Hi,
I've been using Galaxy to analyse rna seq data from mRNA isolated from Hela cells. Like others I have the problem of reads that are multihits. I have put up a workflow for analysis on Galaxy that involves the following steps:
1. Run the data using tophat and allow up to 40 maps per read (default)
2. Use a samtools feature to get rid of all mappings that are not mate paired and in a proper pair.
3. Count up how many times an individual read is in the sam file and remove all read pairs that are not mapped to a unique site, putting them in a separate "multihits" file.
4. Keep all the uniquely mapped proper mate paired hits in a unique hits sam file.
This approach generates more unique hits than asking tophat to throw out reads that do not uniquely map (this may have changed with the latest tophat release - I haven't checked yet). I think (and the tophat guys may correct me on this) this is because tophat may be removing reads where one end is not uniquely mapped but the other is (and therefore only makes sense with one of the mates).
However, whatever tophat does (now or in the future) this approach does have the advantage of telling you where and how big the multihit problem is. My datset has, for example, 18 million unique proper paired reads, 1.3 million that map to two places, a few hundered thousand that map to 3 places and so on down the line.
One problem with multihits is that we may be overestimating some genes by including multihits or conversely underestimating some genes by excluding them. This "Bristol" workflow allows us to at least know if a gene has a problem of being prone to multihits.
I think this approach is useful but I may have missed something or be behind the curve!! Who knows but I thought it might be a useful workflow to start a discussion about what to do with multihit reads.
Cheers
David
I've been using Galaxy to analyse rna seq data from mRNA isolated from Hela cells. Like others I have the problem of reads that are multihits. I have put up a workflow for analysis on Galaxy that involves the following steps:
1. Run the data using tophat and allow up to 40 maps per read (default)
2. Use a samtools feature to get rid of all mappings that are not mate paired and in a proper pair.
3. Count up how many times an individual read is in the sam file and remove all read pairs that are not mapped to a unique site, putting them in a separate "multihits" file.
4. Keep all the uniquely mapped proper mate paired hits in a unique hits sam file.
This approach generates more unique hits than asking tophat to throw out reads that do not uniquely map (this may have changed with the latest tophat release - I haven't checked yet). I think (and the tophat guys may correct me on this) this is because tophat may be removing reads where one end is not uniquely mapped but the other is (and therefore only makes sense with one of the mates).
However, whatever tophat does (now or in the future) this approach does have the advantage of telling you where and how big the multihit problem is. My datset has, for example, 18 million unique proper paired reads, 1.3 million that map to two places, a few hundered thousand that map to 3 places and so on down the line.
One problem with multihits is that we may be overestimating some genes by including multihits or conversely underestimating some genes by excluding them. This "Bristol" workflow allows us to at least know if a gene has a problem of being prone to multihits.
I think this approach is useful but I may have missed something or be behind the curve!! Who knows but I thought it might be a useful workflow to start a discussion about what to do with multihit reads.
Cheers
David
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