Efficient seeding techniques for protein ...
Document type :
Communication dans un congrès avec actes
Title :
Efficient seeding techniques for protein similarity search
Author(s) :
Roytberg, Mihkail [Auteur]
Institute of Mathematical Problems in Biology [IMPB RAS]
Gambin, Anna [Auteur]
Institute of Informatics [Warsaw]
Noé, Laurent [Auteur correspondant]
Sequential Learning [SEQUOIA]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Lasota, Slawomir [Auteur]
Institute of Informatics [Warsaw]
Furletova, Eugenia [Auteur]
Institute of Mathematical Problems in Biology [IMPB RAS]
Szczurek, Ewa [Auteur]
Department Computational Molecular Biology [MPIMG Berlin]
Kucherov, Gregory [Auteur]
Sequential Learning [SEQUOIA]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Institute of Mathematical Problems in Biology [IMPB RAS]
Gambin, Anna [Auteur]
Institute of Informatics [Warsaw]
Noé, Laurent [Auteur correspondant]

Sequential Learning [SEQUOIA]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Lasota, Slawomir [Auteur]
Institute of Informatics [Warsaw]
Furletova, Eugenia [Auteur]
Institute of Mathematical Problems in Biology [IMPB RAS]
Szczurek, Ewa [Auteur]
Department Computational Molecular Biology [MPIMG Berlin]
Kucherov, Gregory [Auteur]
Sequential Learning [SEQUOIA]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Scientific editor(s) :
Elloumi
M and K\"{u}ng
J. and Linial
M. and Murphy
R.F. and Schneider
K. and Toma
C.
M and K\"{u}ng
J. and Linial
M. and Murphy
R.F. and Schneider
K. and Toma
C.
Conference title :
Proceedings of the 2nd International Conference BIRD
City :
Vienna
Country :
Autriche
Start date of the conference :
2008-07
Journal title :
Communications in Computer and Information Science
Publisher :
Springer Berlin Heidelberg
Publication date :
2008
English keyword(s) :
spaced seeds
subset seeds
protein similarity search
subset seeds
protein similarity search
HAL domain(s) :
Informatique [cs]/Bio-informatique [q-bio.QM]
Sciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
Sciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
English abstract : [en]
We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity ...
Show more >We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets.We then perform an analysis of seeds built over those alphabet and compare them with the standard Blastp seeding method [2,3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seed is less expressive (but less costly to implement) than the accumulative principle used in Blastp and vector seeds, our seeds show a similar or even better performance than Blastp on Bernoulli models of proteins compatible with the common BLOSUM62 matrix.Show less >
Show more >We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets.We then perform an analysis of seeds built over those alphabet and compare them with the standard Blastp seeding method [2,3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seed is less expressive (but less costly to implement) than the accumulative principle used in Blastp and vector seeds, our seeds show a similar or even better performance than Blastp on Bernoulli models of proteins compatible with the common BLOSUM62 matrix.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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