Titre :

SMAGEXP: a galaxy tool suite for transcriptomics data meta-analysis.

Auteur(s) :

Blanck, Samuel [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Briend, Guillemette [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
MOdel for Data Analysis and Learning [MODAL]

Titre de la revue :

GigaScience

Éditeur :

Oxford Univ Press

Date de publication :

2019-02-01

Mot(s)-clé(s) en anglais :

Computational Biology
Data Analysis
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Humans
Meta-Analysis as Topic
Neoplasms
Oligonucleotide Array Sequence Analysis
Software

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

With the proliferation of available microarray and high-throughput sequencing experiments in the public domain, the use of meta-analysis methods increases. In these experiments, where the sample size is often limited, ...
Lire la suite >With the proliferation of available microarray and high-throughput sequencing experiments in the public domain, the use of meta-analysis methods increases. In these experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably enhance the statistical power and give more accurate results. For those purposes, it combines either effect sizes or results of single studies in an appropriate manner. R packages metaMA and metaRNASeq perform meta-analysis on microarray and next generation sequencing (NGS) data, respectively. They are not interchangeable as they rely on statistical modeling specific to each technology. SMAGEXP (Statistical Meta-Analysis for Gene EXPression) integrates metaMA and metaRNAseq packages into Galaxy. We aim to propose a unified way to carry out meta-analysis of gene expression data, while taking care of their specificities. We have developed this tool suite to analyze microarray data from the Gene Expression Omnibus database or custom data from Affymetrix© microarrays. These data are then combined to carry out meta-analysis using metaMA package. SMAGEXP also offers to combine raw read counts from NGS experiments using DESeq2 and metaRNASeq package. In both cases, key values, independent from the technology type, are reported to judge the quality of the meta-analysis. These tools are available on the Galaxy main tool shed. A dockerized instance of galaxy containing SMAGEXP and its dependencies is available on Docker hub. Source code, help, and installation instructions are available on GitHub. The use of Galaxy offers an easy-to-use gene expression meta-analysis tool suite based on the metaMA and metaRNASeq packages.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL