(Note: the video above is just an amusing example of a guitar duel, it is not intended in any way as a comment on Vivien’s or my personality. Vivien is great and me… well, you may have a point there. It is also worth noting that Bobby is not actually playing the “Holy Trinity of Rock ‘n’ Roll”, E-A-B. The chords being played are E-G-A E-G-B♭-A E-G-A-G-E. The older among us will recognize that as the same progression as the main riff from Deep Purple’s classic rock anthem Smoke On The Water.)

Over at Informatics Iron, Matthew Dublin states in his summary of the article that I want to bring everyone “back to square one” because I say that the solution to the computing challenges in genomics will likely involve a mixture of internal and external resources. The current reality is that most people are currently using local resources and, as those resources become more and more underpowered compared to their needs, they will extend their workflows to leverage external resources as well. In other words, researchers are not likely to scrap their current computing infrastructures and migrate entirely to the cloud when their computing needs grow beyond their existing resources. Hopefully by the time most people need to spill over into external resources middleware systems will exist that intelligently schedule jobs to appropriate computational resources, internal or external, with a minimal amount of job metadata from bioinformatician submitting the job.

Here’s a video hint for those who do not understand the reference in the title of this post.

So, what other costs should be considered? Well, the types of costs and actual values will depend greatly on your situation. Will you be doing the sequencing or will you be contracting at a core facility or sequencing-as-a-service company? Will you be doing the analysis or relying on a third party? How will you be validating your results? How many people will be working on the project at what percent of their efforts? Will you buy everyone a Pet Rock when the project reaches 1 exabases of sequence?

Here I’ll run through a standard cost calculation for a typical academic sequencing and analysis center to sequence and analyze a human genome. The names and costs have been changed to protect the innocent (this means I chose nice, round numbers that are the right order of magnitude). Why not use real numbers? Read the previous paragraph (I’ll wait …): your cost factors and numbers will not be the same as anyone else’s. So you’re going to have to do the calculation for yourself, not just lift the numbers from this post.

First we can consider the consumables (e.g., flow cells and reagents) costs. Let’s say those are $10,000. Then there is the instrument depreciation. Let’s say the instrument costs $600,000, has an expected life of three years, and can do 40 runs per year. Assuming a straight-line depreciation, the instrument depreciation per run is $5,000 (= $600,000 / (3 × 40)). If the instrument supports two flow cells, you would divide the number in half to get $2,500. Now, the DNA doesn’t just hop on the sequencer by itself. DNA has to be acquired, consents signed and approved by institutional review boards (IRBs), and sequencing libraries have to be made. Let’s say sample acquisition costs $100,000 for 50 samples; that’s $2,000 per sample. Shepherding the project and consents through the IRB takes one full-time employee (FTE) at 10% effort one month. We’ll say the cost of one FTE (salary, benefits, etc.) is $60,000 per year. So getting the project through IRB approval costs $500. If the project is able to use all 50 samples, that’s only $10 per sample! If the consumables and personnel time to make a sequencing library is $200, then the total production cost for sequencing our human genome is $14,710. Wait, I forgot the IT and LIMS support! In this scenario we’ll say that each instrument needs one IT FTE and one LIMS FTE, each at 25% effort ($750). And you need disk space for the data ($1,000, you can cut that in half if you throw away everything but the sequence, qualities, and alignments) and compute time ($100) to run alignments and QC. Add to that 50% overhead charges that your institution takes to cover administration, utilities, lab space, etc. (a company would need to determine each of these costs and add them in rather than this overhead multiplier) and your $10,000 genome costs you nearly $25,000. And you haven’t even called a variant yet.

Speaking of variants, let’s assume you want to call SNPs, indels, and structural variations. The first thing you will have to do is align your reads. Let’s say you are efficient and simply use the alignments from the production QC step. Above we assumed $100 for these alignments, but what goes into that number? First you have to determine an average alignment time per genome. Let’s say 90 Gb of sequence (30× coverage of a human genome) in 2×100 base read pairs takes 1,000 core×hr to align to the human reference genome. If you did this on Amazon EC2 ($0.17/core×hr), it would cost you $170 (plus data transfer and storage costs). If you have your own cluster, you need to amortize the cost of your cluster (compute nodes, racks, networking equipment and cabling, PDUs, etc.) per core×hr, add in the cost of your administrators per core×hr, and utilities or overhead per core×hr to get your cost. When you do that calculation, let’s say you get $0.10 per core×hr, so the alignment costs you $100 (but you already paid it above). Merging the BAM files from each lane’s worth of data and marking duplicates takes 50 hours, costing $5. Calling SNPs and indels (including reassembly) takes 100 hours, costing $10. Detecting structural variation using aberrant read pairs takes 200 hours, costing $20. Annotating all the variants across an entire genome takes 100 hours, costing $10. The disk space for all of this costs you $1,000 (again, you’ll need to calculate a cost per GB factoring storage, racks, switches, servers, personnel, etc. to get this number). Finally, somebody needs to run (or automate) this analysis pipeline. Figure that one analyst and one developer each at 10% effort can accomplish this over the course of two weeks; $480. Add all this up and your analysis with overhead runs you about $2300, or about 10% of the cost of generating the data. Of course, human resequencing for variant detection is not the only application of sequencing data. Other types of analysis, e.g., de novo assembly and metagenomic analysis, can have significantly higher costs per base. For example, in metagenomic analysis you may want to classify reads that do not align to known sequences by aligning them in protein space against a database like NCBI nr. If you generate 10 Gb of sequence per sample and 25% of the read pairs do not align to anything else, you will need to align 12.5 million reads. If you use the most common tool for this sort of alignment, NCBI BLAST+ blastx, it would take over 5,500 core×hr, costing about $550 by itself.

Now that you have your sequence data and list of variants, you are going to need to validate them. There are a lot of different ways to validate variants, e.g., PCR, pool, and sequence or Sequenom, so I am not going to go through a detailed cost calculation. It suffices to say that, depending on the number of variants you want to validate, the cost can rise into the thousands of dollars. Whatever platform you choose, you will need to go through a thorough cost calculation (like the one done above for the original sequencing and analysis). For the sake of this post, which is already too long, we’ll say the validation cost is $2,000.

Finally, somebody has to be running this show. Let’s say project management personnel costs $20,000, or $400 per sample. Put this all together and your $10,000 genome costs about $30,000. In other words, the often quoted consumables number only accounts for about 50% of the total cost (Note: overhead applies to consumables also, so while $10,000 looks like 1/3 of $30,000, it is actually half). Again, none of the numbers I use above are real (but they are in the ball park) and all sequencing and analysis facilities are going to have different contributors to their costs resulting in varying contributions from consumables. However, regardless of the cost contribution of consumables at present, the cost of consumables are projected to fall below $5,000 by the end of this year, and they won’t stop there. As such, it is already meaningless to only quote consumable costs when stating the price of sequencing a genome. By the end of the year, it will be ridiculous.

Update: Clarified Archon X Prize cost accounting.

Today in Nature our recent research studying basal-like breast cancer as compared to normal DNA, a subsequent brain metastasis, and a xenograft derived from the primary tumor (a xenograft is the implantation of a portion of the tumor biopsy into the fatty tissue of an immunodeficient mouse) was published along with an commentary by Joe Gray. Surprisingly, while the xenograft was derived from the primary tumor, its mutational profile had many characteristics similar to that of the brain metastasis. This finding indicates that mutations required for successful transplantation of a tumor into a mouse are perhaps similar to those required for the formation of metastasis (and provide insights into why some tumor types are not prone to metastasis and fail to grow xenografts). The article also represents the first publication of the complete sequence of an African-American female.

You can find news coverage of the article and its findings at KWMU, BusinessWeek, News24, GenomeWeb, and the WU Record (includes video).

The zebra finch reference genome and several companion papers are being published today in Nature and Genome Research. The zebra finch is a model for human vocal development and it is hoped that a better understanding of its genome and the genes involved in learning song can shed light on how humans learn language. These learnings may be helpful to researchers studying diseases that slow language development such as autism. Wes Warren of The Genome Center led the consortium to sequence the reference genome. Coverage of the publications can be found at the BBC, ABC, CBS, NPR/KWMU, The Independent, St. Louis Post-Dispatch, GenomeWeb, and WU Record (includes a video).