I have a species that has a 4-5% variation between alleles and each RNA-seq sample has several hundreds individuals. I am mapping against a template from another, single individual that has a much more complete transcriptome assembly.
Currently, I am using bowtie + RSEM, but the way bowtie works seems sub-optimal for this dataset as a lot of reads have a few mismatches in the seed compared with reference, so reads are not really mapping that well (65% or so) despite playing around with more lenient parameter choices in bowtie, such as decreasing seed length, increasing mismatches, allowing more backtracking etc. Mapping with e. g. BWA with default parameters restores a sizable amount of this decreased mapping, so the issue seems to be with bowtie.
Are there any alternatives to bowtie that can be combined with either RSEM (by e.g. removing gapped alignments?) or any other similar program that handles multi-mapping reads? If not, are there are alternatives that might work better for this kind of dataset? Like if we give up the ability to accurately handle multi-mapping reads, what other options are there that might be suitable for datasets like this one?