Literature DB >> 21938624

Genome-wide dissection of posttranscriptional and posttranslational interactions.

Mukesh Bansal1, Andrea Califano.   

Abstract

Transcriptional interactions in the cell are modulated by a variety of posttranscriptional and posttranslational mechanisms that make them highly dependent on the molecular context of the specific cell. These include, among others, microRNA-mediated control of transcription factor (TF) mRNA translation and degradation, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more cofactors, and mRNA/protein degradation and stabilization processes. Thus, the ability of a transcription factor to regulate its targets depends on a variety of genetic and epigenetic mechanisms, resulting in highly context-dependent regulatory networks. In this chapter, we introduce a step-by-step guide on how to use the MINDy systems biology algorithm (Modulator Inference by Network Dynamics) that we recently developed, for the genome-wide, context-specific identification of posttranscriptional and posttranslational modulators of transcription factor activity.

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Year:  2012        PMID: 21938624     DOI: 10.1007/978-1-61779-292-2_8

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  8 in total

Review 1.  From cancer genomes to oncogenic drivers, tumour dependencies and therapeutic targets.

Authors:  Cheryl Eifert; R Scott Powers
Journal:  Nat Rev Cancer       Date:  2012-08       Impact factor: 60.716

2.  A negative selection heuristic to predict new transcriptional targets.

Authors:  Luigi Cerulo; Vincenzo Paduano; Pietro Zoppoli; Michele Ceccarelli
Journal:  BMC Bioinformatics       Date:  2013-01-14       Impact factor: 3.169

3.  STK38 is a critical upstream regulator of MYC's oncogenic activity in human B-cell lymphoma.

Authors:  B C Bisikirska; S J Adam; M J Alvarez; P Rajbhandari; R Cox; C Lefebvre; K Wang; G E Rieckhof; D W Felsher; A Califano
Journal:  Oncogene       Date:  2012-11-26       Impact factor: 9.867

Review 4.  "Good enough solutions" and the genetics of complex diseases.

Authors:  James N Weiss; Alain Karma; W Robb MacLellan; Mario Deng; Christoph D Rau; Colin M Rees; Jessica Wang; Nicholas Wisniewski; Eleazar Eskin; Steve Horvath; Zhilin Qu; Yibin Wang; Aldons J Lusis
Journal:  Circ Res       Date:  2012-08-03       Impact factor: 17.367

5.  Inferring protein modulation from gene expression data using conditional mutual information.

Authors:  Federico M Giorgi; Gonzalo Lopez; Jung H Woo; Brygida Bisikirska; Andrea Califano; Mukesh Bansal
Journal:  PLoS One       Date:  2014-10-14       Impact factor: 3.240

6.  Identification of Post-Transcriptional Modulators of Breast Cancer Transcription Factor Activity Using MINDy.

Authors:  Thomas M Campbell; Mauro A A Castro; Bruce A J Ponder; Kerstin B Meyer
Journal:  PLoS One       Date:  2016-12-20       Impact factor: 3.240

7.  Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM.

Authors:  Andrew J Sedgewick; Stephen C Benz; Shahrooz Rabizadeh; Patrick Soon-Shiong; Charles J Vaske
Journal:  Bioinformatics       Date:  2013-07-01       Impact factor: 6.937

8.  Multiscale modeling of the causal functional roles of nsSNPs in a genome-wide association study: application to hypoxia.

Authors:  Li Xie; Clara Ng; Thahmina Ali; Raoul Valencia; Barbara L Ferreira; Vincent Xue; Maliha Tanweer; Dan Zhou; Gabriel G Haddad; Philip E Bourne; Lei Xie
Journal:  BMC Genomics       Date:  2013-05-28       Impact factor: 3.969

  8 in total

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