BWA-MEM - Heng Li's Answer to Bowtie2

BWA-MEM - Heng Li's Answer to Bowtie2

BWA-MEM paper is now available from arxiv.

Aligning sequence reads, clone sequences and assembly contigs with BWA- MEM

BWA-MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end-to-end alignments, supports paired-end reads and performs split alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases. For short-read mapping, BWA-MEM shows better performance than several state-of-art read aligners to date. Availability and implementation: BWA-MEM is implemented as a component of BWA, which is available at

It has nice comparison with other alignment algorithms.

We evaluated the performance of BWA-MEM on simulated data together with NovoAlign (, GEM, Bowtie2, Cushaw2, BWA-SW and BWA (Figure 1). On accuracy, NovoAlign is the best. BWA-MEM is close to NovoAlign for PE reads and is comparable to GEM and Cushaw2 for SE. On speed, BWA-MEM is similar to GEM and Bowtie2 for this data set, but is about 6 times as fast as Bowtie2 for a 650bp long-read data set.

Written by M. //