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Table 2 Comparative table detailing features of different GO analysis software tools

From: GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data

Software

Multiple organisms

Custom annotations

Platform

Statistical method

Visualisation

Flexible threshold

Multi-level factors

Environment

Application

GOexpress (2015)

Yes

Yes

Microarray RNA-seq

Gene permutation; RF/One-way ANOVA

Gene expression; GO

Yes

Yes

R/Bioconduct r Web-app (R/Shiny)

Ranking and visualisation of genes and GO termswith expression levels that best classify multiple experimental groups

MLseq (2014)

No

No

RNA-seq

Choose from one of several algorithms (SVM, bagSVM, RF, CART)

No

No

Yes

R/Bioconductor

Application of several ML methods to RNA-seq data (using a read count table)

seqGSEA (2014)

Yes

Yes

RNA-seq

Subject permutation; Use a statistic based on the negative binomial distribution to find differentially spliced genes between two groups

Gene ranking; Gene set ranking

No

No

R/Bioconductor

Gene set enrichment analysis of high-throughput RNA-seq data by integrating differential expression and splicing

GOseq (2010)

Yes

Yes

RNA-seq

Probability weighting function (PWF); Resampling; Wallenius distribution or random sampling to choose a null distribution to find under and over representation of GO categories

No

No

No

R/Bioconductor

Detection of GO and/or other user defined categories which are over/under represented in RNA-seq data

GOrilla (2009)

Yes

No

Microarray RNA-seq

Exact mHG P-value computation

GO (enrichment)

Yes

No

Web-based

Identification and visualisation of enriched GO terms in ranked lists of genes

GOstats (2007)

Yes

Yes

Microarray

Hypergeometric test

Gene ontology (enrichment)

Yes

No

R/Bioconductor

Tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations

STEM (2006)

Yes

Yes

Microarray

STEM clustering (assignment to predefined set of model profiles); k-means clustering

Gene expression cluster visualisation; integration with GO (enrichment)

Yes

No

Java

Clustering, comparison, and visualisation of short time series gene expression data from microarray experiments (~8 time points or fewer)

GSA (2007)

No

Yes

Microarray

Maxmean

GO (enrichment)

Yes

Yes

R/CRAN

Identification of gene sets where most genes or either positively or negatively correlate in a coordinated manner with higher values of phenotype.

  1. Abbreviations: RF random forest, ANOVA analysis of variance, SVM support vector machines, bagSVM bagging support vector machines, CART classification and regression trees