Dataset Information

RegAnalyst: a web interface for the analysis of regulatory motifs, networks and pathways.

ABSTRACT: RegAnalyst is a user-friendly web interface that integrates MoPP (Motif Prediction Program), MyPatternFinder (pattern detection tool) and MycoRegDB (mycobacterial promoter and regulatory elements database). Since motif discovery is a challenging task, numerous tools have been developed over the past few years. Strikingly, the existing programs were not successful in detecting the known consensus in all mycobacterial (epitomizing degenerate) datasets even in the absence of noise and their performance was further reduced in the presence of noise. Consequently, MoPP, a de novo and greedy (for degeneracy) 'inexact' word-based tool that is tailored to enumerate significantly conserved degenerate oligonucleotide motifs was developed. Benchmarking on datasets from MycoRegDB and SCPD (http://rulai.cshl.edu/SCPD/) indicate that MoPP (i) consistently outperforms other motif discovery tools on highly degenerate as well as less degenerate datasets and (ii) successfully detects completely degenerate motifs (with no two instances of a pattern being exactly the same) even in the presence of noise. We have also developed another accessory program, MyPatternFinder, that scans a given sequence or genome to find exact or approximate matches to a query motif of any length identified by MoPP or any other user-defined motif. RegAnalyst will be a valuable tool for in silico analysis of regulatory networks and can be accessed at http://www.nii.ac.in/~deepak/RegAnalyst.

SUBMITTER: Sharma D

PROVIDER: S-EPMC2703886 | biostudies-other | 2009 Jul

REPOSITORIES: biostudies-other

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RegAnalyst: a web interface for the analysis of regulatory motifs, networks and pathways.

Sharma Deepak D Mohanty Debasisa D Surolia Avadhesha A

Nucleic acids research 20090521 Web Server issue

RegAnalyst is a user-friendly web interface that integrates MoPP (Motif Prediction Program), MyPatternFinder (pattern detection tool) and MycoRegDB (mycobacterial promoter and regulatory elements database). Since motif discovery is a challenging task, numerous tools have been developed over the past few years. Strikingly, the existing programs were not successful in detecting the known consensus in all mycobacterial (epitomizing degenerate) datasets even in the absence of noise and their perform ...[more]

PMID: 19465400

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Project description:The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

Dataset Information

RegAnalyst: a web interface for the analysis of regulatory motifs, networks and pathways.

Publications

RegAnalyst: a web interface for the analysis of regulatory motifs, networks and pathways.

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