Flexible pattern matching in strings: practical on-line search algorithms for texts and biological sequences
Average complexity of exact and approximate multiple string matching
Theoretical Computer Science
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multipattern string matching with q-grams
Journal of Experimental Algorithmics (JEA)
On the complexity of the spaced seeds
Journal of Computer and System Sciences
Bioinformatics
Hardness of optimal spaced seed design
Journal of Computer and System Sciences
Bioinformatics
ZOOM! Zillions of oligos mapped
Bioinformatics
Worst case efficient single and multiple string matching in the RAM model
IWOCA'10 Proceedings of the 21st international conference on Combinatorial algorithms
Seed design framework for mapping SOLiD reads
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Worst-case efficient single and multiple string matching on packed texts in the word-RAM model
Journal of Discrete Algorithms
Fast multiple string matching using streaming SIMD extensions technology
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
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With Next Generation Sequencers, sequence based transcriptomic or epigenomic assays yield millions of short sequence reads that need to be mapped back on a reference genome. The upcoming versions of these sequencers promise even higher sequencing capacities; this may turn the read mapping task into a bottleneck for which alternative pattern matching approaches must be experimented. We present an algorithm and its implementation, called mpscan, which uses a sophisticated filtration scheme to match a set of patterns/reads exactly on a sequence. MPSCAN can search for millions of reads in a single pass through the genome without indexing its sequence. Moreover, we show that MPSCAN offers an optimal average time complexity, which is sublinear in the text length, meaning that it does not need to examine all sequence positions. Comparisons with BLAT-like tools and with six specialised read mapping programs (like BOWTIE or ZOOM) demonstrate that mpscan also is the fastest algorithm in practice for exact matching. Our accuracy and scalability comparisons reveal that some tools are inappropriate for read mapping. Moreover, we provide evidence suggesting that exact matching may be a valuable solution in some read mapping applications. As most read mapping programs somehow rely on exact matching procedures to perform approximate pattern mapping, the filtration scheme we experimented may reveal useful in the design of future algorithms. The absence of genome index gives mpscan its low memory requirement and flexibility that let it run on a desktop computer and avoids a time-consuming genome preprocessing.