3 ms·
In general it's a (computationally) hard problem to restitch a genome together. Even today, when you 'get your genome sequenced' you are not getting a full read
by jfarlow 10y ago
In general it's a (computationally) hard problem to restitch a genome together. Even today, when you 'get your genome sequenced' you are not getting a full read-through of your entire genome's data.
Imagine you want to reconstruct the data on two RAIDs that are mostly, but importantly not exactly, mirrors of each other. Each RAID has 23 drives. Each drive has ~1Gb or so of data. And much of the data is not only mirrored between the two RAIDs, but is also mirrored between the 23 drives - and many of that mirrored data is 'off by 1' in very important ways (both 'must', and 'must not' scenarios). Further some of the data contains very long sections of highly repetitive data. And some of the data is mechanically biased to be harder to read than others.
You must now reconstruct those two RAIDs with single-bit accuracy - as a single bit-flip in certain sections determines whether or not you get cancer. The data you are given to do the reconstruction is a 200Gb single column CSV file with each row being 12 bytes of data.
Go.
- bozoUser 10y agohmm I don`t think so I fathom the complete complexity of the process but with so many powerful GPU`s out there, is there a possibility of reconstruction in a matter of days if not hours?
- JangoSteve 10y agoYes, it depends on the size of the sequencing panel that was done (i.e. a targeted panel for a specific gene or set of genes versus whole exome versus whole genome). But even for whole exome, you're talking a few hours or faster depending on hardware. In addition to our company, Genomenon (which seeks to speed up the interpretation time required to analyze the data _after_ it's been computationally aligned and annotated), I'm also friends with another startup down the street, called Parabricks, which seeks to speed up this alignment process (aka secondary analysis) even further.
- virtuabhi 10y agoThe steps in the genomic analysis pipelines are not always embarrassingly parallel. The degree of parallelization cannot be increased to an arbitary number. In addition, the parallel executions can give slightly different results when compared with the serial output, so we need "safe" data-partitioning schemes and rigorous error control. If you are further interested in parallelization schemes for genomic pipelines, please have a look at our paper on the strengths and limitations of big data technology for genomic analysis (published last week) - https://people.cs.umass.edu/~aroy/sigmod17-roy.pdf https://people.cs.umass.edu/~aroy/sigmod17-roy.pdf