Literature DB >> 34902122

Improving Analysis and Annotation of Microarray Data with Protein Interactions.

Max Kotlyar1,2, Serene W H Wong1,2, Chiara Pastrello1,2, Igor Jurisica3,4,5,6.   

Abstract

Gene expression microarrays are one of the most widely used high-throughput technologies in molecular biology, with applications such as identification of disease mechanisms and development of diagnostic and prognostic gene signatures. However, the success of these tasks is often limited because microarray analysis does not account for the complex relationships among genes, their products, and overall signaling and regulatory cascades. Incorporating protein-protein interaction data into microarray analysis can help address these challenges. This chapter reviews how protein-protein interactions can help with microarray analysis, leading to benefits such as better explanations of disease mechanisms, more complete gene annotations, improved prioritization of genes for future experiments, and gene signatures that generalize better to new data.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Gene expression; Gene expression analysis; Network analysis; Protein–protein interactions

Mesh:

Year:  2022        PMID: 34902122     DOI: 10.1007/978-1-0716-1839-4_5

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


  55 in total

1.  NCBI GEO: archive for functional genomics data sets--update.

Authors:  Tanya Barrett; Stephen E Wilhite; Pierre Ledoux; Carlos Evangelista; Irene F Kim; Maxim Tomashevsky; Kimberly A Marshall; Katherine H Phillippy; Patti M Sherman; Michelle Holko; Andrey Yefanov; Hyeseung Lee; Naigong Zhang; Cynthia L Robertson; Nadezhda Serova; Sean Davis; Alexandra Soboleva
Journal:  Nucleic Acids Res       Date:  2012-11-27       Impact factor: 16.971

2.  Comparison of microarray and RNA-Seq analysis of mRNA expression in dermal mesenchymal stem cells.

Authors:  Junqin Li; Ruixia Hou; Xuping Niu; Ruifeng Liu; Qiang Wang; Chunfang Wang; Xinhua Li; Zhongping Hao; Guohua Yin; Kaiming Zhang
Journal:  Biotechnol Lett       Date:  2015-10-13       Impact factor: 2.461

3.  The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.

Authors:  Charles Wang; Binsheng Gong; Pierre R Bushel; Jean Thierry-Mieg; Danielle Thierry-Mieg; Joshua Xu; Hong Fang; Huixiao Hong; Jie Shen; Zhenqiang Su; Joe Meehan; Xiaojin Li; Lu Yang; Haiqing Li; Paweł P Łabaj; David P Kreil; Dalila Megherbi; Stan Gaj; Florian Caiment; Joost van Delft; Jos Kleinjans; Andreas Scherer; Viswanath Devanarayan; Jian Wang; Yong Yang; Hui-Rong Qian; Lee J Lancashire; Marina Bessarabova; Yuri Nikolsky; Cesare Furlanello; Marco Chierici; Davide Albanese; Giuseppe Jurman; Samantha Riccadonna; Michele Filosi; Roberto Visintainer; Ke K Zhang; Jianying Li; Jui-Hua Hsieh; Daniel L Svoboda; James C Fuscoe; Youping Deng; Leming Shi; Richard S Paules; Scott S Auerbach; Weida Tong
Journal:  Nat Biotechnol       Date:  2014-08-24       Impact factor: 54.908

4.  RNA-Seq-quantitative measurement of expression through massively parallel RNA-sequencing.

Authors:  Brian T Wilhelm; Josette-Renée Landry
Journal:  Methods       Date:  2009-03-29       Impact factor: 3.608

5.  Bring on the biomarkers.

Authors:  George Poste
Journal:  Nature       Date:  2011-01-13       Impact factor: 49.962

Review 6.  Transcriptomic responses in the fish intestine.

Authors:  Samuel A M Martin; Carola E Dehler; Elżbieta Król
Journal:  Dev Comp Immunol       Date:  2016-03-16       Impact factor: 3.636

Review 7.  RNA-Seq: a revolutionary tool for transcriptomics.

Authors:  Zhong Wang; Mark Gerstein; Michael Snyder
Journal:  Nat Rev Genet       Date:  2009-01       Impact factor: 53.242

8.  Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.

Authors:  Shanrong Zhao; Wai-Ping Fung-Leung; Anton Bittner; Karen Ngo; Xuejun Liu
Journal:  PLoS One       Date:  2014-01-16       Impact factor: 3.240

9.  ArrayExpress update - from bulk to single-cell expression data.

Authors:  Awais Athar; Anja Füllgrabe; Nancy George; Haider Iqbal; Laura Huerta; Ahmed Ali; Catherine Snow; Nuno A Fonseca; Robert Petryszak; Irene Papatheodorou; Ugis Sarkans; Alvis Brazma
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

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