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Best approach for realignertargetcreator and indelrealigner

steve1980steve1980 Posts: 1Member

Hi,

I am trying to decide between two approaches for performing realignment around indels. I have ~600 samples that have been aligned to a very fragmented draft genome assembly. What is best:
1. take each sample and create a list of targets, followed by realignment on each sample.
2. combine all samples into one large bam file and create a list of targets, followed by realignment on the same large bam file.

Also, would there be any advantages in terms of speed with either approach?

Cheers,

Steve

Answers

  • Geraldine_VdAuweraGeraldine_VdAuwera Posts: 5,978Administrator, GATK Developer admin

    Hi Steve,

    Actually you don't need to combine the samples in a single BAM file to process them together, you can just pass them all as inputs in a list file.

    Realigning them all together is best because then the realignment will be consistent over all of them. That said it can be a lengthy process with a lot of samples, so if you find performance is an issue you can do the target creation on the full list, then realign in batches -- as long as you use the same target intervals file that is completely fine. You can also look into multithreading to speed things up.

    Geraldine Van der Auwera, PhD

  • stechenstechen University of PennsylvaniaPosts: 23Member

    Greetings!

    For using multiple bam files as inputs to RrealignerTargetCreator, I'd used "-I file_1.bam -I file_2.bam -I file_3.bam" in the command. This was with GATK 2.2. Is the syntax the same for the most recent version of GATK as well?

    Thank you! Stephanie

  • Geraldine_VdAuweraGeraldine_VdAuwera Posts: 5,978Administrator, GATK Developer admin

    Hi Stephanie,

    Yes, the syntax is still the same. Keep in mind you can also pass in a list of files in a text file (with .list extension) to make it easier. FYI our Best Practice recommendation now is to realign per lane or per sample only. We have found that multisample realignment, while it never hurts results, yields little benefit compared to its computational cost.

    Geraldine Van der Auwera, PhD

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