Minimap2 and the future of BWA
My minimap2 paper has been accepted for publication in Bioinformatics. You can find the latest LaTeX source at OverLeaf or in the tex directory of minimap2. I am intentionally delaying the publication process for personal reasons. It will take a while for you to see the published version at Bioinformatics. I thought to write this blog post when the paper comes out, but there have been a few discussions on minimap2 recently, so I decide to write it now.
I wrote a blog post on long-read alignment with bwa-mem several years ago. In short, bwa-mem was not designed for long reads initially. It works, but not well. When I was developing minimap for read overlapping, I realized approximate mapping could achieve comparable accuracy to bwa-mem at a much faster speed. I didn’t expand minimap to a full-pledge aligner because (1) I knew base-level alignment was going to be very slow and (2) bwa-mem still worked fine. However, both reasons became invalid in the coming years.
In early 2017, Nick Loman et al invented a new protocol to sequence nanopore reads of 100kb in length. Bwa-mem failed miserably on such ultra-long reads – it was not “fine” at all. In addition, not long after I published minimap, Suzuki and Kasahara released minialign. It implements a banded base-level alignment algorithm that is practical for long-read alignment and much faster than the alternatives. These events finally motivated me to develop minimap2.
The status of minimap2
For long reads, minimap2 is a much better mapper than bwa-mem in almost every aspect: it is >50X faster, more accurate, gives better alignment at long gaps and works with ultra-long reads that fail bwa-mem. Minimap2 also goes beyond a typical long-read mapper. It can achieve good full-genome alignment (see the minimap2 paper, section 3.4) and is used by QUAST-LG. Minimap2 can also align high-quality cDNAs and noisy long RNA-seq reads (section 3.2). PacBio has started to consider minimap2 in their Iso-seq pipeline. The feature set and the code base of minimap2 are also fairly stable. I see little reason to use bwa-mem for long reads in future.
The story on short-read alignment is a little complex, though. I did plan to replace bwa-mem with minimap2 on short-read alignment, too. In the minimap2 paper, I showed that minimap2 is 3X as fast as bwa-mem and achieves comparable accuracy to bwa-mem on short variant calling (section 3.3). In the final round of the review, an reviewer still argued that minimap2 wouldn’t work well for short reads. I didn’t think so at the time given that Illumina Inc. has independently evaluated minimap2 and observed that minimap2 is highly competitive. Therefore, I didn’t follow the suggestion of that reviewer.
However, Andrew Carroll at DNAnexus has recently showed me that minimap2 was slower than bwa-mem on two NovaSeq runs at his hand. Part of the reason, I guess, is that the two NovaSeq runs have a little higher error rate, which triggers expensive heuristics in minimap2 more frequently. Furthermore, I also realize that bwa-mem will be better than minimap2 at Hi-C alignment because bwa-mem is more sensitive to short matches. In the end, I admit minimap2 is not ready to replace bwa-mem all around. I owe that reviewer an apology.
Generally, I still think minimap2 is a competitive short-read mapper and I will use it often in my research projects. However, given that the performance of minimap2 is not as consistent as bwa-mem for short reads of varying quality, bwa-mem is still better for production uses, at least before I find a way to improve minimap2.
The future of bwa
Bwa will stay. I am thinking to bring some minimap2 features to bwa-mem, such as fast alignment extension and global alignment. This will make code cleaner and fix a long-existing bug in bwa-mem: a tiny fraction of base-level alignment is suboptimal. Nonetheless, implementing these features will not speed up bwa-mem much because base-level alignment is not the computation bottleneck for short reads. I am also likely to remove the bwa-sw algorithm and issue a deprecation warning when the “pacbio” or the “ont2d” presets are used. In the mean time, several talented developers at Intel Inc. are restructuring bwa-mem for considerable performance boost at no loss of accuracy. I will work with them. If this effort hopefully works out, the end product will become bwa-mem2. All these won’t happen soon, unfortunately.
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