rsuchecki
commented
6 years ago It is a valid point and something we were thinking about. I agree that straightforward benchmarking-based presentation of biokanga will not take us far, but showing that it places simulated reads where they originated from seems like a reasonable starting point.
Some things we discussed with @spriggsy83 and @alexwhan where around the benefits conveyed by features such as PE processing (especially orphaned-ends recovery) or chimeric trimming. For example using biokanga align with chimeric trimming, one could go from raw reads to SNP markers in a single step. This could be one of biokanga's unique selling points for the purposes of publication. The localised haplotype detection might be another.
kit4b
From the proposed paper outlining in 'readme.md' I am unsure as to the justifying rationale(s) used to select specific capabilities of BioKanga for publication. Remember that the core functionalities targeted in the proposed paper are those which were incorporated into BioKanga back in about 2011. I think we have missed the boat on the basic alignment capabilities of BioKanga and the paper should be either a straight methods publication, with no experimental justification, or concentrate on the latest incorporated features such as the localised haplotype detection which clearly differentiates BioKanga from all other competing bioinformatics toolsets.