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Objective review of de novo stand-alone error correction methods for NGS data

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Objective review of de novo stand-alone error correction methods for NGS data

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dc.contributor.author Alic, Andrei Stefan es_ES
dc.contributor.author Ruzafa, David es_ES
dc.contributor.author Dopazo, Joaquin es_ES
dc.contributor.author Blanquer Espert, Ignacio es_ES
dc.date.accessioned 2017-04-25T09:53:26Z
dc.date.available 2017-04-25T09:53:26Z
dc.date.issued 2016-04
dc.identifier.issn 1759-0876
dc.identifier.uri http://hdl.handle.net/10251/79927
dc.description.abstract [EN] The sequencing market has increased steadily over the last few years, with different approaches to read DNA information prone to different types of errors. Multiple studies demonstrated the impact of sequencing errors on different applications of next-generation sequencing (NGS), making error correction a fundamental initial step. Different methods in the literature use different approaches and fit different types of problems. We analyzed 50 methods divided into five main approaches (k-spectrum, suffix arrays, multiple-sequence alignment, read clustering, and probabilistic models). They are not published as a part of a suite (stand-alone), and target raw, unprocessed data without an existing reference genome (de novo). These correctors handle one or more sequencing technologies using the same or different approaches. They face general challenges (sometimes with specific traits for specific technologies) such as repetitive regions, uncalled bases, and ploidy. Even assessing their performance is a challenge in itself because of the approach taken by various authors, the unknown factor (de novo), and the behavior of the third-party tools employed in the benchmarks. This study aims to help the researcher in the field to advance the field of error correction, the educator to have a brief but comprehensive companion, and the bioinformatician to choose the right tool for the right job. © 2016 John Wiley & Sons, Ltd es_ES
dc.description.sponsorship We want to thank our colleague Eloy Romero Alcale who has provided valuable advice regarding the structure of the document. This work was supported by Generalitat Valenciana [GRISOLIA/2013/013 to A.A.].
dc.language Inglés es_ES
dc.publisher Wiley es_ES
dc.relation GV/GRISOLIA/2013/013 es_ES
dc.relation.ispartof Wiley Interdisciplinary Reviews: Computational Molecular Science es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Burrows-Wheeler Transform es_ES
dc.subject Short-Read Data es_ES
dc.subject Sequencing Reads es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title Objective review of de novo stand-alone error correction methods for NGS data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/wcms.1239
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Alic, AS.; Ruzafa, D.; Dopazo, J.; Blanquer Espert, I. (2016). Objective review of de novo stand-alone error correction methods for NGS data. Wiley Interdisciplinary Reviews: Computational Molecular Science. 6(2):111-146. doi:10.1002/wcms.1239 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.1002/wcms.1239 es_ES
dc.description.upvformatpinicio 111 es_ES
dc.description.upvformatpfin 146 es_ES
dc.type.version info:eu repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 305484 es_ES
dc.contributor.funder Generalitat Valenciana (GV)


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