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