Sean, Anton and Jen,
Thanks for all of the suggestions (in separate replies) on how to better
analyze my SelectSure captured Exome data. My original work-flow is below in
the e-mail string.
Based on the suggestions, I plan to change my work-flow by increasing my
quality filter from 20 to 25-30 and increasing my minimum coverage from 3x to
~20x. I will use the Join function to compare the SNPs that are in common with
the samples from two family members to filter (narrow down) what they have in
common, since I am looking for a hereditary disease. Then i will use the
Join function again with the SNPs from build (131) to characterize the SNPs.
Sean suggested realignment around indels and potentially quality score
recalibration. Is that even possible with Galaxy at the moment?
Where in the flow can I perform Indel analysis? Will I need to process my data
separately for SNPs and Indel analysis, or can they be done sequentially in the
same linear work-flow? I am still a little unsure of the best way to hand this.
Please let me know if you have any more suggestions or comments before I
re-launch the analysis later this evening. Once I get a flow that works, I hope
to be able to publish it for everyone to benefit from.
Thanks to the Galaxy team for an outstanding platform and support!
--- On Tue, 4/5/11, Sean Davis <sdav...@mail.nih.gov> wrote:
From: Sean Davis <sdav...@mail.nih.gov>
Subject: Re: [galaxy-user] Analyzing Targeted Resequencing data with Galaxy
To: "Mike Dufault" <dufau...@yahoo.com>
Cc: "galaxy-user" <firstname.lastname@example.org>
Date: Tuesday, April 5, 2011, 4:39 PM
Hi, Mike. See my couple of comments below....
On Tue, Apr 5, 2011 at 2:22 PM, Mike Dufault <dufau...@yahoo.com> wrote:
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:
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!!!
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