Hello all,
I have RNA-Seq data from a species A for which I don't have a reference genome. I would like to map this data on the closest available genome from species B.
I have read this post and I am planning to use hisat2 or STAR. Does any of you have recommandations regarding the parameters to set for mapping, given that I don't have a reference genome? I would expect that I should use this program with less "stringency" than if I could map on the species A genome. For instance, the default parameter in STAR is --outFilterMismatchNmax 10: should I set it to 20? 30? In hisat2, it is --score-min L,0,-0.2.
My reads are 100nt.
In addition, could you recommend any lecture related to this question?
Many thanks for your answers!
2 answers
Hi tlorin,
I use STAR to do this frequently with the parameter --outFilterMismatchNmax 8. You can check the alignment percentage in the Log.final.out file and adjust your mismatch threshold accordingly. You may face the issue of reads aligning to a lot of different locations and can filer your bam file by the mapq score to address this. Be aware that unaligned reads may be placed in the "% of reads unmapped: too short" category even if they are unaligned because of mismatches. Dobin said something about this in one of the threads for the STAR google group.
That is the post I went by. That threshold works for my dataset for the most part because I have a lot of reads. I'd say try different values and see how your results differ.
BBMap will align RNA-seq data to genomes with a substantially lower identity than most other aligners, particularly other splice-aware aligners. You might add the flags "maxindel=200k minid=0.7" to increase alignment rate in this case.
Thanks Brian! If I add maxindel=200k minid=0.7 do I risk mapping to spurious locations or will the mapping report the best unique hit (so, the homologous sequence in my case)?
With looser requirements, you can have a greater risk of mapping to spurious locations, but in that case they will have a lower mapq score. BBMap will always map reads to the best-matching location. So, "minid=0.9" and "minid=0.8" and "minid=0.7" will all report the same alignment for a given read, if the best alignment has, say, 95% identity. But if the best alignment only has 85% identity, then "minid=0.9" won't report anything.
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Here you have a very good review about how to do NGS analysis with non-model organisms. It has a part on RNA-seq and how to deal with situations were you do not have a reference genomeNext-generation biology: Sequencing and data analysis approaches for non-model organisms