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I have got some illumina data in a a merged BAM file for a particular sample and I would like to convert it back to per lane data files (BAM or fastq) so it would be compatible with the analysis pipeline. Any ideas on the best way to do this?
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When I did this a few days ago (with only knowledge of command-line stuff and samtools), here's a rough guide to what I did:- Extract header from BAM file Code:
samtools view -H infile.bam > header.sam
- filter out lanes from BAM file, add header Code:
(cat header.sam; samtools view infile.bam | grep '^DATA...._<lane>_.') > out_lane<lane>.sam
- convert SAM to BAM Code:
samtools view -S out_lane<lane>.sam -b > out_lane<lane>.bam
There's probably some better way, but it would have taken longer for me to find than for me to just do it and deal with the additional overhead of storing temporary SAM files per lane. - Extract header from BAM file
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If the lane info is stored in RG tags you can use this: http://genome.sph.umich.edu/wiki/SplitBamComment
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SplitBam looks ideal, but I can't find a download link for it. Any ideas?
And thanks for the other suggestion too, that makes a lot of sense.
--EDIT--
Actually it looks like there are no @RG in the headers ... Oh Dear.Last edited by danielsbrewer; 09-29-2011, 01:03 AM.Comment
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SplitBam download link?
I am trying to do the exact same thing and I do have @RG tags, but I can not find a download link for SplitBam. Does anyone know how to download this program?
JasonComment
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Originally posted by danielsbrewer View PostGo here and either follow the instructions for retrieving the current development release from the source repository or download the tarball for the latest stable realese (0.1.4). Note that either way you are downloading the entire libStatGen toolkit, which you must then compile. SplitBam is merely one of the programs in the toolkit.Originally posted by jtladner View PostComment
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Originally posted by jtladner View Post
splitBam is part of the bamUtil package, which i found it here https://github.com/Homebrew/homebrew-science and it was very easy to download it.Comment
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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