Genome Researchers Have Too Much Data

An anonymous reader writes "The NY Times reports, 'The field of genomics is caught in a data deluge. DNA sequencing is becoming faster and cheaper at a pace far outstripping Moore's law. The result is that the ability to determine DNA sequences is starting to outrun the ability of researchers to store, transmit and especially to analyze the data. Now, it costs more to analyze a genome than to sequence a genome. There is now so much data, researchers cannot keep it all.' One researcher says, 'We are going to have to come up with really clever ways to throw away data so we can see new stuff.'"

Comments

as a genome researcher

[ecorona]

As a genome researcher, I'd like to point out that I, for one, do not have nearly enough genome data. I simply need about 512GB of RAM on a computer with a hard drive that is about 100x faster than my current SSD, and processing power about 1000x cheaper. Right now, I bite the bullet and carefully construct data structures and implement all sorts of tricks make the most out of the RAM I do have, minimize how much I have to use a hard drive, and extract every bit of performance available out of my 8 core machine. I wait around and eventually get things done, but my research would go way faster and be more sophisticated if I didn't have these hardware limitations.

Re:Wrong problem

[TooMuchToDo]

Genomes have *a lot* of redundant data across multiple genomes. It's not hard to do de-duplication and compression when you're storing multiple genomes in the same storage system.

Wikipedia seems to agree with me:

http://en.wikipedia.org/wiki/Human_genome#Information_content

The 2.9 billion base pairs of the haploid human genome correspond to a maximum of about 725 megabytes of data, since every base pair can be coded by 2 bits. Since individual genomes vary by less than 1% from each other, they can be losslessly compressed to roughly 4 megabytes.

Disclaimer: I have worked on genome data storage and analysis projects.

Re:Wrong problem

[GAATTC]

Nope - the bottleneck is largely analysis. While the volume of the data is sometimes annoying in terms of not being able to attach whole data files to emails (19GB for a single 100bp flow cell lane from a HiSeq2000) it is not an intellectually hard problem to solve and it really doesn't contribute significantly to the cost of doing these experiments (compared to people's salaries). The intellectually hard problem has nothing to do with data storage. As the article states "The result is that the ability to determine DNA sequences is starting to outrun the ability of researchers to store, transmit and especially to analyze the data.". We just finished up generating and annotating a de novo transcriptome (sequences of all of the expressed genes in an organism without a reference genome). Sequencing took 5 days and cost ~$1600. Analysis is going on 4 months and has taken at least one man year at this point and there is still plenty of analysis to go.

Drops in NGS Costs Outpacing Storage Costs

[Anonymous Coward]

The big problem is that the dramatic decreases in sequencing costs driven by next-gen sequencing (in particular the Illumina HiSeq 2000, which produces in excess of 2TB of raw data per run) have outpaced the decreases in storage costs. We're getting to the point where storing the data is going to be more expensive than sequencing it. I'm a grad student working in a lab with 2 of the HiSeqs (thank you HHMI!) and our 300TB HP Extreme Storage array (not exactly "extreme" in our eyes) is barely keeping up (on top of the problems were having with datacenter space, power, and cooling).

I'll reference an earlier /. post about this:
http://science.slashdot.org/story/11/03/06/1533249/graphs-show-costs-of-dna-sequencing-falling-fast

There are some solutions to the storage problems such as Goby (http://campagnelab.org/software/goby/) but those require additional compute time, and we're already stressing our compute cluster as is. Solutions like "the cloud(!)" don't help much when you 10TB of data to transfer just to start the analysis - the connectivity just isn't there.

Re:Last post

[edremy]

The error rate is too high- data copying using that medium and the best available (naturally derived) technology makes an error roughly every 100,000 bases. There are existing correction routines, but far too much data is damaged on copy, even given the highly redundant coding tables.

Then again, it could be worse: you could use the single strand formulation. Error rates are far higher. This turns out to be a surprisingly effective strategy for organisms using it, although less so for the rest of us.