Table 2.
Comparative table detailing features of different GO analysis software tools
| 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. |
Abbreviations: RF random forest, ANOVA analysis of variance, SVM support vector machines, bagSVM bagging support vector machines, CART classification and regression trees