Title :

Seed design framework for mapping SOLiD reads

Author(s) :

Noé, Laurent [Auteur correspondant]
Algorithms for large scale sequence analysis [SEQUOIA2]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Gîrdea, Marta [Auteur]
Algorithms for large scale sequence analysis [SEQUOIA2]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Kucherov, Gregory [Auteur]
Sequential Learning [SEQUOIA]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]

Conference title :

RECOMB

City :

Lisbon

Country :

Portugal

Start date of the conference :

2010-08-12

Book title :

Proc. RECOMB 2010

Journal title :

Lecture Notes in Computer Science

Publisher :

Springer

Publication date :

2010

English keyword(s) :

read mapping
spaced seeds design
Applied Biosystems SOLiD

HAL domain(s) :

Sciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
Informatique [cs]/Bio-informatique [q-bio.QM]

English abstract : [en]

The advent of high-throughput sequencing technologies con- stituted a major advance in genomic studies, offering new prospects in a wide range of applications. We propose a rigorous and flexible algorithmic solution to ...
Show more >The advent of high-throughput sequencing technologies con- stituted a major advance in genomic studies, offering new prospects in a wide range of applications. We propose a rigorous and flexible algorithmic solution to mapping SOLiD color-space reads to a reference genome. The solution relies on an advanced method of seed design that uses a faith- ful probabilistic model of read matches and, on the other hand, a novel seeding principle especially adapted to read mapping. Our method can handle both lossy and lossless frameworks and is able to distinguish, at the level of seed design, between SNPs and reading errors. We illustrate our approach by several seed designs and demonstrate their efficiency.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Comparison of Complete Genomes: an algorithmic and statistical approach to investigate the mechanisms of bacterial genome evolution

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL