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- Frings, Oliver, 1982-, et al.
(författare)
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MGclus : Network clustering employing shared neighbors
- 2013
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Ingår i: Molecular BioSystems. - : Royal Society of Chemistry (RSC). - 1742-206X .- 1742-2051. ; 9:7, s. 1670-1675
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Tidskriftsartikel (refereegranskat)abstract
- Network analysis is an important tool for functional annotation of genes and proteins. A common approach to discern structure in a global network is to infer network clusters, or modules, and assume a functional coherence within each module, which may represent a complex or a pathway. It is however not trivial to define optimal modules. Although many methods have been proposed, it is unclear which methods perform best in general. It seems that most methods produce far from optimal results but in different ways. MGclus is a new algorithm designed to detect modules with a strongly interconnected neighborhood in large scale biological interaction networks. In our benchmarks we found MGclus to outperform other methods when applied to random graphs with varying degree of noise, and to perform equally or better when applied to biological protein interaction networks. MGclus is implemented in Java and utilizes the JGraphT graph library. It has an easy to use command-line interface and is available for download from http://sonnhammer.sbc.su.se/download/software/ MGclus/.
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| 2. |
- McCormack, T., et al.
(författare)
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Statistical Assessment of Crosstalk Enrichment between Gene Groups in Biological Networks
- 2013
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:1, s. e54945-
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets. Results: Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA). Availability and Implementation: CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.
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