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dc.contributor.authorCanal-Alonso, Ángel
dc.contributor.authorEgido, Noelia
dc.contributor.authorJiménez, Pedro
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.date.accessioned2023-10-02T11:24:43Z
dc.date.available2023-10-02T11:24:43Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/10366/153111
dc.description.abstract[EN]The analysis of genetic data has always been a problem due to the large amount of information available and the difficulty in isolating that which is relevant. However, over the years progress in sequencing techniques has been accompanied by a development of computer techniques to the current application of artificial intelligence. We can summarize the phases of sequence analysis in the following: quality assessment, alignment, pre-variant processing, variant calling and variant annotation. In this article we will review and comment on the tools used in each phase of genetic sequencing, and analyze the drawbacks and advantages offered by each of them.es_ES
dc.language.isoenges_ES
dc.subjectMachine learninges_ES
dc.subjectBioinformaticses_ES
dc.subjectNext-Generation Sequencinges_ES
dc.subjectPipelinees_ES
dc.subject.meshSequence Analysis *
dc.titleNGS data analysis: a review of major tools and pipeline frameworks for variant discoveryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.unesco2409 Genéticaes_ES
dc.subject.unesco1203.04 Inteligencia Artificiales_ES
dc.relation.projectIDCCTT3/20/SA/0003es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/draftes_ES
dc.subject.decsanálisis de secuencias *


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