Literature DB >> 18065427

TimeClust: a clustering tool for gene expression time series.

Paolo Magni1, Fulvia Ferrazzi, Lucia Sacchi, Riccardo Bellazzi.   

Abstract

UNLABELLED: TimeClust is a user-friendly software package to cluster genes according to their temporal expression profiles. It can be conveniently used to analyze data obtained from DNA microarray time-course experiments. It implements two original algorithms specifically designed for clustering short time series together with hierarchical clustering and self-organizing maps. AVAILABILITY: TimeClust executable files for Windows and LINUX platforms can be downloaded free of charge for non-profit institutions from the following web site: http://aimed11.unipv.it/TimeClust.

Mesh：

Year:  2007        PMID： 18065427     DOI： 10.1093/bioinformatics/btm605

Source DB:  PubMed          Journal:  Bioinformatics        ISSN： 1367-4803            Impact factor:   6.937


  18 in total

Review 1.  Studying and modelling dynamic biological processes using time-series gene expression data.

Authors:  Ziv Bar-Joseph; Anthony Gitter; Itamar Simon
Journal:  Nat Rev Genet       Date:  2012-07-18       Impact factor: 53.242

2.  Inferring cluster-based networks from differently stimulated multiple time-course gene expression data.

Authors:  Yuichi Shiraishi; Shuhei Kimura; Mariko Okada
Journal:  Bioinformatics       Date:  2010-03-11       Impact factor: 6.937

Review 3.  Computational methods for analyzing dynamic regulatory networks.

Authors:  Anthony Gitter; Yong Lu; Ziv Bar-Joseph
Journal:  Methods Mol Biol       Date:  2010

4.  Regulation of the yeast metabolic cycle by transcription factors with periodic activities.

Authors:  Aliz R Rao; Matteo Pellegrini
Journal:  BMC Syst Biol       Date:  2011-10-12

5.  Brain transcriptome profiles in mouse model simulating features of post-traumatic stress disorder.

Authors:  Seid Muhie; Aarti Gautam; James Meyerhoff; Nabarun Chakraborty; Rasha Hammamieh; Marti Jett
Journal:  Mol Brain       Date:  2015-02-28       Impact factor: 4.041

6.  A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data.

Authors:  Jasmin Straube; Alain-Dominique Gorse; Bevan Emma Huang; Kim-Anh Lê Cao
Journal:  PLoS One       Date:  2015-08-27       Impact factor: 3.240

Review 7.  Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.

Authors:  Daniel Spies; Constance Ciaudo
Journal:  Comput Struct Biotechnol J       Date:  2015-08-24       Impact factor: 7.271

8.  Temporal expression profiling identifies pathways mediating effect of causal variant on phenotype.

Authors:  Saumya Gupta; Aparna Radhakrishnan; Pandu Raharja-Liu; Gen Lin; Lars M Steinmetz; Julien Gagneur; Himanshu Sinha
Journal:  PLoS Genet       Date:  2015-06-03       Impact factor: 5.917

Review 9.  A survey of computational tools for downstream analysis of proteomic and other omic datasets.

Authors:  Anis Karimpour-Fard; L Elaine Epperson; Lawrence E Hunter
Journal:  Hum Genomics       Date:  2015-10-28       Impact factor: 4.639

10.  BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data.

Authors:  Joana P Gonçalves; Sara C Madeira; Arlindo L Oliveira
Journal:  BMC Res Notes       Date:  2009-07-07
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