Discovering causal signaling pathways through gene-expression patterns.
High-throughput gene-expression studies result in lists of differentially expressed genes. Most current meta-analyses of these gene lists include searching for significant membership of the translated proteins in various signaling pathways. However, such membership enrichment algorithms do not provide insight into which pathways caused the genes to be differentially expressed in the first place. Here, we present an intuitive approach for discovering upstream signaling pathways responsible for regulating these differentially expressed genes. We identify consistently regulated signature genes specific for signal transduction pathways from a panel of single-pathway perturbation experiments. An algorithm that detects overrepresentation of these signature genes in a gene group of interest is used to infer the signaling pathway responsible for regulation. We expose our novel resource and algorithm through a web server called SPEED: Signaling Pathway Enrichment using Experimental Data sets. SPEED can be freely accessed at http://speed.sys-bio.net/.
Parikh
Jignesh R
JR
Bioinformatics Program, Boston University, Boston, MA 02115, USA.
Klinger
Bertram
B
Xia
Yu
Y
Marto
Jarrod A
JA
Blüthgen
Nils
N
D000465:Algorithms; D022762:CCAAT-Enhancer-Binding Proteins / Q000235:genetics; D045744:Cell Line, Tumor; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D020407:Internet; D015470:Leukemia, Myeloid, Acute / Q000235:genetics / Q000378:metabolism; D009154:Mutation; D015398:Signal Transduction; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
D022762:CCAAT-Enhancer-Binding Proteins; C515848:CEBPA protein, human; D014157:Transcription Factors