Motivation: Large-scale biological experiments provide snapshots into the huge number of processes running in parallel within the organism. These processes depend on a large number of (hidden) (epi)genetic, social, environmental and other factors that are out of experimentalists' control. This makes it extremely difficult to identify the dominant processes and the elements involved in them based on a single experiment. It is therefore desirable to use multiple sets of experiments targeting the same phenomena while differing in some experimental parameters (hidden or controllable). Although such datasets are becoming increasingly common, their analysis is complicated by the fact that the various biological elements could be influenced by different sets of factors.
Results: The central hypothesis of this article is that biologically related elements and processes are affected by changes in similar ways while unrelated ones are affected differently. Thus, the relations between related elements are more consistent across experiments. The method outlined here looks for groups of elements with robust intra-group relationships in the expectation that they are related. The major groups of elements may be identified in this way. The strengths of relationships per se are not valued, just their consistency. This represents a completely novel and unutilized source of information. In the analysis of time course microarray experiments, I found cell cycle- and ribosome-related genes to be the major groups. Despite not looking for these groups in particular, the identification of these genes rivals that of methods designed specifically for this purpose.
Availability: A C++ implementation is available at http://www.rinst.org/ICS/ICS_Programs.tar.gz.
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Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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Site | Modification | Modifier | Source | Reference |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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Increase the total number of rows showing on this page by using the pull-down located below the table, or use the page scroll at the table's top right to browse through the table's pages; use the arrows to the right of a column header to sort by that column; filter the table using the "Filter" box at the top of the table; click on the small "i" buttons located within a cell for an annotation to view further details about experiment type and any other genes involved in the interaction.
Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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Evidence ID | Analyze ID | File | Description |
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