Literature DB >> 34321962

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.

Tyler Grimes1, Somnath Datta1.   

Abstract

Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available on CRAN and on GitHub at https://github.com/tgrimes/SeqNet.

Keywords:  Gaussian graphical model; Gene regulatory networks; co-expression methods; differential network analysis

Year:  2021        PMID: 34321962      PMCID: PMC8315007          DOI: 10.18637/jss.v098.i12

Source DB:  PubMed          Journal:  J Stat Softw        ISSN: 1548-7660            Impact factor:   6.440


  79 in total

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Authors:  Hidde de Jong
Journal:  J Comput Biol       Date:  2002       Impact factor: 1.479

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Review 4.  Computational methods for Gene Regulatory Networks reconstruction and analysis: A review.

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5.  Joint Learning of Multiple Differential Networks With Latent Variables.

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Review 6.  Systematic review of next-generation sequencing simulators: computational tools, features and perspectives.

Authors:  Min Zhao; Di Liu; Hong Qu
Journal:  Brief Funct Genomics       Date:  2017-05-01       Impact factor: 4.241

7.  DINGO: differential network analysis in genomics.

Authors:  Min Jin Ha; Veerabhadran Baladandayuthapani; Kim-Anh Do
Journal:  Bioinformatics       Date:  2015-07-06       Impact factor: 6.937

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Authors:  Patrick E Meyer; Frédéric Lafitte; Gianluca Bontempi
Journal:  BMC Bioinformatics       Date:  2008-10-29       Impact factor: 3.169

9.  A Bayesian regression approach to the inference of regulatory networks from gene expression data.

Authors:  Simon Rogers; Mark Girolami
Journal:  Bioinformatics       Date:  2005-05-06       Impact factor: 6.937

10.  The Reactome pathway Knowledgebase.

Authors:  Antonio Fabregat; Konstantinos Sidiropoulos; Phani Garapati; Marc Gillespie; Kerstin Hausmann; Robin Haw; Bijay Jassal; Steven Jupe; Florian Korninger; Sheldon McKay; Lisa Matthews; Bruce May; Marija Milacic; Karen Rothfels; Veronica Shamovsky; Marissa Webber; Joel Weiser; Mark Williams; Guanming Wu; Lincoln Stein; Henning Hermjakob; Peter D'Eustachio
Journal:  Nucleic Acids Res       Date:  2015-12-09       Impact factor: 16.971

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  1 in total

1.  A novel probabilistic generator for large-scale gene association networks.

Authors:  Tyler Grimes; Somnath Datta
Journal:  PLoS One       Date:  2021-11-12       Impact factor: 3.240

  1 in total

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