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Old 02-05-2010, 04:45 PM   #1
nilshomer
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Default Sequence Alignment Quality Control

As manual processing of sequencing data becomes impossible, it would be good to identify sequencing quality metrics that could/should be calculated/plotted within a sequencing pipeline.

What programs do you use to check the quality of your sequencing runs? Specifically, what metrics have you found useful pre-alignment, post-alignment, and post-variant-detection? What metrics/plots would you find useful if they were automatically generated?
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Old 02-05-2010, 08:10 PM   #2
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Quote:
Originally Posted by nilshomer View Post
As manual processing of sequencing data becomes impossible, it would be good to identify sequencing quality metrics that could/should be calculated/plotted within a sequencing pipeline.

What programs do you use to check the quality of your sequencing runs? Specifically, what metrics have you found useful pre-alignment, post-alignment, and post-variant-detection? What metrics/plots would you find useful if they were automatically generated?
Here are some that come to my mind:

Pre-alignment:

1. Assuming we have available genotype data on the sample, some quick screening of the reads to find genotype concordance can be useful to avoid sample swaps.

Post-alignment:

1. Basic stats: % mapped uniquely mapped reads, % mapped reads, effective %mapped reads (after removing PCR duplicates), total throughput, effective throughput (after removing duplicates).

2. Error rate plot. For CS, before and after CS corrections.

3. For MP data: insert size distribution plot.

Post variant detection:

1. plot: ref/var coverage distribution.

Nils, which ones are you using on your end? Anyone?
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Old 02-05-2010, 09:04 PM   #3
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Originally Posted by drio View Post
Here are some that come to my mind:

Pre-alignment:

1. Assuming we have available genotype data on the sample, some quick screening of the reads to find genotype concordance can be useful to avoid sample swaps.

Post-alignment:

1. Basic stats: % mapped uniquely mapped reads, % mapped reads, effective %mapped reads (after removing PCR duplicates), total throughput, effective throughput (after removing duplicates).

2. Error rate plot. For CS, before and after CS corrections.

3. For MP data: insert size distribution plot.

Post variant detection:

1. plot: ref/var coverage distribution.

Nils, which ones are you using on your end? Anyone?
I have few those above implemented and more but I want to get a reasonable group of QC metrics before releasing. How would you compare genotypes before alignment? Would you look for specific sequences?
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Old 02-06-2010, 12:53 AM   #4
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Post alignment: compare enrichment / read distributions for different fractions of reads (high quality vs low quality, number of mismatches for reads in ChIP-seq peaks etc). Bin reads by average QV and compare % aligned reads for different aligners at different QV. For comparison between aligners I would also look at differences in coverage over various repeats, what % reads are uniquely placed by only one aligner and how many reads are placed at different positions.
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Old 02-06-2010, 07:24 AM   #5
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Post alignment: compare enrichment / read distributions for different fractions of reads (high quality vs low quality, number of mismatches for reads in ChIP-seq peaks etc). Bin reads by average QV and compare % aligned reads for different aligners at different QV. For comparison between aligners I would also look at differences in coverage over various repeats, what % reads are uniquely placed by only one aligner and how many reads are placed at different positions.
I do not mean to compare aligners, but evaluate the alignment itself.
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