty much the catch-all for the field (Dynamite is a programming kit for doing this; in effect a programming language for dynamic programming). But, partly as a point of pride & partly because I saw I'd need to hack the one C# copy I looked at in detail, I did it. I even wrote a schmancy version -- a simple cDNA to genomic sequence aligner with two classes of gaps (one being an intron, with a really trivial model of a splice junction -- I think it used dinucleotides) All coded in Perl -- no speed demon, but it solved the problem where we needed it.
Now, it took me a good few hours to do it -- better than the few days of the first time, but not instant. I can claim that this time I didn't fall back on any study aids, such as the many online descriptions or Eddy & Durbin's & co. very well written book.
The implementation says a lot about me too. I thought of many ways to code it and finally settled on one. For example, there is the question of how to represent the alignment matrix; I used a two-dimensional array scheme (actually implemented using dictionaries -- a holdover from my Perl-centric days) but I could have also made it a graph of nodes. There is also the actual thrashing through the matrix -- the algorithm is inherently recursive, but following familial idiosyncracies I wrote the code to use loops -- well, actually I completely waffled and implemented so it can use recursion, but actually loops through! The applications I'm considering are going to be short alignments, so I didn't worry about memory efficiency (who wants to be that will bite me back!) nor did I fixate on speed (care to double the bet?) -- indeed, I wrote it to allow all sorts of baroque variations, such as different penalties for opening gaps in the two different sequences & for basic profile-to-sequence alignments. Plus it is either Smith-Waterman (local) or Needleman-Wunsch-Sellers (global), with a simple toggle.
So now the pitch: If you are a bioinformatics programmer & you haven't written one, I urge you to do it. It's great practice & nothing illustrates an algorithm like trying to implement it. If you don't consider yourself a programmer, guess what? It's perhaps not the obviously easy first start, but just thinking about it will stretch your mind. Plus, you get a free bioinformatics Rorschach test from your implementation choices!
One last thought: who can think up (and execute) the most comically baroque -- but functional -- implementation of S-W/NWS? Has it already been done in PostScript? How about in a relational database (I've written some pretty baroque SQL this year, but I doubt I could tackle this)? S-W as an Excel spreadsheet? Coded with glider guns? A full description for a true Turing machine? Of course, the grand prize winner would clearly either be to build a DNA computer to compute an alignment -- but perhaps that could even be topped by implementing the algorithm with living cells as the alignment cells!
Sunday, August 24, 2008
One more Olympic thought
One other item that was in the mental draft of yesterday's Olympic pondering, but was inadvertantly dropped. Another possible genetically-driven edge in athletic performance would not be directly on performance but on the reaction to performance. Prime athletes might have different pain or endorphin responses, less post-exercise inflammation, different injury responses. Some of these might be specific to specific events or types of sports -- joint pounding running or gymnastics puts very different stresses on the body than something like swimming or speedskating.
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