GECKO is a genetic algorithm to classify and explore high throughput sequencing data.

Autor: Thomas A; 1Institute of Human Genetics, CNRS UPR1142, Machine learning and gene regulation, University of Montpellier, Montpellier, France., Barriere S; 1Institute of Human Genetics, CNRS UPR1142, Machine learning and gene regulation, University of Montpellier, Montpellier, France., Broseus L; 1Institute of Human Genetics, CNRS UPR1142, Machine learning and gene regulation, University of Montpellier, Montpellier, France., Brooke J; 1Institute of Human Genetics, CNRS UPR1142, Machine learning and gene regulation, University of Montpellier, Montpellier, France., Lorenzi C; 1Institute of Human Genetics, CNRS UPR1142, Machine learning and gene regulation, University of Montpellier, Montpellier, France., Villemin JP; 1Institute of Human Genetics, CNRS UPR1142, Machine learning and gene regulation, University of Montpellier, Montpellier, France., Beurier G; 2AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France., Sabatier R; 3IGF, Centre National de la Recherche Scientifique, INSERM U1191, University of Montpellier, Montpellier, France., Reynes C; 3IGF, Centre National de la Recherche Scientifique, INSERM U1191, University of Montpellier, Montpellier, France., Mancheron A; 4LIRMM, Université de Montpellier, CNRS, UMR5506, Montpellier, France.; 5Institut Biologie Computationnelle, Montpellier, France., Ritchie W; 1Institute of Human Genetics, CNRS UPR1142, Machine learning and gene regulation, University of Montpellier, Montpellier, France.
Jazyk: angličtina
Zdroj: Communications biology [Commun Biol] 2019 Jun 20; Vol. 2, pp. 222. Date of Electronic Publication: 2019 Jun 20 (Print Publication: 2019).
DOI: 10.1038/s42003-019-0456-9
Abstrakt: 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.
Competing Interests: Competing interestsThe authors declare no competing interests.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje