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GECKO is a genetic algorithm to classify and explore high throughput sequencing data
Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586863/ https://www.ncbi.nlm.nih.gov/pubmed/31240260 http://dx.doi.org/10.1038/s42003-019-0456-9 |
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author | Thomas, Aubin Barriere, Sylvain Broseus, Lucile Brooke, Julie Lorenzi, Claudio Villemin, Jean-Philippe Beurier, Gregory Sabatier, Robert Reynes, Christelle Mancheron, Alban Ritchie, William |
author_facet | Thomas, Aubin Barriere, Sylvain Broseus, Lucile Brooke, Julie Lorenzi, Claudio Villemin, Jean-Philippe Beurier, Gregory Sabatier, Robert Reynes, Christelle Mancheron, Alban Ritchie, William |
author_sort | Thomas, Aubin |
collection | PubMed |
description | Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data. |
format | Online Article Text |
id | pubmed-6586863 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65868632019-06-25 GECKO is a genetic algorithm to classify and explore high throughput sequencing data Thomas, Aubin Barriere, Sylvain Broseus, Lucile Brooke, Julie Lorenzi, Claudio Villemin, Jean-Philippe Beurier, Gregory Sabatier, Robert Reynes, Christelle Mancheron, Alban Ritchie, William Commun Biol Article Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data. Nature Publishing Group UK 2019-06-20 /pmc/articles/PMC6586863/ /pubmed/31240260 http://dx.doi.org/10.1038/s42003-019-0456-9 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Thomas, Aubin Barriere, Sylvain Broseus, Lucile Brooke, Julie Lorenzi, Claudio Villemin, Jean-Philippe Beurier, Gregory Sabatier, Robert Reynes, Christelle Mancheron, Alban Ritchie, William GECKO is a genetic algorithm to classify and explore high throughput sequencing data |
title | GECKO is a genetic algorithm to classify and explore high throughput sequencing data |
title_full | GECKO is a genetic algorithm to classify and explore high throughput sequencing data |
title_fullStr | GECKO is a genetic algorithm to classify and explore high throughput sequencing data |
title_full_unstemmed | GECKO is a genetic algorithm to classify and explore high throughput sequencing data |
title_short | GECKO is a genetic algorithm to classify and explore high throughput sequencing data |
title_sort | gecko is a genetic algorithm to classify and explore high throughput sequencing data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586863/ https://www.ncbi.nlm.nih.gov/pubmed/31240260 http://dx.doi.org/10.1038/s42003-019-0456-9 |
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