Literature DB >> 34958073

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline.

Robert C Moseley1, Sophia Campione2, Bree Cummins3, Francis Motta4, Steven B Haase2.   

Abstract

Developing gene regulatory network models is a major challenge in systems biology. Several computational tools and pipelines have been developed to tackle this challenge, including the newly developed Inherent Dynamics Pipeline. The Inherent Dynamics Pipeline consists of several previously published tools that work synergistically and are connected in a linear fashion, where the output of one tool is then used as input for the following tool. As with most computational techniques, each step of the Inherent Dynamics Pipeline requires the user to make choices about parameters that don't have a precise biological definition. These choices can substantially impact gene regulatory network models produced by the analysis. For this reason, the ability to visualize and explore the consequences of various parameter choices at each step can help increase confidence in the choices and the results.The Inherent Dynamics Visualizer is a comprehensive visualization package that streamlines the process of evaluating parameter choices through an interactive interface within a web browser. The user can separately examine the output of each step of the pipeline, make intuitive changes based on visual information, and benefit from the automatic production of necessary input files for the Inherent Dynamics Pipeline. The Inherent Dynamics Visualizer provides an unparalleled level of access to a highly intricate tool for the discovery of gene regulatory networks from time series transcriptomic data.

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Year:  2021        PMID: 34958073      PMCID: PMC8991438          DOI: 10.3791/63084

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  18 in total

1.  Model Rejection and Parameter Reduction via Time Series.

Authors:  Bree Cummins; Tomas Gedeon; Shaun Harker; Konstantin Mischaikow
Journal:  SIAM J Appl Dyn Syst       Date:  2018-05-31       Impact factor: 2.316

2.  Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics.

Authors:  Tarmo Aijö; Harri Lähdesmäki
Journal:  Bioinformatics       Date:  2009-08-25       Impact factor: 6.937

3.  Constraining G1-specific transcription to late G1 phase: the MBF-associated corepressor Nrm1 acts via negative feedback.

Authors:  Robertus A M de Bruin; Tatyana I Kalashnikova; Charly Chahwan; W Hayes McDonald; James Wohlschlegel; John Yates; Paul Russell; Curt Wittenberg
Journal:  Mol Cell       Date:  2006-08       Impact factor: 17.970

4.  NDD1, a high-dosage suppressor of cdc28-1N, is essential for expression of a subset of late-S-phase-specific genes in Saccharomyces cerevisiae.

Authors:  C J Loy; D Lydall; U Surana
Journal:  Mol Cell Biol       Date:  1999-05       Impact factor: 4.272

5.  An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum.

Authors:  Lauren M Smith; Francis C Motta; Garima Chopra; J Kathleen Moch; Robert R Nerem; Bree Cummins; Kimberly E Roche; Christina M Kelliher; Adam R Leman; John Harer; Tomas Gedeon; Norman C Waters; Steven B Haase
Journal:  Science       Date:  2020-05-15       Impact factor: 47.728

6.  Two yeast forkhead genes regulate the cell cycle and pseudohyphal growth.

Authors:  G Zhu; P T Spellman; T Volpe; P O Brown; D Botstein; T N Davis; B Futcher
Journal:  Nature       Date:  2000-07-06       Impact factor: 49.962

7.  Saccharomyces Genome Database: the genomics resource of budding yeast.

Authors:  J Michael Cherry; Eurie L Hong; Craig Amundsen; Rama Balakrishnan; Gail Binkley; Esther T Chan; Karen R Christie; Maria C Costanzo; Selina S Dwight; Stacia R Engel; Dianna G Fisk; Jodi E Hirschman; Benjamin C Hitz; Kalpana Karra; Cynthia J Krieger; Stuart R Miyasato; Rob S Nash; Julie Park; Marek S Skrzypek; Matt Simison; Shuai Weng; Edith D Wong
Journal:  Nucleic Acids Res       Date:  2011-11-21       Impact factor: 16.971

8.  Combining tree-based and dynamical systems for the inference of gene regulatory networks.

Authors:  Vân Anh Huynh-Thu; Guido Sanguinetti
Journal:  Bioinformatics       Date:  2015-01-07       Impact factor: 6.937

9.  Wisdom of crowds for robust gene network inference.

Authors:  Daniel Marbach; James C Costello; Robert Küffner; Nicole M Vega; Robert J Prill; Diogo M Camacho; Kyle R Allison; Manolis Kellis; James J Collins; Gustavo Stolovitzky
Journal:  Nat Methods       Date:  2012-07-15       Impact factor: 28.547

10.  Causal network inference using biochemical kinetics.

Authors:  Chris J Oates; Frank Dondelinger; Nora Bayani; James Korkola; Joe W Gray; Sach Mukherjee
Journal:  Bioinformatics       Date:  2014-09-01       Impact factor: 6.937

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  1 in total

1.  Experimental guidance for discovering genetic networks through hypothesis reduction on time series.

Authors:  Breschine Cummins; Francis C Motta; Robert C Moseley; Anastasia Deckard; Sophia Campione; Marcio Gameiro; Tomáš Gedeon; Konstantin Mischaikow; Steven B Haase
Journal:  PLoS Comput Biol       Date:  2022-10-10       Impact factor: 4.779

  1 in total

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