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ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning
We describe ReorientExpress, a method to perform reference-free orientation of transcriptomic long sequencing reads. ReorientExpress uses deep learning to correctly predict the orientation of the majority of reads, and in particular when trained on a closely related species or in combination with re...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883653/ https://www.ncbi.nlm.nih.gov/pubmed/31783882 http://dx.doi.org/10.1186/s13059-019-1884-z |
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author | Ruiz-Reche, Angel Srivastava, Akanksha Indi, Joel A. de la Rubia, Ivan Eyras, Eduardo |
author_facet | Ruiz-Reche, Angel Srivastava, Akanksha Indi, Joel A. de la Rubia, Ivan Eyras, Eduardo |
author_sort | Ruiz-Reche, Angel |
collection | PubMed |
description | We describe ReorientExpress, a method to perform reference-free orientation of transcriptomic long sequencing reads. ReorientExpress uses deep learning to correctly predict the orientation of the majority of reads, and in particular when trained on a closely related species or in combination with read clustering. ReorientExpress enables long-read transcriptomics in non-model organisms and samples without a genome reference without using additional technologies and is available at https://github.com/comprna/reorientexpress. |
format | Online Article Text |
id | pubmed-6883653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68836532019-12-03 ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning Ruiz-Reche, Angel Srivastava, Akanksha Indi, Joel A. de la Rubia, Ivan Eyras, Eduardo Genome Biol Method We describe ReorientExpress, a method to perform reference-free orientation of transcriptomic long sequencing reads. ReorientExpress uses deep learning to correctly predict the orientation of the majority of reads, and in particular when trained on a closely related species or in combination with read clustering. ReorientExpress enables long-read transcriptomics in non-model organisms and samples without a genome reference without using additional technologies and is available at https://github.com/comprna/reorientexpress. BioMed Central 2019-11-29 /pmc/articles/PMC6883653/ /pubmed/31783882 http://dx.doi.org/10.1186/s13059-019-1884-z Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Method Ruiz-Reche, Angel Srivastava, Akanksha Indi, Joel A. de la Rubia, Ivan Eyras, Eduardo ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning |
title | ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning |
title_full | ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning |
title_fullStr | ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning |
title_full_unstemmed | ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning |
title_short | ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning |
title_sort | reorientexpress: reference-free orientation of nanopore cdna reads with deep learning |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883653/ https://www.ncbi.nlm.nih.gov/pubmed/31783882 http://dx.doi.org/10.1186/s13059-019-1884-z |
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