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| dc.contributor.author | Canal-Alonso, Ángel | |
| dc.contributor.author | Egido, Noelia | |
| dc.contributor.author | Jiménez, Pedro | |
| dc.contributor.author | Corchado Rodríguez, Juan Manuel | |
| dc.date.accessioned | 2023-10-02T11:24:43Z | |
| dc.date.available | 2023-10-02T11:24:43Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://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.iso | eng | es_ES |
| dc.subject | Machine learning | es_ES |
| dc.subject | Bioinformatics | es_ES |
| dc.subject | Next-Generation Sequencing | es_ES |
| dc.subject | Pipeline | es_ES |
| dc.subject.mesh | Sequence Analysis | * |
| dc.title | NGS data analysis: a review of major tools and pipeline frameworks for variant discovery | es_ES |
| dc.type | info:eu-repo/semantics/article | es_ES |
| dc.subject.unesco | 2409 Genética | es_ES |
| dc.subject.unesco | 1203.04 Inteligencia Artificial | es_ES |
| dc.relation.projectID | CCTT3/20/SA/0003 | es_ES |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
| dc.type.hasVersion | info:eu-repo/semantics/draft | es_ES |
| dc.subject.decs | análisis de secuencias | * |
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