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

Abstract : 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.
Complete list of metadatas

Cited literature [36 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-02163400
Contributor : Alban Mancheron <>
Submitted on : Monday, June 24, 2019 - 11:57:11 AM
Last modification on : Wednesday, June 26, 2019 - 1:36:37 AM

File

THOMAS2019.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Aubin Thomas, Sylvain Barriere, Lucile Broseus, Julie Brooke, Claudio Lorenzi, et al.. GECKO is a genetic algorithm to classify and explore high throughput sequencing data. Communications Biology, Nature Publishing Group, 2019, 2 (1), ⟨10.1038/s42003-019-0456-9⟩. ⟨lirmm-02163400⟩

Share

Metrics

Record views

67

Files downloads

54