Any ideas for optimizing tophat settings when mapping long reads (100bp RNA-seq single) to a reference genome?
So far we map around 30% of the reads using default settings, which is much lower than we have commonly observed.
Previously we were able to map more than 70% of 50bp reads to the same reference genome. We have also tried further quality filtering without raising the mapping rate.
Maybe another tool is better for long reads, but we would like to run cufflinks on the tophat output for differential expression/splicing.
Any thoughts?
So far we map around 30% of the reads using default settings, which is much lower than we have commonly observed.
Previously we were able to map more than 70% of 50bp reads to the same reference genome. We have also tried further quality filtering without raising the mapping rate.
Maybe another tool is better for long reads, but we would like to run cufflinks on the tophat output for differential expression/splicing.
Any thoughts?
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