Big Data: Astronomical or Genomical?

http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195

by chuckcode • 11 years ago

24 16 11 years ago

journals.plos.org

etrain• 11 years ago

For anyone else confused about the Twitter numbers reported in Table 1, I believe it should read "0.5-15 billion tweets/day" as opposed to "0.5-15 billion tweets/year" on the first line, which is consistent with the claim that Twitter produces 500m tweets/day currently. When you make this adjustment, you recover their annual storage estimates.

Still - this estimate is based on 3KB/tweet, which is probably derived from looking at the raw twitter XML feed - I'd expect this to compress easily down to 10-30x smaller than the author's claimed numbers - more with proper data modeling.

Nevertheless - huge problems to be dealt with in the sciences and video.

meeper16• 11 years ago

Analyzing the source asking about analyzing itself would seem to be connected to the largest data set - Genomics and all the biology, including the brain and conciousness, associated. It's the most complex form of data we can collect and get our hands on so to speak.

collyw5• 11 years ago

What data do we have about consciousness?

chuckcode• 11 years ago

Seems like better representation could save a few orders of magnitude in data size. Humans only differ at about 1/1000 nucleotides so storing just differences rather than all genomic positions should greatly alleviate the issue. In theory you would just have to store less than a megabyte of nucleotides per personal genome. Probably have to get rid of one-off sequencing errors and quality scores at some point as scaling up to planet wide genomic sequencing.

temujin• 11 years ago

Better representations are already used for downstream data analysis and continue to be improved; see e.g. http://arxiv.org/abs/1506.08452 .

However, algorithms for producing variant calls from raw sequencer output are also improving over time, and the way to get the most benefit from them is to save the old raw data so newer algorithms can be applied to them. That's where the storage challenges come into play.

glofish• 11 years ago

Genomic data is storage is very redundant, SRA stores the raw data the way it is collected and that is extremely inefficient.

For example if scientists were to collect data that matched the human genome perfectly they would still store all the data, tens or even hundreds of gigabytes - yet the information content of all that is actually one bit: the data matches perfectly the human genome, nothing more needed to be stored at all.

In addition the current genomic data collection is a "prisoner" of the technology. It produces lots of small "reads" (DNA fragments) instead of a single genomic measurement. Because the data is so fragmented we need to measure the same thing many times over (hundreds even). Hence even more redundancy at least 100 fold.

Now magnify this over tens or hundreds of thousands of samples and naturally we have a problem.

If we could sequence one human genome it would take 3x10^9 bytes --> 2.3 gigabytes.

But instead if we were to store the only the changes relative to the reference representing the human genome would take 5x10^6 bytes --> 4 megabytes. Hardly a big challenge to store.

But here is another thing, why store that data when you could re sequence it? All we need is a faster sequencing methodology and all of a sudden data does not need to be stored only the sample does.

shas3• 11 years ago

Classic compression problem. There is no free lunch in that you'd still need to compute the 'difference', which is a combinatorial search. If you could indeed compute the difference, technically you would throw away the raw data and store only differences. But you'd still need to temporarily have access to the raw data.

collyw5• 11 years ago

Lots of stuff genomics researchers do is inefficient. Generally they store sequence in strings and compare those, when 4 bases can be represented by two bytes. Then you can compare them using bitwise operators, which is very close to the way processors work, and orders of magnitude faster than comparing strings.

jjtheblunt• 11 years ago

I believe you have a typo and meant 2 Bits.

mbreese2• 11 years ago

Sometimes genomics researchers do things inefficiently on purpose. You very rarely want to look to see if two strings are exactly the same. Instead, you usually try to look at how similar the sequences are, taking into account ambiguities or mismatches. In this scenario, storing sequences compressed as 2-bit encoding isn't significantly faster (if it is at all).

GFK_of_xmaspast• 11 years ago

Where "orders of magnitude" here means "four times"? (and one suspects the additional complexity will not do good things to your instruction cache)

ETA: wait, hold on. To compare two pairs of bits, you can do an xor, mask out the bits you care about, compare that against 0, and jump based on the flag. To compare two bytes, you can do an xor, mask out the bits you care about if you're packing them into something larger, compare that against 0, and jump based on the flag. Totally not seeing the advantage here.

tosseraccount• 11 years ago

Note that CRAM format does use the reference genome to compress sequences.

http://www.1000genomes.org/faq/what-are-cram-files

CRAM files are alignment files like BAM files. They represent a compressed version of the alignment. This compression is driven by the reference the sequence data is aligned to.

Benchmarks here : http://www.htslib.org/benchmarks/CRAM.html

Big Data: Astronomical or Genomical?

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