Earlier this week Life Technologies announced the next revision of their SOLiD platform, SOLiD 4. I don’t have all the details that I had for the Illumina HiSeq 2000, but here is what I do know: the system will produced 100 Gb of alignable sequence data on two slides per 14 day run. The sequence data will be paired-end, 50×35 base reads. Reagent costs for each run will be about $6,000. Since you need about 100 Gb of sequence to sequence a human genome, you’re looking at about $6000 in reagent costs per human genome. They also indicated that capacity for the instrument will increase to 300 Gb per run and the cost for reagents per human genome will be less than $3000 by the end of 2010. In comparison, the Illumina HiSeq 2000 reagent costs will be about $10,000 per human genome at its release with, by my calculations, a path to about $4000 per human genome (I have no idea what the time frame might be to reach the end of that path, but given this announcement by Life, it will likely be aggressive). You have to love the way competition drives down costs. Similar to Illumina’s announcement of a big HiSeq 2000 purchase at its announcement, Life announced that Ignite Institute would acquire 100 SOLiD 4 instruments as part of partnership with Life. Life also announced a major bioinformatics investment program as well as a physician education program through their Foundation.

Update: According to the press release, Ignite is “acquiring”, not purchasing, the instruments in “partnership” with Life. So it appears this is not an outright purchase of a large number of instruments. I have updated the text in the post to be more accurate.

Update: I received some SOLiD 3 number from Nicholas Socci (thanks Nicholas!).

Update2: I received a fuller set of numbers from drd and the SOLiD 3 column is complete (thanks drd!).

By the way, sorry for the extended absence, things have been crazy.

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The SRA is currently accepting 454 data in standard flowgram format (SFF) and Solexa in SRF format. Soon 454 and AB SOLiD will support the SRF format and submissions will commence in that format for those platforms. The SFF format contains the flowgrams (intensity per cycle at each spot), base calls, and base quality values. In other words, the SFF is very similar to the SCF format used for capillary sequencing data (except flowgrams are discrete whereas chromatograms are continuous). Also, NCBI (as recently discussed) has developed their own storage format for massively parallel sequencing data that they will also be accepting as a submission format within the next few months.

So what is an SRF? Well, it is basically just a container format, i.e., what you store in it is up to the implementation. Thus far, SRF has only been implemented for Illumina/Solexa data; so the rest of this post is specific to that platform and the data types that its implementation of the SRF format contains. The Solexa SRF implementation was done largely by James Bonfield at Sanger and is distributed as part of the io_lib package (now distributed separately from the Staden package). I would imagine that the SOLiD implementation will be very similar to the Solexa implementation. The 454 implementation will likely be very similar to the SFF already in wide use.

For the 1000 Genomes pilot projects, the 1000 Genomes Data Collection Center (DCC) is asking that we submit the “raw”, “processed”, and “base” data for each spot. Raw data are the intensity values (int) and noise (nse) values. Processed data are the processed intensity values (sig2) and four-channel quality values (prb). Base data are the base calls (the quality value is gotten from the prb for the called base). This results in about 50 bytes per base for the SRF. Compared to 2 bits per base, the minimum possible for DNA’s four letter alphabet, this is a 200-fold increase. So not only do these instruments generate a lot more data, we are storing more information per base now too. The average submission for an Solexa run is about 100 GB.

Why store all this extra information? Essentially, people do not trust/believe the data at this point. The quality values provided by these pipelines are not as reliable as those generated for capillary sequence data. Some people want the raw data so that they can develop and improve base calling/quality algorithms. Clearly you would not need all the 1000 Genomes data to develop such algorithms (although the technology changes at such a rate that you would likely want some rolling subset of the latest runs). Others want the raw data because they think they may want to go back and re-analyze data when better algorithms become available. For a wide variety of reasons (disk space, computational cost, network bandwidth, keeping pace with newly generated data), I doubt any such massive re-analysis will ever take place.

After the data flow meeting was a meeting of the 1000 Genomes Steering Committee. The day and a half that ensued was filled with a lot of lively discussion. When all was said and done, one thing was clear: there are a lot of questions that need to be answered. The analysis group presented convincing results from simulations that indicated 2× coverage in a large number of individual genomes (Pilot 1) is probably not sufficient to detect the rare variants the project is going after (present in 1-2% of the population). The simulations indicated that the power of the study to detect such variants (at a constant cost, i.e., constant total amount of sequence generated) would be greatly enhanced by sequencing half as many people at 4× coverage. There was no firm decision on how to change the pilot (if at all), but going forward it is likely that some of the individuals in Pilot 1 will be sequenced up to 4× or even 8×. Thus, while the project may be named 1000 Genomes, exactly how many genomes we are going to sequence is yet to be determined.

Another issue that arose was the rapid development of the massively parallel sequencing technologies. These platforms (454 FLX, Illumina Genome Analyzer, and AB SOLiD) increase their throughput, improve their data quality, improve analysis software, etc. several times each year. Such dynamic platforms make the development of tools to analyze their data, e.g., align the data to a reference genome and detect variants, very difficult. The right platforms and tools today may not be the best next month or next year when the main project gets underway. This causes two major needs to come to the fore. First, experimental design will not end when the project starts. The experiment will need to be adjusted as capabilities and capacities change. Second, we will not only have to continually develop and refine tools throughout the project, we will need to develop frameworks to continually test and compare the tools that are available. It’s always fun to hit a moving target.

The meeting also discussed the ethical, legal, and social implications (ELSI) of the project. This discussion largely focused on which populations to sample for the project. Should we deepen our knowledge of individuals of Central European, African, and East Asian ancestry to aid in methodology development? Or should we broaden our knowledge of overall human variation by including fewer individuals from a larger number of populations? To be determined…