RazerS 3


Motivation: During the last years NGS sequencing has become a key technology for many applications in the biomedical sciences. Throughput continues to increase and new protocols provide longer reads than currently available. In almost all applications, read mapping is a first step. Hence, it is crucial to have algorithms and implementations that perform fast, with high sensitivity, and are able to deal with long reads and a large absolute number of indels.

Results: RazerS is a read mapping program with adjustable sensitivity based on counting q-grams. In this work we propose the successor RazerS 3 which now supports shared-memory parallelism, an additional seed-based filter with adjustable sensitivity, a much faster, banded version of the Myers’ bit-vector algorithm for verification, memory saving measures and support for the SAM output format. This leads to a much improved performance for mapping reads, in particular long reads with many errors. We extensively compare RazerS 3 with other popular read mappers and show that its results are often superior to them in terms of sensitivity while exhibiting practical and often competetive run times. In addition, RazerS 3 works without a precomputed index.

Main Features:

  • import of FASTA/FASTQ read and genome files
  • 5 output formats (including SAM)
  • reads can be of arbitrary length
  • supports Hamming and edit distance read mapping with configurable error rates
  • supports paired-end read mapping
  • configurable and predictable sensitivity (runtime/sensitivity tradeoff)
  • key improvements (compared to RazerS):
    • multicore parallelization
    • additional pigeonhole filter optimized for low error-rates with controllable sensitivity
    • banded Myers’ algorithm for verification
    • full sensitivity under the definition given in Rabema
    • SAM output

Availability and Implementation: Source code and binaries are freely available for download at http://www.seqan.de/projects/razers. RazerS 3 is implemented in C++ and OpenMP under a GPL license using the SeqAn library and supports Linux, Mac OS X, and Windows.


  • Download the binaries
  • View the source code and README on GitHub
  • The previous version of RazerS can be found here
  • Check out our newer, faster read aligner Yara

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