ErmineJ: Tool for functional analysis of gene expression data sets

By Homin K. Lee, William Braynen, Kiran Keshav, Paul Pavlidis. BMC Bioinformatics, 2005

296 citations recorded by the source corpus.

BackgroundIt is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories. The most common method for such analysis uses the hypergeometric distribution (or a related technique) to look for "over-representation" of groups among genes selected as being differentially expressed or otherwise of interest based on a gene-by-gene analysis. However, this method suffers from some limitations, and biologist-friendly tools that implement alternatives have not been reported.ResultsWe introduce ErmineJ, a multiplatform user-friendly stand-alone software tool for the analysis of functionally-relevant sets of genes in the context of microarray gene expression data. ErmineJ implements multiple algorithms for gene set analysis, including over-representation and resampling-based methods that focus on gene scores or correlation of gene expression profiles. In addition to a graphical user interface, ErmineJ has a command line interface and an application programming interface that can be used to automate analyses. The graphical user interface includes tools for creating and modifying gene sets, visualizing the Gene Ontology as a table or tree, and visualizing gene expression data. ErmineJ comes with a complete user manual, and is open-source software licensed under the Gnu Public License.ConclusionThe availability of multiple analysis algorithms, together with a rich feature set and simple graphical interface, should make ErmineJ a useful addition to the biologist's informatics toolbox. ErmineJ is available from http://microarray.cu.genome.org.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bab4ed5c0fef4286ab5cb511f9da6f6f765e938f

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In Molecular Biology

Citations in the corpus

PageMan: An interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments (2006, methodology): “For example, many tools exist that utilize GO [10], KEGG, or MIPS functional categories to provide an overview of changes in expression using overrepresentation (ORA) Reviewed by [11] or other approaches [6,12].”
Analyzing gene expression data in terms of gene sets: methodological issues (2007, unknown): “Contact: j.j.goeman@lumc.nl”
Gene-set approach for expression pattern analysis (2008, methodology): “According to this classification, Catmap [3], PAGE [14], T-profiler [15], ErmineJ [16] and Q1 test of Tian etal.”
EnrichNet: network-based gene set enrichment analysis (2012, background): “…functional genomics data from high-throughput experiments often involves the assessment of potential functional associations between a gene or protein set of interest, e.g. differentially expressed genes in a microarray study and known gene/protein sets representing cellular processes and pathways.”
GeneTrail—advanced gene set enrichment analysis (2007, methodology): “Other tools focus on certain types of high-throughput data as microarray gene expression data [ErmineJ (5), CRSD (6), GSEA-P (7)] or offer only one type of statistical analysis, as the GSEA-P tool (7), that is designed for GSEA only.”
Network-based prediction of human tissue-specific metabolism (2008, unknown): “Applying two such state-of-the-art methods, Ermine”
The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists (2007, methodology): “For comparison, the dataset was analyzed by tools with very different algorithms, that is, DAVID Tools [26], GoMiner [16], Ontologizer [33], GOStat [3], ermineJ [36], ADGO [37] and GENECODIS [38].”
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists (2008, unknown): “Other enrichment tools in the GSEA class using the ‘no-cutoﬀ’ strategy, such as ErmineJ (31), FatiScan (55), MEGO (36), PAGE (29), MetaGF, Go-Mapper (22) and ADGO (45), etc., employ parametric statistical approaches such as z -score, t -test, permutation analysis, etc.”
DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis (2007, unknown): “GOMiner [9], ermineJ [10], GOStat [11], etc.”
A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes. (2008, methodology): “A commonly used approach is to import a list of genes that meets the threshold criteria into a pathway program, such as the freely available ErmineJ (24), GeneMapp, David/EASE, SAFE (3), or PLAGE (45) or commercially available programs like Metacore (16) or Ingenuity.”