Go Back   SEQanswers > Bioinformatics > Bioinformatics

Similar Threads
Thread Thread Starter Forum Replies Last Post
Indel detection in high coverage amplicons sequenced by MiSeq lpalacios Bioinformatics 2 02-24-2015 07:04 AM
Extremely high Coverage in some regions JMFA Bioinformatics 2 01-11-2013 03:42 AM
False negative variant calling in haploids. Call variants using coverage (not stats)? Genomics101 Bioinformatics 3 06-07-2012 12:29 PM
Variant calling for high-coverage Illumina data dgmacarthur Bioinformatics 19 04-08-2011 07:34 AM
New Paper: High Quality SNP Calling Using Illumina Data at Shallow Coverage nmalhis Bioinformatics 0 03-01-2010 03:40 PM

Thread Tools
Old 02-11-2013, 12:36 PM   #1
Location: UK

Join Date: Nov 2010
Posts: 49
Default Variant calling on custom amplicons with extremely high coverage

I have a hard time obtaining variants from a bam file constructed using amplicon sequencing. When I load bam's on IGV I can see loads of reads (well, there is a limit of how much You can really see but the depth show correct coverage). I have 30.000-1.500.000 reads (identical ones) per each bam file.
I tried to use GATK with specific region bed file (thought that it will speed it up) but it takes forever (already 5h+! for 6 regions of ~250bp).
Tried samtools mpileup (multiple bam's at once) but I cannot find any of known variants (visible under IGV).
Shall I use it one-by-one on each bam (and combine?)?
memento is offline   Reply With Quote
Old 02-11-2013, 04:30 PM   #2
Senior Member
Location: San Diego

Join Date: May 2008
Posts: 912

Sounds like downsampling would make your life easier.

Removing duplicates would be one quick way of accomplishing this. But there are other ways. Picard, for one, can downsample a .bam. You can also do a quick and dirty version yourself, by grepping for reads, say, from one tile.
swbarnes2 is offline   Reply With Quote
Old 02-18-2013, 09:38 PM   #3
Location: Singapore

Join Date: Nov 2010
Posts: 30

I would second the downsampling approach.

The removal of duplicates can in theory and depending on your setup introduce some biases. For example if your looking at subpopulations in viral or bacterial sequencing (i.e. not a 'simple' diploid genome) you might end up with only a handful of reads after duplicate removal, and those will not represent the actual 'allele' frequencies.
me_myself_andI is offline   Reply With Quote

Thread Tools

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is Off

All times are GMT -8. The time now is 10:18 PM.

Powered by vBulletin® Version 3.8.9
Copyright ©2000 - 2021, vBulletin Solutions, Inc.
Single Sign On provided by vBSSO