Literature DB >> 22028466

ANAT: a tool for constructing and analyzing functional protein networks.

Nir Yosef1, Einat Zalckvar, Assaf D Rubinstein, Max Homilius, Nir Atias, Liram Vardi, Igor Berman, Hadas Zur, Adi Kimchi, Eytan Ruppin, Roded Sharan.   

Abstract

Genome-scale screening studies are gradually accumulating a wealth of data on the putative involvement of hundreds of genes in various cellular responses or functions. A fundamental challenge is to chart the molecular pathways that underlie these systems. ANAT is an interactive software tool, implemented as a Cytoscape plug-in, for elucidating functional networks of proteins. It encompasses a number of network inference algorithms and provides access to networks of physical associations in several organisms. In contrast to existing software tools, ANAT can be used to infer subnetworks that connect hundreds of proteins to each other or to a given set of "anchor" proteins, a fundamental step in reconstructing cellular subnetworks. The interactive component of ANAT provides an array of tools for evaluating and exploring the resulting subnetwork models and for iteratively refining them. We demonstrate the utility of ANAT by studying the crosstalk between the autophagic and apoptotic cell death modules in humans, using a network of physical interactions. Relative to published software tools, ANAT is more accurate and provides more features for comprehensive network analysis. The latest version of the software is available at http://www.cs.tau.ac.il/~bnet/ANAT_SI.

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Year:  2011        PMID: 22028466     DOI: 10.1126/scisignal.2001935

Source DB:  PubMed          Journal:  Sci Signal        ISSN: 1945-0877            Impact factor:   8.192


  26 in total

1.  Systematic identification of gene annotation errors in the widely used yeast mutation collections.

Authors:  Taly Ben-Shitrit; Nir Yosef; Keren Shemesh; Roded Sharan; Eytan Ruppin; Martin Kupiec
Journal:  Nat Methods       Date:  2012-02-05       Impact factor: 28.547

2.  Signaling hypergraphs.

Authors:  Anna Ritz; Allison N Tegge; Hyunju Kim; Christopher L Poirel; T M Murali
Journal:  Trends Biotechnol       Date:  2014-05-22       Impact factor: 19.536

Review 3.  A network-oriented perspective on cardiac calcium signaling.

Authors:  Christopher H George; Dimitris Parthimos; Nicole C Silvester
Journal:  Am J Physiol Cell Physiol       Date:  2012-07-25       Impact factor: 4.249

4.  Elucidating influenza inhibition pathways via network reconstruction.

Authors:  Arnon Mazza; Irit Gat-Viks; Roded Sharan
Journal:  J Comput Biol       Date:  2014-01-22       Impact factor: 1.479

5.  In vivo regulation of gene expression and T helper type 17 differentiation by RORγt inverse agonists.

Authors:  Jill Skepner; Mark Trocha; Radha Ramesh; Xiaoyan A Qu; Darby Schmidt; Erkan Baloglu; Mercedes Lobera; Scott Davis; Michael A Nolan; Thaddeus J Carlson; Jonathan Hill; Shomir Ghosh; Mark S Sundrud; Jianfei Yang
Journal:  Immunology       Date:  2015-04-03       Impact factor: 7.397

Review 6.  Integrative approaches for finding modular structure in biological networks.

Authors:  Koyel Mitra; Anne-Ruxandra Carvunis; Sanath Kumar Ramesh; Trey Ideker
Journal:  Nat Rev Genet       Date:  2013-10       Impact factor: 53.242

7.  Proteomic Analysis of Dynein-Interacting Proteins in Amyotrophic Lateral Sclerosis Synaptosomes Reveals Alterations in the RNA-Binding Protein Staufen1.

Authors:  Noga Gershoni-Emek; Arnon Mazza; Michael Chein; Tal Gradus-Pery; Xin Xiang; Ka Wan Li; Roded Sharan; Eran Perlson
Journal:  Mol Cell Proteomics       Date:  2015-11-23       Impact factor: 5.911

8.  Systematic identification and correction of annotation errors in the genetic interaction map of Saccharomyces cerevisiae.

Authors:  Nir Atias; Martin Kupiec; Roded Sharan
Journal:  Nucleic Acids Res       Date:  2015-11-23       Impact factor: 16.971

9.  Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs).

Authors:  Michaël Pierrelée; Ana Reynders; Fabrice Lopez; Aziz Moqrich; Laurent Tichit; Bianca H Habermann
Journal:  Sci Rep       Date:  2021-07-01       Impact factor: 4.379

10.  Met kinetic signature derived from the response to HGF/SF in a cellular model predicts breast cancer patient survival.

Authors:  Gideon Y Stein; Nir Yosef; Hadar Reichman; Judith Horev; Adi Laser-Azogui; Angelique Berens; James Resau; Eytan Ruppin; Roded Sharan; Ilan Tsarfaty
Journal:  PLoS One       Date:  2012-09-25       Impact factor: 3.240

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