The industry standard for aligning short reads seems to be bwa-mem. However, in my tests I have seen that using bwa backtrack (bwa-aln + bwa-sampe + bwa-samse) performs better. It is slightly slower, but gives significantly better results in terms of both sensitivity and specificity. I have tested it using the genome in a bottle data and public samples (NA12878 and NA12877 among others) and found that backtrack consistently outperformed bwa-mem.
So why is bwa-mem the standard? Am I wrong and other tests have shown the opposite? I don't really see how since I tested using the most common datasets and validation data. Is it that the slight increase in efficiency outweighs the decrease in performance?
The only other explanation I can see is that bwa backtrack is designed specifically for Illumina reads and all my tests have been on Illumina data. Is it just that bwa-mem is "sequencer agnostic"? So that we can use the same algorithm irrespective of what sequencing platform is used? In that case, it makes sense to use backtrack for if we only deal with Illumina data and mem if we can have different sequencers. But, if so, seeing as Illumina is so widespread, why isn't backtrack used more often on Illumina data? I feel I must be missing something.