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Compressed indexing for genomic retrieval
... Linear-scan-based systems include FASTA and BLAST, which are described in Chapter 5. Index-based systems, such as BLAT [79] and CAFE [80,81], perform a query using a pre-built index of the database. Although linear-scan-based systems are faster than index-based systems for smaller databases, as the size of the genomic sequence databases continually increases, index-based systems are more and more appealing. ...
... FASTA is considered as the most accurate (sensitive) system, while BLAST is more popular and faster but less sensitive. Index-based systems, such as BLAT [79], CAFE [80,81], and Suffix Sequoia [111,86] perform a query using a pre-built index of the database. Although linear-scan-based systems are faster than index-based systems for smaller databases, as the size of the genomic sequence databases continually increases, index-based systems are more and more appealing for their efficiency. ...
... C'est la structure de données qui, pour un mot donné, nous donne directement la liste des documents où il apparaît très rapidement. Dans le cadre de la recherche de similarités sur les séquences génomiques, le logiciel CAFE [57,58,59] utilisant cette structure de données a été proposé. ...
The sequence comparison process is one of the main bioinformatics task. The new sequencing technologies lead to a fast increasing of genomic data and strengthen the need of fast and efficient tools to perform this task. In this thesis, a new algorithm for intensive sequence comparison is proposed. It has been specifically designed to exploit all forms of parallelism of today microprocessors (SIMD instructions, multi-core architecture). This algorithm is also well suited for hardware accelerators such as FPGA or GPU boards. The algorithm has been implemented into the PLAST software (Parallel Local Alignment Search Tool). Different versions are available according to the data to process (protein and/or DNA). A MPI version has also been developed. According to the nature of the data and the type of technologies, speedup from 3 to 20 has been measured compared with the reference software, BLAST, with the same level of quality.
La comparaison de séquences est une des tâches fondamentales de la bioinformatique. Les nouvelles technologies de séquençage conduisent à une production accélérée des données génomiques et renforcent les besoins en outils rapides et efficaces pour effectuer cette tâche. Dans cette thèse, nous proposons un nouvel algorithme de comparaison intensive de séquences, explicitement conçu pour exploiter toutes les formes de parallélisme présentes dans les microprocesseurs de dernière génération (instruction SIMD, architecture multi-coeurs). Cet algorithme s'adapte également à un parallélisme massif que l'on peut trouver sur des accélérateurs de type FPGA ou GPU. Cet algorithme a été mis en oeuvre à travers le logiciel PLAST (Parallel Local Alignment Search Tool). Différentes versions sont disponibles suivant les données à traiter (protéine et/ou ADN). Une version MPI a également été mise au point pour un déploiement sur un cluster de PCs. En fonction de la nature des données et des technologies employées des accélérations de 3 à 20 ont été mesurées par rapport à la référence du domaine, le logiciel BLAST, pour un niveau de qualité équivalent.
To improve the accuracy of rapid homology searching it is common practice to filter all queries to mask low complexity regions prior to searching. We show in this paper, through a large-scale study of querying the PIR database, that applying popular filtering techniques unselectively to all queries may reduce retrieval effectiveness. We also show that masking queries with our new technique, cafefilter, which uses the overall distribution of motifs in a database, is at least as effective as current popular query filtering tools in large-scale tests.
Genomic sequence databases are widely used by molecular biologists
for homology searching. Amino acid and nucleotide databases are
increasing in size exponentially, and mean sequence lengths are also
increasing. In searching such databases, it is desirable to use
heuristics to perform computationally intensive local alignments on
selected sequences and to reduce the costs of the alignments that are
attempted. We present an index-based approach for both selecting
sequences that display broad similarity to a query and for fast local
alignment. We show experimentally that the indexed approach results in
significant savings in computationally intensive local alignments and
that index-based searching is as accurate as existing exhaustive search
schemes
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