IndeCut evaluates performance of network motif discovery algorithms

Abstract

Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no sound practical method to numerically evaluate whether any network motif discovery algorithm performs as intended -thus it was not possible to assess the validity of resulting network motifs. In this work, we present IndeCut, the first and only method that allows characterization of network motif finding algorithm performance on any network of interest. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut estimates the minimally required number of samples that each network motif discovery tool needs in order to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the most accurate network motif discovery tool for their network of interest among many available options. IndeCut is an open source software package and is available at https://github.com/megrawlab/IndeCut.

Small Genetic Circuits and MicroRNAs: Big Players in Polymerase II Transcriptional Control in Plants

“…The WaRSwap randomization algorithm (Megraw et al, 2013) provides a quicksampling heuristic that focuses on uniform sampling of biological networks with any combination of interaction types present (TFonly and TF gene as well as those interaction types involving miRNAs); it accounts for TF autoregulation, large source and target hubs, and the possibility that miRNA target rearrangements can occur in evolutionary time. User-friendly software is now available (Ansariola and Megraw, 2015). CoMoFinder (Liang et al, 2015) takes a different approach, focusing on networks with all three component types present (TFs, miRNAs, and genes) where autoregulation and target rearrangements are not present; attention is turned away from uniform sampling and toward a fast method for finding subcircuits that are overrepresented with respect to a subcollection of networks identified by the algorithm as having many differences with the input network.…”

Systematic integration of protein affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile

“…The enrichment of signed feed-forward loops (FFLs), regulated feedback, and regulating feedback network motifs was computed using FANMOD 25 , which takes into consideration TF regulatory roles (activation and repression). The command line version of FANMOD from IndeCut 29 was used with default parameters, except for the inclusion of regulatory role (colored edges) 25 (fanmod 3 100000 1 <input_file> 1 0 1 2 0 1 0 1000 3 3 <output_file> 1 1). Z-scores for signed FFLs, regulated feedback, and regulating feedback network motifs were extracted for each cancer and converted to triad significance profiles using the methods of Milo et al, 2004 24 .…”