Personalized Annoyance of Research Enthusiast (PARE)

Last night I finally got my paws on a paper which started out on a frustrating tack. Last week, a flurry of news items heralded a new approach from Vogelstein's group at Johns Hopkins that involved second generation sequencing of patient tumor samples. But, the early reports claimed it had been published in Science Translational Medicine, whereas it most certainly wasn't there except a suggestive teaser about the next week's issue. I thought perhaps someone had really blown it and ignored an embargo, but then it turned out the AAAS meeting is going on and the work was presented there. Few things more irritating than a paper being bandied about that I can't get my eyes on! Plus, I have a manuscript due next week that this might be relevant to, so the desire to get a copy was intense!

Yesterday, it really did come out. You'll need a subscription to read it -- though that is only $50 for online access if you already have a Science personal subscription. The gist of the paper showed up in the reports. Using SOLiD, they sequenced cancer genomes around 1X coverage using 1.5Kb mate-paired libraries using 25 long reads. For copy number analysis they also used single end reads. The key point is to identify rearrangements using the mate-paired fragments.

Now, many papers have looked at rearrangements in cancer using mate paired or paired end strategies. What sets this paper apart is doing something with it: turning these into patient specific tumor markers (an approach they call PARE for personalized analysis of rearranged ends). Because rearrangements are specific to the tumor and not at all like what is in the patient's normal DNA, they make great PCR amplicons for finding the tumor. Indeed, they were able to detect tumor DNA in blood with their assays.

This is an example of second generation sequencing getting very close to the clinic. But what will it take to get it there? Many of the news items claimed the cost might be soon down around $3K. Now, to do this properly you really need to either do the sequencing on both normal and tumor DNA or make a bunch of assays and expect some to be duds. Why? Because some of these structural changes will either be alignment noise or private germline structural variants. They do use copy-number analysis to filter the list -- many tumor rearrangments will be associated with local copy number amplification. But more importantly, the cost numbers sound suspiciously like reagent-only cost, not fully-loaded. Fully loaded costs include the ~$1.5M sequencing center (SOLiD + prep gear + compute farm), real estate & salaries. These could easily double or triple that cost, though someone who actually owns a green eyeshade should figure that out for sure.

The paper talks a little bit about the risk that as a tumor evolves one of these markers might be lost. This is particularly the case here because, unlike many papers, they really aren't worried if the rearrangement is driving the tumor. It's a handy landmark, though you would find driving rearrangements with it too. But, one particular worry is that a given rearrangement might not be in the dominant clone or a clone which treatment selects for survival. So having multiple markers will be a useful protection -- though that will up costs.

But back to irritating: a key value left out of this paper (and unfortunately most such papers) is the amount of input DNA for sequencing. Many of these sorts of protocols start with 5-10 micrograms of DNA, though some mate-pair schemes call for 5 to 10 times that. For some tumor types, that's a kings's ransom -- particularly for recurrent tumors or inoperable ones. Even beyond that, large scale application of this approach will require automating the library construction process end-to-end.

It's also worth noting that this is an application where absolute speed isn't critical . For generating a marker to be used for long-term following of the tumor, needing two weeks for SOLiD library prep & assembly and another few weeks to develop the PCR assays won't be a major roadblock. But, any sequencing-based approach used to determine treatment strategy needs to turn around results in not much more than 1-2 days. That's a high hurdle, and a wide open spot for fast sequencing technologies such as 454, PacBio, nanopores & Ion Torrent.

This is also an approach where someone with a long but noisy sequencing technology should take a hard look. Calling rearrangements with very long reads shouldn't require nearly the level of accuracy as calling point mutations.


Leary, R., Kinde, I., Diehl, F., Schmidt, K., Clouser, C., Duncan, C., Antipova, A., Lee, C., McKernan, K., De La Vega, F., Kinzler, K., Vogelstein, B., Diaz, L., & Velculescu, V. (2010). Development of Personalized Tumor Biomarkers Using Massively Parallel Sequencing Science Translational Medicine, 2 (20), 20-20 DOI: 10.1126/scitranslmed.3000702

Marco Island is HOT!

The Marco Island Advances in Genome Biology and Technology, or AGBT (or just Marco Island) conference started up today. Whatever weather they're having is better than the cold rain that soaked my commute.

A sure sign a conference is hot is that there are lots of announcements prior to the conference that could be at the conference. So, we've been treated to lots of announcements from established players (such as Illumina and ABI) and new entrants -- Pacific Biosciences has announced that they will launch their system there and has already been lining up sample prep & informatics partners and announcing their early access sites. PacBio has also started making noise about a follow-on instrument that will be for clinical apps -- launched in 2014!! Puh-leeze, that is the inconceivable future!

ABI had a new announcement today -- they're own baby SOLiD (officially the P1) to come out later this year, joining the previously announced 454 junior and Illumina IIe. The claim of "cost per sample as low as $200" is a eyebrow raiser -- I'm guessing that is for a highly multiplexed sample mix. List price at $230K and 50Gbases per run is the claim.

Ion Torrentcompany has been in a very noisy stealth mode -- founder Jonathon Rothberg gave a huge tease of a talk at the Providence meeting that ended just before giving anything specific. Of course, given that he launched 454, he gets a little slack in the hype department as he has delivered. BioIT World has a very nice writeup (which editor Kevin Davies was kind enough to point out to me about 2 weeks ago -- a sign of sloth on my part that I haven't mentioned it earlier). They didn't exactly succeed in peeling off the layers of secrecy, but it is far more detail than I've seen anywhere else (but in line with the few rumors I did hear). Rothberg will be giving the final talk at AGBT, and was expected to actually reveal some details. The general buzz is 454-style chemistry but with electronic -- not optical -- detection.

So it dropped my jaw through the floor today when Ion Torrent announced that they will be launching in April, starting with the gifting of two systems through a grant competition. I'd figured from what I heard & from the general pattern with AGBT that this year would bring the wraps off but nothing would be operating for another year or two (some previous AGBT announcees have seemingly faded to oblivion).

Of course, the devil is in the execution. Will they actually be able to deliver working systems? What will the reagent costs run? How reliable will the instruments be? And what will the performance profile look like -- run lengths, error rates & error modes and input DNA amounts & preparation.

Hold onto your seats -- and watch the #AGBT Twitter feed! Things will continue to get interesting.

To Stockholm via Ph.D. Thesis

The Scientist has a profile of Aaron Ciechanover, who shared the Nobel Prize for work on the proteasome. His Nobel-cited work began in his Ph.D. thesis.

In one of the physics books I was recently reading (I forget which one now, might have been How to Teach Physics to Your Dog, but I think it was Six Easy Pieces) it was mentioned that Louis de Broglie's committee wasn't sure what to do with his crazy proposal that everything has both particle and wave natures, but after consulting with Einstein awarded him his degree. Of course, this proposal withstood experimental test and led to a Nobel.

Anyone know other examples of Nobels which cite the laureate's thesis work?

Non-benign genetic carrier status

Earlier this week the Wall Street Journal carried an article addressing the growing interest in finding health issues related to being a carrier of a recessive genetic disease.

Three diseases were discussed in some detail. Sickle-cell anemia is generally thought of as being very harmful when homozygous but essentially benign when heterozygous. But, it has been known for a while that heterozygotes (called sickle trait) can experience red blood cell sickling (and the accompanying pain and tissue damage) under low oxygen tension. The WSJ journal article points out that such sickling can also occur during strenuous physical exercise; the NCAA even has specific guidelines for extra rest for sickle cell heterozygotes.

An emerging story mentioned in the article is the risks of being a carrier for fragile X, an X-linked disorder which can severely impede mental development. Fragile X is a nucleotide triplet repeat expansion disease, meaning that some males who have a disease allele will have mild or no symptoms but can transmit a more severe form of the disease. Male carriers of these alleles can develop severe neurodegeneration late in life, a condition called FXTAS. Female carriers appear to be at greater risk for anxiety and depression as well as premature ovarian failure.

Other examples mentioned are a greater risk of Parkinson's in Gaucher's disease carriers (at about 5-fold greater risk than the general population) and increased risks of chronic sinus disease and asthma in cystic fibrosis carriers.

Touched on in the article is the fact that many carriers are completely unaware of the fact. Most testing is done if someone is (a) aware of the disease in the family and (b) considering having children. Even then, not everyone is tested. Many of these diseases are rare enough that many carriers could be unaware of the disease being present in the family -- if it never happened to manifest or be correctly diagnosed. Other persons may have missing knowledge about their parentage.

I don't know for certain, but I doubt many of these disease-causing mutations are in the tests used by most personal genetic profiling companies, other than the emerging ones focused on reproductive counseling. The availability in the near future of cheap whole genome sequencing in the near future could lead to huge numbers of people discovering these genetic issues. But, for most recessive diseases we do not know any possible negative effects of carrier status. Much more research will be needed to tease out additional issues.

Anybody know some good bioinformatic programming problems?

I recently found out that I've received a summer undergraduate intern slot. I have a soft spot for summer internships -- my own was a great experience -- and the company runs a very nice program, with specific social and learning experiences for the cadre. Anyone interested in applying should do so through the company website (and not here!). I do promise not to fill this space with "can you believe what the intern did today?!?!?", though executing "sudo rm -r /" might earn a slot!

I'm still trying to sketch out a grand scheme for the internship. But, it will certainly combine a certain amount of data analysis with a certain amount of programming. One person I've phone-screened has already asked about suggestions for programming problems to practice on. It's a great show of initiative, which I like but discovered for which I wasn't really prepared.

The challenge for me is to rewind my brain back to an early stage and remember what makes a good -- but doable -- problem. In my head, everything either seems too trivial or potentially discouragingly difficult. So, I'd be very interested in examples of programming challenges given to early programmers with a significant bioinformatics angle -- no bubble sorts or games of Wumpus!

I did find a couple of links with some examples: one from MIT and another from Duke (these links are really a level above). I'd love to find other examples -- and mostly don't care about the language used in the examples. I'm probably going to nudge my intern towards Java/Scala (leveraging BioJava as much as possible), perhaps if only to encourage me to put some more time in on my own retraining project.

So, any suggestions?

More Than One Way to Skin a Kumquat

My recent piece on citrus seems to have struck a chord, based on the multiple comments and the fact that GenomeWeb's blog picked up on it as well. That's all very gratifying, tbut also stirred me to notice what I had missed on the subject. No, not the obvious point that getting some genome sequences is just a tiny first step to my grand bioengineering dream. And not what the TIGS review pointed out, that American markets in particular have tended to favor uniformity over quality or novelty (though perhaps that is changing, at least in high-end markets). Nope, what bugs me now is missing the obvious about kumquats.

Now, as I mentioned, they're hard to find. I checked some mail order places and the seasons apparently vary depending on where they are grown. But at Christmas time I could find only one market -- and I checked a half dozen -- which was selling them. Even the same high end chain that sold them next to work didn't offer them at the outlet nearest my home. So, don't be embarassed if you've never tried one.

The first beauty of a kumquat is you eat the whole thing -- skin and all (it is advisable to spit the pip). But the second beauty is within that -- the two very different tastes. The skin is thin but very sweet, whereas the flesh is tart.

Natural kumquats therefore are a binary package, and an unusual one. I'm trying to think of other fruits eaten skin-on for which the skin has a distinctive and pleasant taste. I eat lots of fruit skins, but most are just texture & roughage as far as I can tell. Concord grapes are an obvious exception -- I'll confess to swiping them from my neighbor's trellis growing up. The pulp is gteen and much like a seedless green grape in taste (but decidedly NOT seedless!) whereas the skin has the delicious Concord-ness to it. Many other grapes are probably similar. Certainly the winemakers use skin-in or no skin as a point of control over taste.

Now, with all citrus the skin and pulp can have very different aromas. Orange zest adds a distinctive flavor which is different than adding orange juice to a recipe. With a bit of genetic sleuthing (GFP limes?), the promoters responsible for specific production in skin and flesh can be worked out. And then the engineering can get another dimension -- different tastes in kumquat skin and pulp.

Clearly what I have in mind is a lot of genetically engineered fruit, which I will be happy to taste. GMO foods have not met much acceptance, but as some have pointed out before a significant issue is that most engineered traits have been to benefit producers (pest / pesticide resistance) with no benefit to the consumer beyond price. Early attempts at longer shelf life tomatoes and carrots flopped, but that's still more of a benefit for the producer than the consumer. Nutritionally-augmented foods (e.g. "golden rice") address nutritional needs which Western activists don't face.

Present something really novel and exciting in terms of flavor experience, and then you'll see a real separation of those who are truly committed to a no-GMO purity and those who can be tempted away. Furthermore, simply rewiring existing citrus biosynthetic pathways would dodge some of the other arguments raised against GMOs, in terms of introducing allergens or such.

It is a bit of optimistic to think I'll ever see a line of flavor-augmented mix-and-match kumquats. But if anyone starts making some, I'll be happy to volunteer for the taste testing squad.

Celebrating Citrus

I've been on a citrus kick at work lately, trying out different varieties I picked up at one of the adjacent grocery stores (curiously, we're sandwiched between 2). When I was growing up I think I knew only seeded oranges, navel oranges, tangerines, tangelos, grapefruit, lemons and limes. Through some combination of better awareness and better availability, there's a lot more I can find. I gained some notoriety this week by bringing a pummelo to a breakfast meeting; if you haven't seen one, they make grapefruit look small. Tastewise, it's a bit milder and a bit sweeter than a grapefruit.

A lot of this is seasonal, as I've been finding. Kumquats are a nearly perfect desk snack -- completely neat except for the need to spit the pips -- but seem to be available only around the New Year -- and then only in a few stores. Amongst the treats currently available are clementines -- almost as good a desk snack as kumquats though you do need to peel them and some wonderful non-orange oranges. Cara cara oranges turn out to be delightfully pink on the inside whereas the blood oranges are precisely named -- after one knife slip I found myself searching my skin in vain for the source of the red spots on the table.

What I can find in the store is still just a tiny sample of all the citrus known to exist. My brother sent me a New Yorker article on professional flavorists which mentioned many more, including pummelos with quite foul-smelling rinds but yet another delicious flavor of pulp.

Just after the turn of the century, a very good review of citrus genetics was published in Trends in Genetics. The molecular story appears to point to all this wonderful diversity originating from three wild species. Amazing! It gets stranger when you delve into the reproductive biology of citrus -- not only can they be propagated sexually or by cuttings, but they are quite adept at apomixis, the development of a new individual from an unfertilized ovum.

There is, of course, a citrus genome project, hosted at the JGI. And perhaps more predictably, I'm a bit impatient for completion. As the rationale page explains (and from which I've stolen the wonderful image of citrus diversity above), the sweet orange genome is only 382 Mb. When the new HiSeq and SOLiD instruments come on-line, they could sequence several individuals at 40X coverage in one run.

What I'd really like is to have the genetic blueprint for all those wonderful flavors and colors in order to repackage them. Keeping in mind, of course, that some useful characteristics (such as seedlessness) aren't simple traits but products of karytype (I would love a fully seedless kumquat!). Imagine if you could have a whole series of clementine-like fruits, with the size & easy peeling characteristics but with the whole range of other citrus flavors and colors genetically grafted in -- cara cara clementines and blood clementines and ruby red clementines and perhaps even sweet lemontines and key clemenlimes. What a wonderfully healthy snacking then!

Disagreeing to Disagree

A year ago (almost exactly) I wrote an entry taking to task a paper analyzing protein kinases in the draft chimpanzee genome. After writing that entry, I felt it proper to leave a comment at the journal (BMC Genomics). Instead of publishing the comment, the editor invited me to formalize my criticisms and perhaps give positive suggestions of how to do such an analysis. Between Codon dissolving, my interlude of consulting and starting Infinity this got pushed to late May, it went out for review & a round of revision & then the original authors were invited to write a rebuttal. By the time their rebuttal came back (late fall), I decided I was getting a bit worn on the whole thing and just tweaked my submission to underline a few things rather than go hammer-and-tongs for a counter-rebuttal.

Anyhow, my criticism and the authors' response is now up on the BMC Genomics website & indexed in Medline (hooray!). I won't go hammer-and-tongs here either. But, whereas sometimes two parties in an argument agree to disagree, having established consensus on what they are arguing about, I would characterize this with this post's title: the authors' pretty much argue that all of my points are based on misunderstandings and misinterpretations of what they wrote. I of course don't agree on that point.

In the end, one key point is that they are arguing they did the best with the dataset they chose to use as a source and I argue that they should have been more skeptical of the source. In the end, what I believe is that many of their unusual results will go away if the chimp genome is finished or if the chimp mRNAs under dispute are questioned.

So, this ends up as another project for my "Proposal to sequence genomes KR thinks deserve sequencing" grant. Ha! It would be fun to have a slush fund to pursue these sorts of things. $10K and access to chimp poly-A RNA is all this problem would need. But, I'm not independently wealthy so it will remain a pipe dream. Of course, there is a bonobo sequencing project and that would be somewhat useful. But if you go looking for such, make sure you google "Bonobo ensembl" not "Bonobo ensemble" as my smartphone helped me do -- you get some bizarre links but nothing to do with great ape sequencing.

Whither science museums?

Last week at this time I was surviving a terrible electrical storm -- tremendous cracks and crackles all around me. Luckily, it was just the lightning show at the Boston Museum of Science, featuring the world's largest Van de Graaf generator and a pair of huge Tesla coils and other sparking whatnots. TNG was part of a huge overnight group.

I love the MoS and always enjoy a trip there. But it isn't hard to go there and wonder what the future of such museums are and how the current management is taking them.

Exhibit #1: The big event at the MoS right now is the traveling Harry Potter show. We of course got tickets and lumped on audio tour. It's great fun, especially if you enjoyed the movies (it is mostly movie props & costumes), but makes absolutely no pretensions of having even a veneer of science. I've seen movie-centric exhibits at science museums before and they usually try to at least portray how movie technology works. None of that here, other than an occasional remark on the audio tour. Clearly, this is a money maker first and a big draw. But do such exhibits represent a dangerous distraction from the mission of a science museum? Would such a show be more appropriate in an art museum (it apparently was in the art museum in Chicago)?

Another exhibit could also could be described as more art than science -- but is that necessarily a bad thing. It was an exhibit of paintings by an artist who attempts to portray large numbers to make them comprehensible. The exhibit was sparse -- a small number of paintings in a large space, and they really didn't do much for me. Many had a pontillist style -- the dots summing to whatever the number was. One even aped Sunday in the Park. But were they really effective?

Art and science are overlapping domains, so please don't lump me as a philistine trying to keep art out. But I still think there are better ways to host art in a science museum. An in depth exhibit on detecting forgeries or verifying provenance would be an obvious example. The exhibit on optical illusions features a number of artworks which can be viewed in multiple ways.

We actually slept in an exhibit called "Science in the Park", which has a number of interactive exhibits illustrating basic physics concepts. It is certainly popular with the kids, but sometimes you wonder if they are actually extracting anything from it. Could such an exhibit be too fun? One example is a pair of swings of different lengths, to illustrate the properties of pendulums. A saw a lot of swinging, but rarely would a child try both. The exhibit also illustrated a serious challenge with interactive exhibits: durability. The section to illustrate angular momentum was fatally crippled by worn out bearings -- the turntable on which to spin oneself could barely allow 2 rotations. Two exhibits using trolleys looked pretty robust -- but then again some kids were slamming them along the track with all their might.

The lightning show is impressive. I think most kids got a good idea of the skin effect (allowing the operator to sit in a cage being tremendously zapped by the monster Van de Graaf). But how much did they take away? How much can we expect?

Some of the exhibits at the museum predate me by a decade or more. There are some truly ancient animal dioramas that don't seem to get much attention. Another exhibit that is quite old -- but has worn well -- is the Mathematica exhibit. There was only two obvious updatings (a computer-generated fractal mountain and an addendum to the wall of famous mathematicians). It has a few interactive items, and most were working (alas, the Moebius strip traverser was stuck).

One of the treats on Saturday morning was a movie in the Omnimax screen, a documentary on healthy and sick coral reefs in the South Pacific. It's an amazing film, and does illustrate one way to really pack a punch with photos. While the photos of dying and dead reefs are sobering, to me the most stunning photo was of a river junction in Fiji. One river's deep brown (loaded with silt eroded from upstream logging) flowed into another's deep blue. I grew up on National Geographic Cousteau specials, and could also appreciate the drama of one of the filmmakers' grim brush with the bends.

One last thought: late last fall I stumbled on a very intriguing exhibit, though it is unlikely to be the destination of any school field trip. It's the lobby of the Broad Institute, and they have a set of displays aimed at the street outside which both explain some of the high-throughput science methods being used and show data coming off the instruments in real time (some cell phone snapshots below). The Broad is just over a mile from the MoS and there's probably a really fat pipe between them -- it would be great to see these exhibits replicated where large crowds might see them and perhaps be inspired.






(01 Feb 2010 -- fixed stupid typo in title & added question mark)

A little more Scala

I can't believe how thrilled I was to get a run-time error today! Because that was the first sign I had gotten past the Scala roadblock I mentioned in my previous post. It would have been nicer for the case to just work, but apparently my SAM file was incomplete or corrupt. But, moments later it ran correctly on a BAM file. For better or worse, I deserve nearly no credit for this step forward -- Mr. Google found me a key code example.

The problem I faced is that I have a Java class (from the Picard library for reading alignment data in SAM/BAM format). To get each record, an iterator is provided. But my first few attempts to guess the syntax just didn't work, so it was off to Google.

My first working version is

package hello
import java.io.File
import org.biojava.bio.Annotation
import org.biojava.bio.seq.Sequence
import org.biojava.bio.seq.impl.SimpleSequence
import org.biojava.bio.symbol.SymbolList
import org.biojava.bio.program.abi.ABITrace
import org.biojava.bio.seq.io.SeqIOTools
import net.sf.samtools.SAMFileReader

object HelloWorld extends Application {

val samFile=new File("C:/workspace/short-reads/aln.se.2.sorted.bam")
val inputSam=new SAMFileReader(samFile)
var counter=0

var recs=inputSam.iterator
while (recs.hasNext)
{
  var samRec=recs.next;
  counter=counter+1
}
  
 println("records: ",counter);

Ah, sweet success. But, while that's a step forward it doesn't really play with anything novel that Scala lends me. The example I found this in was actually implementing something richer, which I then borrowed (same imports as before)

First, I define a class which wraps an iterator and defines a foreach method:

class IteratorWrapper[A](iter:java.util.Iterator[A])
{
    def foreach(f: A => Unit): Unit = {
        while(iter.hasNext){
          f(iter.next)
        }
    }
}

Second, is the definition within the body of my object of a rule which allows iterators to be automatically converted to my wrapper object. Now, this sounds powerfully dangerous (and vice versa). A key constraint is Scala won't do this if there is any ambiguity -- if there are multiple legal solutions to what to promote to, it won't work. Finally, I rewrite the loop using the foreach construct.

object HelloWorld extends Application {
 implicit def iteratorToWrapper[T](iter:java.util.Iterator[T]):IteratorWrapper[T] = new IteratorWrapper[T](iter)

val samFile=new File("C:/workspace/short-reads/aln.se.2.sorted.bam")
val inputSam=new SAMFileReader(samFile)
var counter=0

for (val samRec<-recs) 
  { 
    counter=counter+1 
  }
println("records: ",counter);

Is this really better? Well, I think so -- for me. The code is terse but still clear. This also saves a lot of looking up some standard wordy idioms -- for some reason I never quite locked in the standard read-lines-one-at-a-time loop in C# -- always had to copy an example.

You can take some of this a bit far in Scala -- the syntax allows a lot of flexibility and some of the examples in the O'Reilly book are almost scary. I probably once would have been inspired to write my own domain specific language within Scala, but for now I'll pass.

Am I taking a performance hit with this? Good question -- I'm sort of trusting that the Scala compiler is smart enough to treat this all as syntactic sugar, but for most of what I do performance is well behind readibility and ease of coding & maintenance. Well, until the code becomes painfully slow.

I don't have them in front of me, but I can think of examples from back at Codon where I wanted to treat something like an iterator -- especially a strongly typed one. C# does let you use for loops using anything which implements the IEnumerable interface, but it can get tedious to wrap everything up when using a library class which I think should implement IEnumerable but the designer didn't.

I still have some playing to do, but maybe soon I'll put something together that I didn't have code to do previously. That would be a serious milestone.

The Scala Experiment

Well, I've taken the plunge -- yet another programming language.

I've written before about this. It's also a common question on various professional bioinformatics discussion boards: what programming language.

It is a decent time to ponder some sort of shift. I've written a bit of code, but not a lot -- partly because I've been more disciplined about using libraries as much as possible (versus rolling my own) but mostly because coding is a small -- but critical -- slice of my regular workflow.

At Codon I had become quite enamored with C#. Especially with the Visual Studio Integrated Development Environment (IDE), I found it very productive and a good fit for my brain & tastes. But, as a bioinformatics language it hasn't found much favor. That means no good libraries out there, so I must build everything myself. I've knocked out basic bioinformatics libraries a number of times (read FASTA, reverse complement a sequence, translate to protein, etc), but I don't enjoy it -- and there are plenty of silly mistakes that can be easy to make but subtle enough to resist detection for an extended period. Plus, there are other things I really don't feel like writing -- like my own SAM/BAM parser. I did have one workaround for this at Codon -- I could tap into Python libraries via a package called Python.NET, but it imposed a severe performance penalty & I would have to write small (but annoying) Python glue code. The final straw is that I'm finding it essential to have a Linux (Ubuntu) installation for serious second-generation sequencing analysis (most packages do not compile cleanly -- if at all -- in my hands on a Windows box using MinGW or Cygwin).

The obvious fallback is Perl -- which is exactly how I've fallen so far. I'm very fluent with it & the appropriate libraries are out there. I've just gotten less and less fond of the language & it's many design kludges (I haven't quite gotten to my brother's opinion: Perl is just plain bad taste). I lose a lot of time with stupid errors that could have been caught at compile time with more static typing. It doesn't help I have (until recently) been using the Perl mode in Emacs as my IDE -- once you've used a really polished tool like Visual Studio you realize how primitive that is.

Other options? There's R, which I must use for certain projects (microarrays) due to the phenomenal set of libraries out there. But R just has never been an easy fit for me -- somehow I just don't grok it. I did write a little serious Python (i.e. not just glue code) at Codon & I could see myself getting into it if I had peers also working in it -- but I don't. Infinity, like many company bioinformatics groups, is pretty much a C# shop though with ecumenical attitudes towards any other language. I've also realized I need as basic comprehension of Ruby, as I'm starting to encounter useful code in that. But, as with Python I can't seem to quite push myself to switch over -- it doesn't appeal to me enough to kick the Perl habit.

While playing around with various second generation sequencing analysis tools, I stumbled across a bit of wierd code in the Broad's Genome Analysis ToolKit (GATK) -- a directory labeled "scala". Turns out, that's yet another language -- and one that has me intrigued enough to try it out.

My first bit of useful code (derived from a Hello World program that I customized having it output in canine) is below and gives away some of the intriguing features. This program goes through a set of ABI trace files that fit a specific naming convention and write out FASTA of their sequences to STDOUT:

package hello
import java.io.File
import org.biojava.bio.Annotation
import org.biojava.bio.seq.Sequence
import org.biojava.bio.seq.impl.SimpleSequence
import org.biojava.bio.symbol.SymbolList
import org.biojava.bio.program.abi.ABITrace
import org.biojava.bio.seq.io.SeqIOTools
object HelloWorld extends Application {

  for (i <- 1 to 32)
    {
 val lz = new java.text.DecimalFormat("00")
 var primerSuffix="M13F(-21)"
 val fnPrefix="C:/somedir/readprefix-"
 if (i>16) primerSuffix="M13R"
 val fn=fnPrefix+lz.format(i)+"-"+primerSuffix+".ab1"
 val traceFile=new File(fn)
 val name = traceFile.getName()
 val trace = new ABITrace(traceFile)
 val symbols = trace.getSequence()
 val seq=new SimpleSequence(symbols,name,name,Annotation.EMPTY_ANNOTATION)
 SeqIOTools.writeFasta(System.out, seq);
    }
}

A reader might ask "Wait a minute? What's all this java.this and biojava.that in there?". This is one of the appeals of Scala -- it compiles to Java Virtual Machine bytecode and can pretty much freely use Java libraries. Now, I mentioned this to a colleague and he pointed out there is Jython (Python to JVM compiler) which reminded me of reference to JRuby (Ruby to JVM compiler). So, perhaps I should revisit my skipping over those two languages. But in any case, in theory Scala can cleanly drive any Java library.

The example also illustrates something that I find a tad confusing. The book keeps stressing how Scala is statically typed -- but I didn't type any of my variables above! However, I could have -- so I can get the type safety I find very useful when I want it (or hold myself to it -- it will take some discipline) but can also ignore it in many cases.

Scala has a lot in it, most of which I've only read about in the O'Reilly book & haven't tried. It borrows from both the Object Oriented Programming (OOP) lore and Functional Programming (FP). OOP is pretty much old hat, as most modern languages are OO and if not (e.g. Perl) the language supports it. Some FP constructs will be very familiar to Perl programmers -- I've written a few million anonymous functions to customize sorting. Others, perhaps not so much. And, like most modern languages all sorts of things not strictly in the language are supplied by libraries -- such as a concurrency model (Actors) that shouldn't be as much of a swamp as trying to work with threads (at least when I tried to do it way back yonder under Java). Scala also has some syntactic flexibility that is both intriguing and scary -- the opportunities for obfuscating code would seem endless. Plus, you can embed XML right in your file. Clearly I'm still at the "look at all these neat gadgets" phase of learning the language.

Is it a picnic? No, clearly not. My second attempt at a useful Scala program is a bit stalled -- I haven't figured out quite how to rewrite a Java example from the Picard (Java implementation of SAMTools) library into Scala -- my tries so far have raised errors. Partly because the particular Java idiom being used was unfamiliar -- if I thought Scala was a way to avoid learning modern Java, I'm quite deluded myself. And, I did note that tonight when I had something critical to get done on my commute I reached for Perl. There's still a lot of idioms I need to relearn -- constructing & using regular expressions, parsing delimited text files, etc. Plus, it doesn't help that I'm learning a whole new development environment (Eclipse) virtually simultaneously -- though there is Eclipse support for all of the languages I looks like I might be using (Java, Scala, Perl, Python, Ruby), so that's a good general tool to have under my belt.

If I do really take this on, then the last decision is how much of my code to convert to Scala. I haven't written a lot of code -- but I haven't written none either. Some just won't be relevant anymore (one offs or cases where I backslid and wrote code that is redundant with free libraries) but some may matter. It probably won't be hard to just do a simple transformation into Scala -- but I'll probably want to go whole-hog and show off (to myself) my comprehension of some of the novel (to me) aspects of the language. That would really up the ante.

A plethora of MRSA sequences

The Sanger Institute's paper in Science describing the sequencing of multiple MRSA (methicillin-resistant Staphylococcus aureus) genomes is very nifty and demonstrates a whole new potential market for next-generation sequencing: the tracking of infections in support of better control measures.

MRSA is a serious health issue; a friend of mine's relative is battling it right now. MRSA is commonly acquired in health care facilities. Further spread can be combated by rigorous attention to disinfection and sanitation measures. A key question is when MRSA shows up, where did it come from? How does it spread across a hospital, a city, a country or the world?

The gist of the methodology is to grow isolates overnight in the appropriate medium and extract the DNA. Each isolate is then converted into an Illumina library, with multiplex tags to identify it. The reference MRSA strain was also thrown in as a control. Using the GAII as they did, they packed 12 libraries onto one run -- over 60 isolates were sequenced for the whole study. With increasing cluster density and the new HiSeq instrument, one could imagine 2-5 fold (or perhaps greater) packing being practical; i.e. the entire study might fit on one run.

The library prep method sounds potentially automatable -- shearing on the covaris instrument, cleanup using a 96 well plate system, end repair, removal of small (<150nt) fragments with size exclusion beads, A-tailing, another 150nt filtering by beads, adapter ligation, another 150nt filtering, PCR to introduce the multiplexing tags, another filtering for <150nt, quantitation and then pooling. Sequencing was "only" 36nt single end, with an average of 80Mb, Alignment to the reference genome was by ssaha (a somewhat curious choice, but perhaps now they'd use BWA) and SNP calling with ssaha_pileup; non-mapping reads were assembled with velvet and did identify novel mobile element insertions. According to GenomeWeb, the estimated cost was about $320 per sample. That's probably just a reagents cost, but gives a ballpark figure.

Existing typing methods either look at SNPs or specific sequence repeats, and while these often work they sometimes give conflicting information and other times lack the power to resolve closely related isolates. Having high resolution is important for teasing apart the history of an outbreak -- correlating patient isolates with samples obtained from the environment (such as hospital floors & such).

Phylogenetic analysis using SNPs in the "core genome" showed a strong pattern of geographical clustering -- but with some key exceptions, suggesting intercontinental leaps of the bug.

Could such an approach become routine for infection monitoring? A fully-loaded cost might be closer to $20K per experiment or higher. With appropriate budgeting, this can be balanced against the cost of treating an expanding number of patients and providing expensive support (not to mention the human misery involved). Full genome sequencing might also not always be necessary; targeted sequencing could potentially allow packing even more samples onto each run. Targeted sequencing by PCR might also enable eliding the culturing step. Alternatively, cheaper (and faster; this is still a multi-day Illumina run) sequencers might be used. And, of course, this can easily be expanded to other infectious diseases with important public health implications. For those that are expensive or slow to grow, PCR would be particularly appropriate.

It is also worth noting that we're only about 15 years since the first bacterial genome was sequenced. Now, the thought of doing hundreds a week is not at all daunting. Resequencing a known bug is clearly bioinformatically less of a challenge, but still how far we've come!



Simon R. Harris, Edward J. Feil, Matthew T. G. Holden, Michael A. Quail, Emma K. Nickerson, Narisara Chantratita, Susana Gardete, Ana Tavares, Nick Day, Jodi A. Lindsay, Jonathan D. Edgeworth, Hermínia de Lencastre, Julian Parkhill, Sharon J. Peacock, & Stephen D. Bentley (2010). Evolution of MRSA During Hospital Transmission and Intercontinental Spread Science, 327 (5964), 469-474 : 10.1126/science.1182395

I'm definitely not volunteering for sample collection on this project

My post yesterday was successful at narrowing down the identity of the mystery creature to a spider of the genus Araneus. Another victory for crowd sourcing! It also points out the value of questioning your assumptions & conclusions. My initial thought on looking at the photos was that the body plan looked spider-like and details of the head and abdomen sometimes pointed me that way -- but then the lack of eight legs convinced me it must be an insect (I thought I could count six in some photos).

One of the commenters addressed my mystery vine with a suggestion that underscored a key detail I inadvertantly left out. The vine raised welts on my legs when it grabbed me -- not in some exaggerated sense, but it definitely had some sort of fine projections (trichomes?) which were not what I'd call thorns, but which stuck to me (and my clothes) like velcro & left behind the small red welts.

The commenter asked if it could have been poison ivy, and I must confess a chuckle. I know that stuff! Boy do I! I've gotten that rash on most of both legs at one point, and all over my neck and arms another time. Numerous cases on my hands over time. It's definitely a very different rash -- much slower to come on, much more itchy with huge welts and very slow to disappear. My clinging plant's rash was short lived.

Poison ivy is easy to identify too. It's truly simple. First, you can start with the old saw "leaves of three, let it be". But, a lot of plants have leaves divided into three parts. Some are yummy: raspberries and strawberries. Others are pretty: columbines. And far more. Also, sometimes it's hard to count -- what else could explain the common confusion of virginia creeper (5-7 divisions) with poison ivy.

Some other key points which make identification simple:
Habitat: Woods, fields, lawns, gardens, roadsides, parking lot edges. Haven't seen it grow in standing water, but I wouldn't rule it out
Size: Tiny plantlets; vines climbing trees for 10+ meters
Habit: Individual plantlets, low growing weed, low growing shrub, climbing vine
Color: Generally dark green, except when not. Red in fall, except when not
Sun: Deep shade to complete sun

Now, this is the pattern where I grew up; my parents joke that it is the county flower. Here in Massachusetts, I don't see quite as much of it and it is very patchy. Wet areas are favorites, but there are also huge stands in non-wet areas. Landward faces of beach dunes are a spot to really watch out for it; Cape Cod is seriously infested.

While it causes humans great angst, poison ivy berries are an important wildlife food source. Indeed, at our previous house I had to be vigilant for sprouts along the flyways into my bird feeder. I've never seen Bambi or Thumper covered with ugly red welts; I'm not sure of the actual taxonomic range of the reaction to the poison (urushiol). Could it really be restricted to humans?

So, here you have a plant which thrives in many ecological niches, is important ecologically, a modest to major pest (inhalation of urushiol is quite dangerous & a hazard for wildfire fighters), an interesting secondary metabolites, and is related to at least two economically important plants (mangoes and cashews, which should be eaten with care by persons with high sensitivity to urushiol). Sounds like a good target for genome sequencing!

Green Krittah

As a youth, I was fortunate enough to spend four summers working as a camp counselor. Three of those were spent in the Nature Department, performing all sorts of environmental education functions (one year I taught archery).

One of my enjoyable duties was to wander the wilds of the camp looking for interesting living organisms to put in our terraria and aquaria, a job I relished -- though the campers could often top me for interesting (I never could find a stick insect, but we rarely lacked for one).

During one of those forays, most likely in my favorite sport of hand-catching of frogs, I stumbled onto a patch of a plant I did not recognize. I still remember it rather well -- perhaps because of the small itchy welts it raised on my unprotected legs. No, not nettles (we had plenty of those, and I often stumbled into them when focused on froggy prey), but rather a vining plant with light green triangular leaves about 5 cm on a side. Indeed, the leaves were nearly equilateral.

So, I took a sample back and poured through our various guides. Now, in an ideal world you would have a guide to every living plant expected in that corner of southeastern Pennsylvania -- which might be a tad tricky as we were on the edge of an unusual geologic formation (the Serpentine Barrens) which has unusual flora. But in general, such general plant guides are hard (or impossible) to come by. What I did have was a great book of trees -- but this wasn't a tree. I had several good wildflower books -- but I saw no blooms. I couldn't find it in the edible wild plant books -- so it was neither edible nor likely to be mistaken for one (rule #1 of wild foraging: never EVER collect "wild parsnips" -- if you're wrong they're probably one of several species which will kill you; if you are right and handle them incorrectly the books say you'll get a vicious rash).

But, being a bit stubborn, I didn't give up -- I kicked the problem upstairs. Partly, this was curiosity -- and partly dreams of being the discoverer of some exotic invader. I mailed a carefully selected sample of the plant along with a description to the county agricultural agent. At the end of the summer, I contacted him (I think by dropping in on his office). He was polite -- but politely stumped as well. So much for the experts.

I'm remembering this & relating it because I've come into a similar situation. My father, who is no slouch in the natural world department, spotted this "green krittah" (as he has named it) on his car last summer. Not recognizing it, and wanting to preserve it (plus, he is an inveterate shutterbug), he shot many closeups of it (the red bar, if I remember correctly, is about 5 mm) -- indeed, being ever the one to document his work he shot the below picture of his photo setup (the krittah is that tiny spot on the car). He even ran it past my cousin the retired entomologist, but he too protested overspecialization -- if it wasn't a pest for the U.S. Navy, he wouldn't know it. At our holiday celebration he asked if I recognized it. I didn't, but given this day of the Internet & my routine use of search tools, surely I could get an answer?


Surely not (so far). I've tried various image-based searches (which were uniformly awful). I've tried searching various descriptions. No luck. Most maddening was a very poetic description on a question-and-answer site, seemingly my krittah but far more imaginative than I would have ever cooked up: "What kind of spider has a lady's face marking it's back? Name spider lady's face markings and red or yellow almond shaped eyes?". Unfortunately, the answer given is a mixture of non sequitur and incoherence -- plus I'm pretty much certain this krittah has 6 legs, not 8. Attempts to wade through image searches were hindered by too few images per gallery and far too few completely useless photos -- of entomologists, of VWs, of spy gear & rock bands & other stuff. I've even tried one online "submit your bug" sort of site, but heard nothing.

I've also tried various insect guides online. The ones I have found are based on the time-tested scheme of dichotomous keys. Each step in the key is a simple binary question, and based on the answer you go to one of two other steps in the key. A great system -- except when it isn't. For one thing, I discovered at least one bit of entomological terminology I didn't know -- so I checked both branches. That isn't too bad -- but suppose I hit more? Or, suppose I answer incorrectly -- or am not sure. It took a lot of looking at Dad's fine photos to absolutely convince myself that the subject has only 6 legs (insect) and not 8 (mite or spider). It also doesn't help that some features (such as the spots on the back) appear differently in different photos. More seriously, the keys I found almost immediately are clearly assuming you are staring at a mature insect -- if you are looking at some sort of larvae they will be completely useless. So perhaps I'm looking at an immature form -- and the key will not help any. In any case, the terminal leaves of the keys I found were woefully underpopulated.



What I would wish for now is a modern automated sketch artist slash photo array. It would ask me questions and I could answer each one yes, no or maybe -- and even it I answered yes it wouldn't rule anything out. With each question the photo array would update -- and I could also say "more like that photo" or "nothing like that photo".

Of course, Dad could have sacrificed the sample for what might seem the obvious approach -- DNA knows all, DNA tells all. That would have nailed it (much as some high school students recently used DNA barcoding to find a new cockroach in their midst), but I think neither of us would want to sacrifice something so beautiful out of pure curiosity (if confirmed to be something awful on the other hand, neither of us would hesitate). Alas, in the mid 1980's I didn't think that way, so I don't have a sample of my vine for further analysis.


If anyone recognizes the bug -- or my vine -- please leave a comment. I'm still curious about both.


(01 Feb 2010 -- corrected size marker to 5 mm, per correspondence with photographer)

The Array Killers?

Illumina announced their new HiSeq 2000 instrument today. There are some great summaries at Genetic Future and PolitGenomics; read both for the whole scoop. Perhaps just as jaw dropping as some of the operating statistics on the new beast is the fact that Beijing Genome Institute has already ordered 128 of them. Yow! That's (back-of-envelope) around $100M in instruments which will consume >$30M/year in reagents. I wish I had that budget!

Illumina's own website touts not only the cost (reagents only) of $10K per human genome, but also that this works out to 200 gene expression profiles per run at $200/profile. That implies multiplexing, as there are 32 lanes on the machine (16 lanes x 2 flow cells -- or is it 32 lanes per flowcell? I'm still trying to figure this out based on the note that it images the flowcell both from the top and bottom). That also implies being able to generate high resolution copy number profiles -- which need about 0.1X coverage given published reports for similar cost.

But it's not just Illumina. If a Helicos run is $10K and it has >50 channels, then that would also suggest around $200/sample to do copy number analysis. I've heard some wild rumors about what some goosed Polonators can do now.

The one devil in trying to do lots of profiles is that means making that many libraries, which is the step that everyone still groans about (particularly my vendors!). Beckman Coulter just announced an automated instrument, but it sounds like it's not a huge step forward. Of course, on the Helicos there really isn't much to do -- it's the amplification based systems that need size selection, which is one major bottleneck.

But, once the library throughput question is solved it would seem that arrays are going to be in big trouble. Of course, all of the numbers above ignore the instrument acquisition costs, which are substantial. Array costs may still be under these numbers, which for really big studies will add up. On the other hand, from what I've seen in the literature the sequencer-based info is always superior to array based -- better dynamic range, higher resolution for copy number breakpoints. Will 2010 be the year that the high density array market goes into a tailspin?

Illumina, of course, has both bases covered. Agilent has a big toe in the sequencing field, though by supplying tools around the space. But there's one obvious big array player so far MIA from the next generation sequencing space. That would seem to be a risky trajectory to continue, by anyone's metrix...

There's Plenty of Room at the Bottom

Friday's Wall Street Journal had a piece in the back opinion section (which has items about culture & religion and similar stuff) discussing Richard Feynman's famous 1959 talk "There's Plenty of Room at the Bottom". This talk is frequently cited as a seminal moment -- perhaps the first proposition -- of nanotechnology. But, it turns out that when surveyed many practitioners in the field claim not to have been influenced by it and often to have never read it. The article pretty much concludes that Feynman's role in the field is mostly promoted by those who promote the field and extreme visions of it.

Now, by coincidence I'm in the middle of a Feynman kick. I first encountered him in the summer of 1985 when his "as told to" book "Surely You're Joking Mr. Feynman" was my hammock reading. The next year he would become a truly national figure with his carefully planned science demonstration as part of the Challenger disaster commission. Other than recently watching Infinity, which focuses around his doomed marriage (his wife would die of TB) & the Manhattan project. Somehow, that pushed me to finally read James Gleick's biography "Genius" and now I'm crunching through "Six Easy Pieces" (a book based largely on Feynman's famous physics lecture set for undergraduates), with the actual lectures checked out as well for stuffing on my audio player. I'll burn out soon (this is a common pattern), but will gain much from it.

I had never actually read the talk before, just summaries in the various books, but luckily it is available on-line -- and makes great reading. Feynman gave the talk at the American Physical Society meeting, and apparently nobody knew what he would say -- some thought the talk would be about the physics job market! Instead, he sketched out a lot of crazy ideas that nobody had proposed before -- how small a machine could one build? How tiny could you write? Could you make small machines which could make even smaller machines and so on and so forth? He even put up two $1000 prizes:

It is my intention to offer a prize of $1,000 to the first guy who can take the information on the page of a book and put it on an area 1/25,000 smaller in linear scale in such manner that it can be read by an electron microscope.

And I want to offer another prize---if I can figure out how to phrase it so that I don't get into a mess of arguments about definitions---of another $1,000 to the first guy who makes an operating electric motor---a rotating electric motor which can be controlled from the outside and, not counting the lead-in wires, is only 1/64 inch cube.

The first prize wasn't claimed until the 1980's, but a string of cranks streamed in to claim the second one -- bringing in various toy motors. Gleick describes Feynman's eyes as "glazed over" when yet another person came in to claim the motor prize -- and an "uh oh" when the guy pulled out a microscope. It turned out that by very patient work it was possible to use very conventional technology to wind a motor that small -- and Feynman hadn't actually set aside money for the prize!

Feynman's relationship to nanotechnology is reminiscent of Mendel's to genetics. Mendel did amazing work, decades ahead of his time. He documented things carefully, but his publication strategy (a combination of obscure regional journals and sending his works to various libraries & famous scientists) failed in his lifetime. Only after three different groups rediscovered his work -- after finding much the same results -- was Mendel started on the road to scientific iconhood. Clearly, Mendel did not influence those who rediscovered him and if his work were still buried in rare book rooms, we would have a similar understanding of genetics to what we have today. Yet, we refer to genetics as "Mendelian" (and "non-Mendelian").

I hope nanotechnologists give Feynman a similar respect. Perhaps some of the terms describing his role are hyperbole ("spiritual founder"), but he clearly articulated both some of the challenges that would be encountered (for example, that issues of lubrication & friction at these scales would be quite different) and why we needed to address them. For example, he pointed out that the computer technology of the day (vacuum tubes) would place inherent performance limits on computers -- simply because the speed of light would limit the speed of information transfer across a macroscopic computer complex. He also pointed out that the then-current transistor technology looked like a dead end, as the entire world's supply of germanium would be insufficient. But, unlike naysayers he pointed out that these were problems to solve, and that he didn't know if they really would be problems.

One last thought -- many of the proponents of synthetic biology point out that biology has come up with wonderfully compact machines that we should either copy or harness. And who first articulated this concept? I don't know for sure, but I now propose that 1959 is the year to beat

The biological example of writing information on a small scale has inspired me to think of something that should be possible. Biology is not simply writing information; it is doing something about it. A biological system can be exceedingly small. Many of the cells are very tiny, but they are very active; they manufacture various substances; they walk around; they wiggle; and they do all kinds of marvelous things---all on a very small scale. Also, they store information. Consider the possibility that we too can make a thing very small which does what we want---that we can manufacture an object that maneuvers at that level!

So if the nanotechnologists don't want to call their field Feynmanian, I propose that synthetic biology be renamed such!

On Being a Scientific Zebra

I got a phone call today from someone asking permission to suggest me as a reviewer of a manuscript this person was about to submit. For future reference, if it's similar to anything I've blogged about, I'd be happy to be a referee. I generally get my reviews in on time (though near deadline), a practice I have gotten much better about since getting a "your review is late" note from a Nobelist -- not the sort of person you want to get on the wrong side of.

I end up reviewing a half dozen or so papers a year, from a handful of journals. NAR has used me a few times & I have some former colleagues who are editors are a few of the PLoS journals. There's also one journal of which I'm on the Editorial Board, Briefings in Bioinformatics (anyone who wishes to write a review is welcome to leave contact info in a comment here which I won't pass through). I'll confess that until recently I hadn't done much for that journal, but now I'm actually trying to put together a special issue on second generation sequencing (and if anyone wants to submit a review on the subject by the end of next month, contact me).

I generally like reviewing. Good writing was always valued in my family, and my parents were always happy to proof my writings when I was at home. This space doesn't see that level of attention -- it is deliberately a bit of fire-and-forget. A review I'm currently writing is now undergoing nearly daily revision; some parts are quite stable but others undergo major revision each time I look at them. Eventually it will stabilize or I'll just hit my deadline.

There's two times when I'm not satisfied with my reviews. The worst is when I realize near the deadline that I've agreed to review a paper where I'm uncomfortable with my expertise for a lot of the material. Of course, if I'd actually read the whole thing on first receipt I'd save myself from this. You generally agree to review these after seeing only the abstract, so I suppose I could put in my report "The abstract is poorly written, as on reading it I thought I'd understand the material but on reading the material I find I don't", but I'm not quite that crazy.

The other unsatisifying case is when I'm uneasy with the paper but can't put my finger on why. Typically, I end up writing a bunch of comments which nibble around the edges of the paper, but that isn't really helpful to anyone.

I also tend to be a little unsatisfied when I get to review very good papers, because there isn't much to say. I generally end up wishing for some further extension (and commenting that it is unfair to ask for it), but beyond that what can you say? If the paper is truly good, you really don't have much to do. A good paper once inflicted a most cruel case of writer's block on me -- it was an early paper reporting a large (in those days) human DNA sequence, I we were invited to write a News & Views on it -- and I couldn't come up with anything satisfying and missed the opportunity.

That leaves the most satisfying reviews -- when a paper is quite bad. This isn't meant to be cruel, but these are the papers you can really dig into. In most cases, there is a core of something interesting, but often either the paper is horridly organized and/or there are gaping holes in it. It can be fun to take someone's manuscript, figure out how you would rearrange & re-plan it, and then write out a description of that. I try to avoid going into copy editor mode, but some manuscripts are so error-ridden it's impossible to resist. Would it really be helpful to the author if I didn't? One subject I do try to be sensitive to is the issue of authors being stuck writing in English when it is not their first language -- given that I can hardly read any other language (a fact plain and simple; I'm not proud of it) it would be unfair of me to demand Strunk&White prose. But, it is critical that the paper actually be understandable. One recent paper used a word repeatedly in a manner that made no sense to me -- presumably this was a regionalism of the authors'.

I once reviewed a complete mess of a paper and ended up writing a manuscript-length review of it. In my mind, I constructed a scenario of a very junior student, perhaps even an undergraduate, who had eagerly done a lot of work with very little (or very poor) supervision from a faculty member. The paper was poorly organized as it was, and many of the key analyses had either been badly done or not done at all. Still, I didn't want to squash that enthusiasm and so I wrote that long report. I don't know if they ever rewrote it.

I can get very focused on the details. Visualization is important to me, so I will hammer on graphs that don't fit my Tufte-ean tastes or poorly written figure legends. Missing supplemental material (or non-functioning websites, for the NAR website issue) send my blood pressure skyrocketing.

I wouldn't want to edit manuscripts full time, but I wouldn't mind a slightly heavier load. So if you are an author or an editor, I reiterate that I'm willing to review papers on computational biology, synthetic biology, genomics and similar. I'd love to review more papers on the sorts of topics I work on now -- such as cancer genomics -- than the overhang from my distant past -- a lot of review requests are based on my Ph.D. thesis work!

Length matters!

I was looking through part of my collection of papers using Illumina sequencing and discovered an unpleasant surprise: more than one does not seem to state the read length used in the experiment. While to some this may seem trivial, I had a couple of interests. First, it's useful for estimating what can be done with the technology, and second since read lengths have been increasing it is an interesting guesstimate of when an experiment was done. Of course, there are lots of reasons to carefully pick read length -- the shorter the length, the sooner the instrument can be turned over to another experiment. Indeed, a recent paper estimates that for RNA-Seq IF you know all the transcript isoforms then 20-25 nucleotides is quite sufficient and you are interested in measuring transcript levels (they didn't, for example, discuss the ideal length for mutation/SNP discovery). Of course, that's a whopping "IF", particularly for the sorts of things I'm interested in.

Now in some cases you can back-estimate the read length using the given statistics on numbers of mapped reads and total mapped nucleotides, though I'm not even sure these numbers are reliably showing up in papers. I'm sure to some authors & reviewers they are tedious numbers of little use, but I disagree. Actually, I'd love to see each paper (in the supplementary materials) show their error statistics by read position, because this is something I think would be interesting to see the evolution of. Plus, any lab not routinely monitoring this plot is foolish -- not only would a change show important quality control information, but it also serves as an important reminder to consider the quality in how you are using the data. It's particularly surprising that the manufacturers do not have such plots prominently displayed on their website, though of course those would be suspected of being cherry-picked. One I did see from a platform supplier had a horribly chosen (or perhaps deviously chosen) scale for the Y-axis, so that the interesting information was so compressed as to be nearly useless.

I should have a chance in the very near future to take a dose of my own prescription. On writing this, it occurs to me that I am unaware of widely-available software to generate the position-specific mismatch data for such plots. I guess I just gave myself an action item!

Nano Anglerfish or Feejee Mermaids?

A few months ago I blogged enthusiastically about a paper in Science describing an approach to deorphan enzymes in parallel. Two anonymous commenters were quite derisive, claiming the chemistry for generating labeled metabolites in the paper impossible. Now Science's editor Bruce Alberts has published an expression of concern, which cites worries over the chemistry as well as the failure of the authors to post promised supporting data to their website and changing stories as to how the work was done.

The missing supporting data hits a raw nerve. I've been frustrated on more than one occasion whilst reviewing a paper that I couldn't access their supplementary data, and have certainly encountered this as a reader as well. I've sometimes meekly protested as a reviewer; in the future I resolve to consider this automatic grounds for "needs major revision". Even if the mistake is honest, it means day considered important is unavailable for consideration. Given modern publications with data which is either too large to print or simply incompatible with paper, "supplementary" data is frequently either central to the paper or certainly just off center.

This controversy also underscores a challenge for many papers which I have faced as a reviewer. To be quite honest, I'm utterly unqualified to judge the chemistry in this paper -- but feel quite qualified to judge many of the biological aspects. I have received for review papers with this same dilemma; parts I can critique and parts I can't. The real danger is if the editor inadvertantly picks reviewers who all share the same blind spot. Of course, in an ideal world a paper would always go to reviewers capable of vetting all parts of it, but with many multidisciplinary papers that is unlikely to happen. However, it also suggests a rethink of the standard practice of assigning three reviewers per paper -- perhaps each topic area should be covered by three qualified reviewers (of course, the reviewers would need to honestly declare this -- and not at review deadline time when it is too late to find supplementary reviewers!).

But, it is a mistake to think that peer review can ever be a perfect filter on the literature. It just isn't practical to go over every bit of data with a fine toothed comb. A current example illustrates this: a researcher has been accused of faking multiple protein structures. While some suspicion was raised when other structures of the same molecule didn't agree, the smoking gun is that the structures have systematic errors in how the atoms are packed. Is any reviewer of a structure paper really going to check all the atomic packing details? At some point, the best defense against scientific error & misconduct is to allow the entire world to scrutinize the work.

One of my professors in grad school had us first year students go through a memorable exercise. The papers assigned one week were in utter conflict with each other. We spent the entire discussion time trying to finesse how they could both be right -- what was different about the experimental procedures and how issues of experiment timing might explain the discrepancies. At the end, we asked what the resolution was, and was told "It's simple -- the one paper is a fraud". Once we knew this, we went back and couldn't believe we had believed anything -- nothing in the paper really supported its key conclusion. How had we been so blind before? A final coda to this is that the fraudulent paper is the notorious uniparental mouse paper -- and of course cloning of mice turns out to actually be possible. Not, of course, by the methods originally published and indeed at that time (mid 1970s) it would be well nigh impossible to actually prove that a mouse was cloned.

With that in mind, I will continue to blog here about papers I don't fully understand. That is one bit of personal benefit for me -- by exposing my thoughts to the world I invite criticism and will sometimes be shown the errors in my thinking. It never hurts to be reminded that skepticism is always useful, but I'll still take the risk of occasionally being suckered by P.T. Barnum, Ph.D.. This is, after all, a blog and not a scientific journal. It's meant to be a bit noisy and occasionally wrong -- I'll just try to keep the mean on the side of being correct.

A Doublet of Solid Tumor Genomes

Nature this week published two papers describing the complete sequencing of a cancer cell line (small cell lung cancer (SCLC) NCI-H209 and melanoma COLO-829) each along with a "normal" cell line from the same individual. I'll confess a certain degree of disappointment at first as these papers are not rich in the information of greatest interest to me, but they have grown on me. Plus, it's rather churlish to complain when I have nothing comparable to offer myself.

Both papers have a good deal of similar structure, perhaps because their author lists share a lot of overlap, including the same first author. However, technically they are quite different. The melanoma sequencing used the Illumina GAII, generating 2x75 paired end reads supplemented with 50x2 paired end reads from 3-4Kb inserts, whereas the SCLC paper used 2x25 mate pair SOLiD libraries with inserts between 400 and 3000 bp.

The papers have estimates of the false positive and false negative rates for the detection of various mutations, in comparison to Sanger data. For single base pair substitutions on the Illumina platform in the melanoma sample, 88% of previously known variants were found and 97% of a sample of 470 newly found variants confirmed by Sanger. However, on small insertion/deletion (indel) there was both less data and much less success. Only one small deletion was previously known, a 2 base deletion which is key to the biology. This was not found by the automated alignment and analysis, though reads containing this indel could be found in the data. A sample of 182 small indels were checked by Sanger and only 36% were confirmed. On large rearrangements, 75% of those tested confirmed by PCR.

The statistics for the SOLiD data in SCLC were comparable. 76% of previously known single nucleotide variants were found and 97% of newly found variants confirmed by Sanger. Two small indels were previously known and neither was found and conversely only 25% of predicted indels confirmed by Sanger. 100% of large rearrangements tested by PCR validated. So overall, both platforms do well for detecting rearrangements and substitutions and are very weak for small indels.

The overall mutation hauls were large, after filtering out variants found in the normal cell line. 22,910 substitutions for the SCLC line and 33,345 in the melanoma line. Both of these samples reflect serious environmental abuse; melanomas often arise from sun exposure and the particular cancer morphology the SCLC line is derived from is characteristic of smokers (the smoking history of the patient was unknown). Both lines showed mutation spectra in agreement with what is previously known about these environmental insults. 92% of C>T single substitutions occured at the second base of a pyrimidne dimers (CC or CT sequences). CC>TT double substitutions were also skewed in this manner. CpG dinucleotides are also to be hotspots and showed elevated mutation frequencies. Transcription-coupled repair repairs the transcribed strand more efficiently than the non-transcribed strand, and in concordance with this in transcribed regions there was nearly a 2:1 bias of C>T changes on the non-transcribed strand. However, the authors state (but I still haven't quite figured out the logic) that transcription-coupled repair can account for only 1/3 of the bias and suggest that another mechanism, previously suspected but not characterized, is at work. One final consequence of transcription-coupled repair is that the more expressed a gene is in COLO-829, the lower its mutational burden. A bias of mutations towards the 3' end of transcribed regions was also observed, perhaps because 5' ends are transcribed at higher levels (due to abortive transcription). A transcribed-strand bias was also seen in G>T mutations, which may be oxidative damage.

An additional angle on mutations in the COLO-829 melanoma line is offered by the observation of copy-neutral loss of heterozygosity (LOH) in some regions. In other words, one copy of a chromosome was lost but then replaced by a duplicate of the remaining copy. This analysis is enabled by having the sequence of the normal DNA to identify germline heterozygosity. Interestingly, in these regions heterzyogous mutations outnumber homozygous ones, marking that these substitutions occurred after the reduplication event. 82% of C>T mutations in these regions show the hallmarks of being early mutations, suggesting they occured late, perhaps after the melanoma metastasized and was therefore removed from ultraviolet exposure.

In a similar manner, there is a rich amount of information in the SCLC mutational data. I'll skip over a bunch to hit the evidence for a novel transcription-coupled repair pathway that operates on both strands. The key point is that highly expressed genes had lower mutation rates on both strands than less expressed genes. A>G mutations showed a bias for the transcribed strand whereas G>A mutations occured equally on each strand.

Now, I'll confess I don't generally get excited about looking a mutation spectra. A lot of this has been published before, though these papers offer a particulary rich and low-bias look. What I'm most interested in are recurrent mutations and rearrangements that may be driving the cancer, particularly if they suggest therapeutic interventions. The melanoma line contained two missense mutations in the gene SPDEF, which has been associated with multiple solid tumors. A truncating stop mutation was found by sequencing SPDEF out of 48 additional tumors. A missense change was found in a metalloprotease (MMP28) which has previously been observed to be mutated in melanoma. Another missense mutation was found in agene which may play a role in ultraviolet repair (though it has been implicated in other processes), suggesting a tumor suppressor role. The sequencing results confirmed two out of three known driver mutations in COLO-829: the V600E activating mutation in kinase BRAF and deletion of the tumor suppressor PTEN. As noted above, the know 2 bp deletion in CDKN2A was not found through the automated process.

The SCLC sample has a few candidates for interestingly mutated genes. A fusion gene in which one partner (CREBBP) has been seen in leukemia gene fusions was found. An intragenic tandem duplication within the chromatin remodelling gene CHD7 was found which should generate an in-frame duplication of exons. Another SCLC cell line (NCI-H2171) was previously known to have a fusion gene involving CHD7. Screening of 63 other SCLC cell lines identified another (LU-135) with internal exon copy number alterations. Lu-135 was further explored by mate pair sequencing witha 3-4Kb library, which identified a breakpoint involving CHD7. Expression analysis showed high expression levels of CHD7 in both LU-135 and NCI-H2171 and a general higher expression of CHD7 in SCLC lines than non-small cell lung cancer lines and other tumor cell lines. An interesting twist is that the fusion partner in NCI-H2171 abd KY-135 is a non-coding RNA gene called PVT1 -- which is thought to be a transcriptional target of the oncogene MYC. MYC is amplified in both these cell lines, suggesting multiple biological mechanisms resulting in high expression of CHD7. It would seem reasonable to expect some high profile functional studies of CHD7 in the not too distant future.

For functional point mutations, the natural place to look is at coding regions and splice junctions, as here we have the strongest models for ranking the likelihood that a mutation will have a biological effect. In the SCLC paper an effort was made to push this a bit further and look for mutations that might affect transcription factor binding sites. One candidate was found but not further explored.

In general, this last point underlines what I believe will be different about subsequent papers. Looking mostly at a single cancer sample, one is limited at one can be inferred. The mutational spectrum work is something which a single tumor can illustrate in detail, and such in depth analyses will probably be significant parts of the first tumor sequencing paper for each tumor type, particularly other types with strong environmental or genetic mutational components. But, in terms of learnign what make cancers tick and how we can interfere with that, the real need is to find recurrent targets of mutation. Various cancer genome centers have been promising a few hundred tumors sequenced over the next year. Already at the recent ASH meeting (which I did not attend), there were over a half dozen presentations or posters on whole genome or exome sequencing of leukemias, lymphomas and myelomas -- the first ripples of the tsunami to come. But, the raw cost of targeted sequencing remains at most a 10th of the cost of an entire genome. The complete set of mutations found in either one of these papers could have been packed onto a single oligo based capture scheme and certainly a high-priority subset could be amplified by PCR without breaking the bank on oligos. I would expect that in the near future tumor sequencing papers will check their mutations and rearrangements on validation panels of at least 50 and preferable hundreds of samples (though assembling such sample collections is definitely not trivial). This will allow the estimation of the population frequency of those mutations which may recur at the level of 5-10% or more. With luck, some of those will suggest pharmacologic interventions which can be tested for their ability to improve patients' lives.


Pleasance, E., Stephens, P., O’Meara, S., McBride, D., Meynert, A., Jones, D., Lin, M., Beare, D., Lau, K., Greenman, C., Varela, I., Nik-Zainal, S., Davies, H., Ordoñez, G., Mudie, L., Latimer, C., Edkins, S., Stebbings, L., Chen, L., Jia, M., Leroy, C., Marshall, J., Menzies, A., Butler, A., Teague, J., Mangion, J., Sun, Y., McLaughlin, S., Peckham, H., Tsung, E., Costa, G., Lee, C., Minna, J., Gazdar, A., Birney, E., Rhodes, M., McKernan, K., Stratton, M., Futreal, P., & Campbell, P. (2009). A small-cell lung cancer genome with complex signatures of tobacco exposure Nature DOI: 10.1038/nature08629

Pleasance, E., Cheetham, R., Stephens, P., McBride, D., Humphray, S., Greenman, C., Varela, I., Lin, M., Ordóñez, G., Bignell, G., Ye, K., Alipaz, J., Bauer, M., Beare, D., Butler, A., Carter, R., Chen, L., Cox, A., Edkins, S., Kokko-Gonzales, P., Gormley, N., Grocock, R., Haudenschild, C., Hims, M., James, T., Jia, M., Kingsbury, Z., Leroy, C., Marshall, J., Menzies, A., Mudie, L., Ning, Z., Royce, T., Schulz-Trieglaff, O., Spiridou, A., Stebbings, L., Szajkowski, L., Teague, J., Williamson, D., Chin, L., Ross, M., Campbell, P., Bentley, D., Futreal, P., & Stratton, M. (2009). A comprehensive catalogue of somatic mutations from a human cancer genome Nature DOI: 10.1038/nature08658