Ontological analysis of gene expression data: current tools, limitations, and open problems

By Purvesh Khatri, Sorin Draghici. Bioinform., 2005

912 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/269a8428ea9d00a57792fe544ec2d784c476f3c0

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In Molecular Biology

Citations in the corpus

A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes. (2008, background): “Most genes known to be involved in a functional category are annotated in annotation databases, such as the Gene Ontology (GO) database (17), Kyoto Encyclopedia of Genes and Genomes (KEGG) (22), or Entrez Gene (23).”
Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences (2016, methodology): “*Enrichment portals run with different algorithms and statistical methods [109] The software package chosen for the analysis (library, algorithm and statistics) will influence the final result At the moment, there is no gold standard method for enrichment Use a minimum of three different portals to ”
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation (2010, methodology): “Over 60 enrichment analysis methods and tools have been developed in the last few years [5,9,10].”
GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics (2012, methodology): “SEA is probably the most typical way to make an enrichment analysis (7,8).”
STEM: a tool for the analysis of short time series gene expression data (2006, background): “A number of software packages are available which can determine GO term enrichments in a set of genes (see [16] for a recent review).”
GSVA: gene set variation analysis for microarray and RNA-Seq data (2013, neutral): “Many other supervised, population based approaches have also been described [12,17,20,31-34].”
The Gene Ontology (GO) project in 2006 (2005, background): “shtml), and a paper reviewing the relative merits of a subset of these tools has recently been published (16).”
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists (2008, positive): “Thereafter, the annotation terms with enriched gene members can be identified from tens of thousands of other annotation terms in a high-throughput fashion (11,12).”
ErmineJ: Tool for functional analysis of gene expression data sets (2005, methodology): “…published benchmarks because the number of gene sets analyzed and the size of the "null" gene set was not reported, and the times reported might in some cases include initial startup times [3], the fastest reported methods on the largest data sets tested completed ORA analyses in under 10 seconds.”
Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges (2012, methodology): “We refer the readers to recent comparisons of ORA tools for more details [12,13].”
Biomedical ontologies: a functional perspective (2007, background): “The most common task is that of finding over-represented GO categories in a list of genes [19].”
Intrinsic Disorder Is a Common Feature of Hub Proteins from Four Eukaryotic Interactomes (2006, methodology): “Difficulties with multiple testing correction and correlated annotations have been identified as a weakness of many approaches to the proteome-wide functional analysis [30].”
MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data (2010, background): “In fact, they have long been the subject of intense debate among researchers involved in gene expression data analysis (5–8).”
A novel signaling pathway impact analysis (2009, positive): “DE genes onto Gene Ontology (GO) terms is the most widely used attempt at this (Drăghici et al., 2003; Khatri and Draghici, 2005; Khatri et al., 2002).”
Ontologizer 2.0 - a multifunctional tool for GO term enrichment analysis and data exploration (2008, neutral): “Since the introduction of GO, many tools have been developed that implement more or less the same approach for identifying GO terms whose annotations are enriched in study sets (Khatri and Drăghici, 2005).”
Concept annotation in the CRAFT corpus (2012, background): “of differentially expressed genes are widely used [8-10],”
The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists (2007, background): “The power of many of these applications is to systematically highlight the most over-represented biological terms, out of a list of hundreds or thousands of terms, to increase the likelihood of investigators identifying biological processes most pertinent to the biological phenomena under study [17]”