Back to Search
Start Over
Determination of strongly overlapping signaling activity from microarray data
- Source :
- BMC Bioinformatics, Vol 7, Iss 1, p 99 (2006), BMC Bioinformatics, BMC Bioinformatics, 2006, 7 (1), pp.99. ⟨10.1186/1471-2105-7-99⟩, BMC Bioinformatics, BioMed Central, 2006, 7 (1), pp.99. ⟨10.1186/1471-2105-7-99⟩
- Publication Year :
- 2006
- Publisher :
- BMC, 2006.
-
Abstract
- BackgroundAs numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups.ResultsHere we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined from measurements of gene persistence in patterns across multiple potential dimensionalities that 15 basis vectors provides the correct dimensionality for interpreting the data. Using gene ontology and data on gene regulation in the Saccharomyces Genome Database, we identified the transcriptional signatures of several cellular processes in yeast, including cell wall creation, ribosomal disruption, chemical blocking of protein synthesis, and, criticially, individual signatures of the strongly coupled mating and filamentation pathways.ConclusionThis works demonstrates that microarray data can provide downstream indicators of pathway activity either through use of gene ontology or transcription factor databases. This can be used to investigate the specificity and success of targeted therapeutics as well as to elucidate signaling activity in normal and disease processes.
- Subjects :
- Cell signaling
Saccharomyces cerevisiae Proteins
[SDV]Life Sciences [q-bio]
Computational biology
Biology
Bioinformatics
lcsh:Computer applications to medicine. Medical informatics
Models, Biological
Biochemistry
Pattern Recognition, Automated
03 medical and health sciences
0302 clinical medicine
Structural Biology
Computer Simulation
Molecular Biology
Transcription factor
lcsh:QH301-705.5
Oligonucleotide Array Sequence Analysis
030304 developmental biology
0303 health sciences
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Microarray analysis techniques
Methodology Article
Gene Expression Profiling
Applied Mathematics
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Computer Science Applications
Gene expression profiling
[SDV] Life Sciences [q-bio]
Drug development
lcsh:Biology (General)
030220 oncology & carcinogenesis
lcsh:R858-859.7
Identification (biology)
Signal transduction
DNA microarray
Algorithms
Signal Transduction
Transcription Factors
Subjects
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 7
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....a1c696091f6f8d0bbe7ee7d05b402c17
- Full Text :
- https://doi.org/10.1186/1471-2105-7-99⟩