BWASW doesn't complete

sgroenewold

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Join Date: Jan 2013

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BWASW doesn't complete

01-21-2013, 09:48 AM

I'm attempting to sequence 250bp reads from an Illumina MiSeq using BWA 0.6.2 on a 64-bit CentOS 6.2 virtual machine.

I previously had the bwa index step freeze on me, but found some advice that 6 GB was needed. After bumping my VM up to 6 GB of RAM that indexing step does indeed now complete.

But now as I attempt the alignment with bwa bwasw, I get the following output and then the machine hangs and doesn't get any further.

[/mnt/hgfs/Data/NGS/MiSeq1203UnCap]$ bwa bwasw ../hg19ReferenceGenome/hg19_reference_genome.fa NA07357_S1_L001_R1_001.fastq > BWARead1.sam

[bsw2_aln] read 50511 sequences/pairs (10000002 bp)...
[bsw2_aln] read 50570 sequences/pairs (10000029 bp)...
[bsw2_aln] read 50581 sequences/pairs (10000158 bp)...
[bsw2_aln] read 50621 sequences/pairs (10000011 bp)...
[bsw2_aln] read 50575 sequences/pairs (10000169 bp)...
[bsw2_aln] read 50671 sequences/pairs (10000039 bp)...
[bsw2_aln] read 50641 sequences/pairs (10000033 bp)...
[bsw2_aln] read 50572 sequences/pairs (10000114 bp)...
[bsw2_aln] read 50611 sequences/pairs (10000082 bp)...
[bsw2_aln] read 50555 sequences/pairs (10000053 bp)...
[bsw2_aln] read 50590 sequences/pairs (10000170 bp)...
[bsw2_aln] read 50576 sequences/pairs (10000111 bp)...
[bsw2_aln] read 50411 sequences/pairs (10000204 bp)...

I've given my VM access to 200+ GB of hard drive space on /usr. But I'm starting this process from a shared /mnt directory, where the data files are located. I don't know if I need to point bwa to do its work in a certain directory, or if that's potentially the source of my problem.

The file I tried to align is the first read of a paired-end read. But since BWASW doesn't use paired-end reads, I'm assuming aligning those fastq files one at a time into their own respective sam files is the proper procedure.
Tags: bwa, bwasw

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Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing

by GATTACAT

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.
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