Scalable string similarity search/join with approximate seeds and multiple backtracking
Proceedings of the Joint EDBT/ICDT 2013 Workshops
| Hi-index | 3.84 |
Motivation: During the past years, next-generation 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 insertions and deletions. 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 competitive run times. In addition, RazerS 3 works without a pre-computed index. 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. Contact: david.weese@fu-berlin.de Supplementary information:Supplementary data are available at Bioinformatics online.