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Big data in genomics: The $1k genome has arrived

nature.com

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Re: Big data in genomics: The $1k genome has arrived

#31

Post by adenadel »

jerven wrote: post #24
adenadel wrote: post #10

Earlier quoted context omitted.

People say this all the time, but with some of the most common applications of high throughput sequencing there are very good canned solutions (using open source software) that you can pay for. DNAnexus, Seven Bridges, and Illumina BaseSpace all provide cloud storage and analysis. Unless you are doing a custom prep for your sequencing one of these probably has an analysis solution for you.

Analysis is custom if you are doing genomics right at this time. Either you are doing something pre-clinical, and you better understand what you are doing i.e. analysis in a black box is not ok. Or you are not dealing with human stuff at all and then there is no analysis in a box on the market. There are tools with GUI's but they don't help that much if you don't understand the underlying systems.

I don't agree with you. I would argue that Genomics England is doing genomics right with the 100k genomes project and they certainly aren't doing custom analysis. Their pipeline is standard through variant calling and annotation. The WGS pipelines are pretty well established at this time. What you do with your VCFs is what can be customized. There is no reason for a small lab to write their own aligner or variant caller. The most that many do is write wrappers to create "customized pipelines", which is really just reinventing the wheel.

EDIT: I want to clarify my mindset. In the environment that I work in there are three stages of analysis: primary, secondary, and tertiary. We mostly ignore the tertiary stage (I'm not actually doing science). Primary analysis is on instrument (base calling), secondary analysis takes you through variant calls and annotation, and tertiary analysis is where the real science happens. When I'm talking about canned analyses I'm talking about what I refer to as secondary analysis. Tertiary analysis is a very hard problem and will often require custom solutions. In my mind I'm separating bioinformatic analysis (secondary analysis) with interpretation (tertiary analysis).

Re: Big data in genomics: The $1k genome has arrived

#32

Post by jerven »

gbveiuwlbiu wrote: post #29
jerven wrote: post #26

Earlier quoted context omitted.

If for 50% of the genes (human) we have no idea of function, then its hard to determine at this point what is a loss-of-function mutation in the first place. Source: I am the lead web developer for uniprot.org, and I deal with lack of real info daily as does everyone in the Swiss-Prot team.

Actually, can I contact you @uniprot.org with more questions?

http://www.isb-sib.ch/fp/people_search.php?cn=jerven%20tjall...

Re: Big data in genomics: The $1k genome has arrived

#33

Post by jerven »

gbveiuwlbiu wrote: post #28
jerven wrote: post #26

Earlier quoted context omitted.

If for 50% of the genes (human) we have no idea of function, then its hard to determine at this point what is a loss-of-function mutation in the first place. Source: I am the lead web developer for uniprot.org, and I deal with lack of real info daily as does everyone in the Swiss-Prot team.

Apart from data privacy and formal restrictions on sharing, what would you say the reason for this lack of public data is? Am I right to say that the bandwidth and storage needed to upload whole genomes is prohibitive?

We collectively just don't know. Not even in the cutting edge literature. Humanity, only has a basic understanding of what the biochemical role of many proteins is, and due too that also a similar idea of what the genes role is in the rather complicated system of making a functioning human.

Bandwidth and storage are infrastructure issues that could be solved with enough money. In biology we are still lacking good technical solutions to do the chemistry and even something "simple" like getting a protein crystalized so we can determine its 3D structure is not trivial or cheap yet.

Fast genome and RNA sequencing are massive improvements and really help. But the basic understanding of what all those genes, regulatory and other parts do is still relatively primitive.

We often "complain" that we wish that people stopped sequencing and went back to do more biochemistry for functional characterization instead ;)

Re: Big data in genomics: The $1k genome has arrived

#34

Post by jerven »

adenadel wrote: post #31
jerven wrote: post #24

Earlier quoted context omitted.

Analysis is custom if you are doing genomics right at this time. Either you are doing something pre-clinical, and you better understand what you are doing i.e. analysis in a black box is not ok. Or you are not dealing with human stuff at all and then there is no analysis in a box on the market. There are tools with GUI's but they don't help that much if you don't understand the underlying systems.

I don't agree with you. I would argue that Genomics England is doing genomics right with the 100k genomes project and they certainly aren't doing custom analysis. Their pipeline is standard through variant calling and annotation. The WGS pipelines are pretty well established at this time. What you do with your VCFs is what can be customized. There is no reason for a small lab to write their own aligner or variant cal…

Getting the variants out is where the analysis starts. What does the variation mean, which ones correlate with a detectable phenotype. Which ones are affecting regulatory systems.

Genomics England is brilliant because it has good access to excellent NHS data which it will use to do a lot of analysis. Which its not getting off the shelf but a lot of talented clincal+bio+informaticians are working hard on that.

Or in other words getting the variants to the human genome reference is just the start of the analysis not the end.

i.e. I am more worried about the end of the pipeline than the contents of them.

I suspected as much on the 1,2,3 level. But even 2 is not really fixed yet. There are great off the shelf tools but even the concept of variation calling against a reference linear genome is not perfect yet especially with the alt_loci business. Which driving the variation graph work in the GA4GH projects.

Re: Big data in genomics: The $1k genome has arrived

#35

Post by aheilbut »

mjpuser wrote: post #8

A quick search for public genome data led me here http://www.completegenomics.com/public-data/69-Genomes/ . I wonder if there will be a time when you can search millions through a rich UI, comparing your own genome with others, etc.

Have a look at ExAC, which has 60k exomes and a pretty snazzy UI: http://exac.broadinstitute.org

Re: Big data in genomics: The $1k genome has arrived

#36

Post by rcthompson »

searine wrote: post #27
adenadel wrote: post #10

Earlier quoted context omitted.

People say this all the time, but with some of the most common applications of high throughput sequencing there are very good canned solutions (using open source software) that you can pay for. DNAnexus, Seven Bridges, and Illumina BaseSpace all provide cloud storage and analysis. Unless you are doing a custom prep for your sequencing one of these probably has an analysis solution for you.

>DNAnexus, Seven Bridges, and Illumina BaseSpace all provide cloud storage and analysis. "Analysis". There is a difference between meaningful analysis which answers long standing questions of biology, and running a program which calls SNPs and spits out genome stats. The hard part is knowing what questions to ask, and how to interpret the answers when you get them. That takes years of training to develop a critical m…

This is absolutely true. I've done differential expression analysis on many RNA-seq and microarray datasets, and despite the fact that the basic pipeline for such analysis is quite straightforward and easily automated, every analysis is asking different biological questions and has different confounding factors that need to be addressed, so the analysis of each dataset ends up being quite different from all the others.

Re: Big data in genomics: The $1k genome has arrived

#37

Post by jeserac »

adenadel wrote: post #31
jerven wrote: post #24

Earlier quoted context omitted.

Analysis is custom if you are doing genomics right at this time. Either you are doing something pre-clinical, and you better understand what you are doing i.e. analysis in a black box is not ok. Or you are not dealing with human stuff at all and then there is no analysis in a box on the market. There are tools with GUI's but they don't help that much if you don't understand the underlying systems.

I don't agree with you. I would argue that Genomics England is doing genomics right with the 100k genomes project and they certainly aren't doing custom analysis. Their pipeline is standard through variant calling and annotation. The WGS pipelines are pretty well established at this time. What you do with your VCFs is what can be customized. There is no reason for a small lab to write their own aligner or variant cal…

It's not strictly true that we're not doing custom analysis. It must also be understood that while Genomics England have some standard pipelines running we are also providing the capability for partners via our GeCIP programme to work within a walled garden to do custom analysis within domains for rare diseases and cancers. You can read more about that here: http://www.genomicsengland.co.uk/about-gecip/

Re: Big data in genomics: The $1k genome has arrived

#38

Post by epistasis »

BKPetkov wrote: post #13
epistasis wrote: post #5

Earlier quoted context omitted.

That sounds quite feasible, though it hasn't really been worth the effort until we have quite a few more genomes. And typically extra information about the variant (is it in a gene, does it change a protein, etc.) so that extra lookups aren't required during a scan. There are typically 4-6 million variants discovered through this method of genome sequencing in a normal genome. A simple variant consists of a genome co…

Interesting - wouldn't you need to have access to "the rest" of the genomes that you are comparing against? In other words, wouldn't you need to keep that ~100 GB from the newly sequenced genome in temporary storage while comparing against the rest of the database stored somewhere in the cloud, before then condensing the new genome into a variant file?

Typically we don't look at other genomes while we find the variants in an individual genome. Each genome is analyzed against the "reference" human genome, which is an average of 10 individuals. This forms the coordinate basis that is shared for everyone else.

Pretty much all genomic data uses a reference genome as the basis. This is versioned, and has a bug tracker, etc., for various regions that have been difficult to assemble.

The flow is:

1. BCL (scans of the glass slide) 2. FASTQ (individual short reads and quality scores, unsorted and in random order) 3. BAM (individual short reads aligned to the reference genome) 4. VCF (the "diff" vs. the reference genome)

All of this can be done with <10GB of reference data and code, where the reference data is the current human genome, a burrows-wheeler transform of the human genome, gene locations, and dbSNP (the database of common human variation).

Re: Big data in genomics: The $1k genome has arrived

#39

Post by mschuster91 »

What I find most worrying with mass-collecting fully sequenced DNA (or DNA at all) is that it is inevitable that law enforcement, military, spy agencies or politicians will want access to the data.

Given enough sequenced DNA and a free-fall of sequencing costs, it might be feasible in the future to find out who exactly took a dump in public just due to DNA. And well, the Israelis are already doing this with dog dumps (http://uk.reuters.com/article/2008/09/16/uk-israel-dogs-idUK...), so applying the same tech to humans is not far away.

Fucking scary if you ask me.

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