Literature DB >> 34251616

Co-expression Networks in Predicting Transcriptional Gene Regulation.

Synan F AbuQamar1, Khaled A El-Tarabily2,3, Arjun Sham4.   

Abstract

Recent progress in transcriptomics and co-expression networks have enabled us to predict the inference of the biological functions of genes with the associated environmental stress. Microarrays and RNA sequencing (RNA-seq) are the most commonly used high-throughput gene expression platforms for detecting differentially expressed genes between two (or more) phenotypes. Gene co-expression networks (GCNs) are a systems biology method for capturing transcriptional patterns and predicting gene interactions into functional and regulatory relationships. Here, we describe the procedures and tools used to construct and analyze GCN and investigate the integration of transcriptional data with GCN to provide reliable information about the underlying biological mechanism.
© 2021. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Biological networks; Co-expression networks; Network analysis; Systems biology; Target gene identification; Transcriptomics

Year:  2021        PMID: 34251616     DOI: 10.1007/978-1-0716-1534-8_1

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


  26 in total

Review 1.  Network biology: understanding the cell's functional organization.

Authors:  Albert-László Barabási; Zoltán N Oltvai
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2.  Network thinking in ecology and evolution.

Authors:  Stephen R Proulx; Daniel E L Promislow; Patrick C Phillips
Journal:  Trends Ecol Evol       Date:  2005-06       Impact factor: 17.712

3.  Efficient detection of network motifs.

Authors:  Sebastian Wernicke
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2006 Oct-Dec       Impact factor: 3.710

Review 4.  Network motifs: theory and experimental approaches.

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Journal:  Nat Rev Genet       Date:  2007-06       Impact factor: 53.242

5.  What is systems biology?

Authors:  Rainer Breitling
Journal:  Front Physiol       Date:  2010-05-21       Impact factor: 4.566

6.  Identification of Arabidopsis candidate genes in response to biotic and abiotic stresses using comparative microarrays.

Authors:  Arjun Sham; Khaled Moustafa; Salma Al-Ameri; Ahmed Al-Azzawi; Rabah Iratni; Synan AbuQamar
Journal:  PLoS One       Date:  2015-05-01       Impact factor: 3.240

7.  Transcriptome analysis reveals genes commonly induced by Botrytis cinerea infection, cold, drought and oxidative stresses in Arabidopsis.

Authors:  Arjun Sham; Ahmed Al-Azzawi; Salma Al-Ameri; Bassam Al-Mahmoud; Falah Awwad; Ahmed Al-Rawashdeh; Rabah Iratni; Synan AbuQamar
Journal:  PLoS One       Date:  2014-11-25       Impact factor: 3.240

Review 8.  'Omics' and Plant Responses to Botrytis cinerea.

Authors:  Synan F AbuQamar; Khaled Moustafa; Lam Son P Tran
Journal:  Front Plant Sci       Date:  2016-11-15       Impact factor: 5.753

9.  Microarray analysis of Arabidopsis WRKY33 mutants in response to the necrotrophic fungus Botrytis cinerea.

Authors:  Arjun Sham; Khaled Moustafa; Shamma Al-Shamisi; Sofyan Alyan; Rabah Iratni; Synan AbuQamar
Journal:  PLoS One       Date:  2017-02-16       Impact factor: 3.240

10.  Network approaches and applications in biology.

Authors:  Trey Ideker; Ruth Nussinov
Journal:  PLoS Comput Biol       Date:  2017-10-12       Impact factor: 4.475

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