Modeling sequencing errors by combining Hidden Markov models.

Détails

ID Serval
serval:BIB_01ABEF75E62A
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Modeling sequencing errors by combining Hidden Markov models.
Périodique
Bioinformatics
Auteur(s)
Lottaz C., Iseli C., Jongeneel C.V., Bucher P.
ISSN
1367-4811[electronic], 1367-4803[linking]
Statut éditorial
Publié
Date de publication
2003
Volume
19 Suppl 2
Numéro
Suppl 2
Pages
ii103-ii112
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.
Mots-clé
Algorithms, Base Sequence, Computer Simulation, Data Interpretation, Statistical, Databases, Genetic, Expressed Sequence Tags, Information Storage and Retrieval/methods, Markov Chains, Models, Genetic, Models, Statistical, Molecular Sequence Data, Pattern Recognition, Automated/methods, Reproducibility of Results, Sensitivity and Specificity, Sequence Analysis, DNA/methods
Pubmed
Web of science
Open Access
Oui
Création de la notice
24/01/2008 16:39
Dernière modification de la notice
20/08/2019 13:23
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