I have two bam files (H3K56ac ChIP and input) that I want to use to score regions (promoters) that I have defined in a bed file. I've been trying to use seqMINER to do this, but i'm pretty sure there is an easier method? Suggestions for a good tool to do this would be much appreciated. Thanks.
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Dear Charlesli,
You may try the featureCounts function included in the bioconductor package Rsubread - http://bioconductor.org/packages/rel.../Rsubread.html
Cheers,
Wei
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BEDOPS bedmap is useful for this task. This application takes elements that overlap other elements in UCSC BED files and applies operations to them (counting them, listing their IDs, calculating statistics from score column data, etc.).
For this task, we can use bedmap --count to show the number of reads that overlap some regions of interest (promoters, distal elements, etc.).
First, prepare your regions-of-interest with BEDOPS sort-bed:
$ sort-bed unsortedRegions.bed > sortedRegions.bed
This preparation step only has to be done once for any input to BEDOPS tools. This step allows BEDOPS tools to operate faster and with a lower memory profile than alternatives. Sorting only has to be done once, as BEDOPS tools export data in sorted order.
Next, let's assume your reads are in BAM format. We can use BEDOPS bam2bed to convert this data to sorted BED, and we then pipe it to bedmap to apply the --count operation:
$ bam2bed < reads.bam \
| bedmap --echo --count sortedRegions.bed - \
> answer.bed
The file answer.bed contains results in the following format:
[ region-1 ] | [ count of reads over region-1 ]
[ region-2 ] | [ count of reads over region-2 ]
...
[ region-N ] | [ count of reads over region-N ]
If your regions are promoters, for example, then this result tells you how many reads are contained within each promoter's genomic coordinates, which you specify in sortedRegions.bed.
The default overlap criterion is one base. In other words, one base of overlap between the read and region is sufficient for inclusion. You can adjust this overlap parameter with other options, if you need more stringency.
Note that this result is a sorted BED file, as well, and can be piped to other BEDOPS tools (or other utilities which process BED data).Last edited by AlexReynolds; 04-29-2013, 03:18 PM.
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by GATTACATLove 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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by SEQadmin2
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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