Hi, Mike.  See my couple of comments below....

Sean

On Tue, Apr 5, 2011 at 2:22 PM, Mike Dufault <dufau...@yahoo.com> wrote:

> Hi all,
>
>
>
> Like many people on this e-mail chain, I have been looking for advice on
> how to process Exome data. Below, I have described in detail what I have
> done with the hope of getting some clarification. Hopefully it will be
> helpful to many of us!
>
>
>
> I have SureSelect Exome captured data. The data was delivered to me as two
> separate files (/1) & (/2). Each file has ~33 million reads; 7.2 GB each. I
> am looking for SNPs from a family with cancer. Eventually I plan to compare
> the date from multiple members of the same family to find a related disease
> SNP.
>
>
>
> Below is the workflow that I used to process my data. I adapted it from the
> Screencast titles: "Mapping Illumina Reads: Paired Ends Example." I used all
> of the same default parameters as in the screencast.
>
>
>
> At the end of step 13, I had ~4,700,000 SNPs. This seemed like a lot so in
> step 14, I filtered on column 7 (c7) which I believe is the Quality SNP
> value. I set the filter as C7>=1 to remove all of the 0 (zero) values for
> Quality SNP. I figured that if they have a value of zero, they must not be
> real SNPs. This left me with ~180,000 SNPs.
>
>
>
> 1: Get Data: Illumina 1.3+ file (/1)
>
> 2: Get Data: Illumina 1.3+ file (/2)
>
> 3: FASTQ Groomer on data 1
>
> 4: FASTQ Groomer on data 2
>
> 5: FASTQ Summary Statistics on data 3
>
> 6: FASTQ Summary Statistics on data 4
>
> 7: Box plot on data 5
>
> 8: Box plot on data 6
>
> 9: Map with Bowtie for Illumina on data 4 and data 3: mapped reads
>

This might not be the best choice, as bowtie does not allow gapped
alignment.  See here for a discussion of indels and SNV calling:

http://bioinformatics.oxfordjournals.org/content/26/6/722.long

You will probably also want to consider local realignment around indels and
potentially quality score recalibration.


> 10: Filter Sam on data 9
>
> 11: SAM-to-BAM on data 10: converted to BAM
>
> 12: Generate pileup on data 11: converted pileup
>
> 13: Filter pileup on data 12
>
> 14: Filter data on 13 (c7>=1)
>
> 15: Sort on data 15 (C7; descending order)
>
>
>
> First, if anyone has ideas on how to improve the workflow, I would be open
> to suggestions; especially from people experienced with Galaxy.
>
>
>
> Second, I am concerned that many/most of the SNPs are known. Should I
> filter my data against the known SNPdb? If so, how can I do this in Galaxy
> (in Bowtie?)
>

Keep in mind that, depending on the version of dbSNP, there are many
cancer-associated SNPs contaminating the database.


>
> Third, as suggested in the screencast, I did not trim or filter my FASTQ
> Groomed data because I was interested in SNPs and I could filter on Quality
> later in the workflow. Would implementing a filtering step on phred quality
> (~20) at this step save me the step of filtering later on. Currently it
> takes multiple hours (~16) to process the data from start to finish, would
> filtering at this step reduce the amount of time that it takes to process my
> data? Presumably, there would be less data to process. I do this on the AWS
> Cloud and time is money!
>
>
>

Adding a gapped alignment algorithm, indel realignment, and quality
recalibration can easily increase this time to a couple of days per sample.


> Fifth, when using Galaxy on the AWS cloud, does adding additional cores or
> adding High CPU ( or both) shorten the time to process the data? When I set
> up extra cores, it appeared that some of them are idle and I don't want to
> pay for idle cores. If anyone could share information on how best to manage
> the cloud, it would be appreciated.
>
>
>
> Finally, what is the difference between “stopping” an instance and
> “terminating” an instance on the cloud? Would I still get charged by AWS if
> I just stop an instance? Any clarification in this area would also be much
> appreciated. Again, time is money!
>
> I hope this helps many of us!
>
>
>
> Unfortunatly, I will not be in Pitt to ask these questions in person.
>
>
>
> Thanks in advance!!!
>
>
>
> Mike
>
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