PatternHunter: faster and more sensitive homology search. MOTIVATION: Genomics and proteomics studies routinely depend on homology searches based on the strategy of finding short seed matches which are then extended. The exploding genomic data growth presents a dilemma for DNA homology search techniques: increasing seed size decreases sensitivity whereas decreasing seed size slows down computation. RESULTS: We present a new homology search algorithm ’PatternHunter’ that uses a novel seed model for increased sensitivity and new hit-processing techniques for significantly increased speed. At Blast levels of sensitivity, PatternHunter is able to find homologies between sequences as large as human chromosomes, in mere hours on a desktop. AVAILABILITY: PatternHunter is available at, as a commercial package. It runs on all platforms that support Java. PatternHunter technology is being patented; commercial use requires a license from BSI, while non-commercial use will be free.

References in zbMATH (referenced in 36 articles , 1 standard article )

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  1. Martin, Donald E. K.: Distributions of pattern statistics in sparse Markov models (2020)
  2. Gagie, Travis; Manzini, Giovanni; Valenzuela, Daniel: Compressed spaced suffix arrays (2017)
  3. Girotto, Samuele; Comin, Matteo; Pizzi, Cinzia: Fast spaced seed hashing (2017)
  4. Girotto, Samuele; Comin, Matteo; Pizzi, Cinzia: Metagenomic reads binning with spaced seeds (2017)
  5. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  6. Liu, Yongchao; Schmidt, Bertil: CUSHAW suite: parallel and efficient algorithms for NGS read alignment (2017)
  7. Martin, Donald E. K.; Noé, Laurent: Faster exact distributions of pattern statistics through sequential elimination of states (2017)
  8. Shen, Carol; Shen, Tony; Lin, Jimmy: Comparative assessment of alignment algorithms for NGS data: features, considerations, implementations, and future (2017)
  9. Vroland, Christophe; Salson, Mikaël; Bini, Sébastien; Touzet, Hélène: Approximate search of short patterns with high error rates using the (01^\ast0) lossless seeds (2016)
  10. Egidi, Lavinia; Manzini, Giovanni: Design and analysis of periodic multiple seeds (2014)
  11. Egidi, Lavinia; Manzini, Giovanni: Better spaced seeds using quadratic residues (2013)
  12. Ji, Zhen; Zhou, Jiarui; Zhu, Zexuan; Chen, Siping: Self-configuration single particle optimizer for DNA sequence compression (2013) ioport
  13. Elloumi, Mourad (ed.); Zomaya, Albert Y. (ed.): Algorithms in computational molecular biology. Techniques approaches and applications. (2011)
  14. Blanchet-Sadri, F.; Fowler, Justin; Gafni, Joshua D.; Wilson, Kevin H.: Combinatorics on partial word correlations (2010)
  15. Chung, Won-Hyoung; Park, Seong-Bae: Hit integration for identifying optimal spaced seeds (2010) ioport
  16. Jeong, In-Seon; Park, Kyoung-Wook; Kang, Seung-Ho; Lim, Hyeong-Seok: An efficient similarity search based on indexing in large DNA databases (2010)
  17. Nakato, Ryuichiro; Gotoh, Osamu: Cgaln: fast and space-efficient whole-genome alignment (2010) ioport
  18. Noé, Laurent; Gîrdea, Marta; Kucherov, Gregory: Designing efficient spaced seeds for SOLiD read mapping (2010)
  19. Battaglia, Giovanni; Cangelosi, Davide; Grossi, Roberto; Pisanti, Nadia: Masking patterns in sequences: A new class of motif discovery with don’t cares (2009)
  20. Homer, Nils; Merriman, Barry; Nelson, Stanley F.: Local alignment of two-base encoded DNA sequence (2009) ioport

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