HTRgene – Integrating Heterogenous Multiple Time-series Data to Investigate Stress Response Gene and Signaling

HTRgene

:: DESCRIPTION

HTRgene was designed for integrating analysis of multiple heterogeneous time-series gene expression data to identify response genes.

::DEVELOPER

Bio & Health Informatics Lab , Seoul National University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python
R

:: DOWNLOAD

HTRgene

:: MORE INFORMATION

Citation

Ahn H, Jung I, Chae H, Kang D, Jung W, Kim S.
HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis.
BMC Bioinformatics. 2019 Dec 2;20(Suppl 16):588. doi: 10.1186/s12859-019-3072-2. PMID: 31787073; PMCID: PMC6886170.

TimesVector 1.5 – Analysis of Time Series Transcriptome data from multiple Phenotypes

TimesVector 1.5

:: DESCRIPTION

TimesVector is a triclustering tool for clustering time-series data that comprises multiple conditions, or phenotypes.

::DEVELOPER

Bio & Health Informatics Lab , Seoul National University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python
R

:: DOWNLOAD

TimesVector

:: MORE INFORMATION

Citation

Jung I, Jo K, Kang H, Ahn H, Yu Y, Kim S.
TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.
Bioinformatics. 2017 Dec 1;33(23):3827-3835. doi: 10.1093/bioinformatics/btw780. PMID: 28096084.

TRAP 2.3 – Time-series RNA-seq Analysis Package

TRAP 2.3

:: DESCRIPTION

TRAP is a package integrating all necessary tasks such as mapping short reads, measuring gene expression levels, finding differentially expressed genes (DEGs), clustering and pathway analysis for time-series data in a single environment.

::DEVELOPER

Bio & Health Informatics Lab , Seoul National University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

TRAP

:: MORE INFORMATION

Citation

Jo K, Kwon HB, Kim S.
Time-series RNA-seq analysis package (TRAP) and its application to the analysis of rice, Oryza sativa L. ssp. Japonica, upon drought stress.
Methods. 2014 Jun 1;67(3):364-72. doi: 10.1016/j.ymeth.2014.02.001. Epub 2014 Feb 8. PMID: 24518221.

Deduce – Deduction of Molecular Reaction Network Structure from Measured Time-series

Deduce

:: DESCRIPTION

Deduce is a now very old but still surprisingly effective correlation-based method of reconstructing molecular reaction networks from time-series data.

::DEVELOPER

The Arkin laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux /Windows/ MacOsX
C Compiler

:: DOWNLOAD

Deduce

:: MORE INFORMATION

LSPR – Detect Periodic Expression Profiles in DNA Microarray Time-series data

LSPR

:: DESCRIPTION

LSPR is a Matlab package used to detect periodic expression profiles in DNA microarray time-series data.

::DEVELOPER

LSPR team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX / Windows
MatLab

:: DOWNLOAD

LSPR

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Apr 1;27(7):1023-5. doi: 10.1093/bioinformatics/btr041. Epub 2011 Feb 3.
LSPR: an integrated periodicity detection algorithm for unevenly sampled temporal microarray data.
Yang R1, Zhang C, Su Z.

TimeClust 1.3 – Clustering tool for Gene Expression Time Series

TimeClust 1.3

:: DESCRIPTION

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.

::DEVELOPER

laboratorio di Bioinformatica e Biologia Sintetica – Univ. of Pavia

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows

:: DOWNLOAD

TimeClust

:: MORE INFORMATION

Citation

Bioinformatics. 2008 Feb 1;24(3):430-2. Epub 2007 Dec 6.
TimeClust: a clustering tool for gene expression time series.
Magni P, Ferrazzi F, Sacchi L, Bellazzi R.

BATS 20080710 – Bayesian Analysis of Time Series Microarray experiments

BATS 20080710

:: DESCRIPTION

BATS is a new user friendly GUI software for Bayesian Analysis of Time Series microarray experiments. It implements a truly functional fully Bayesian approach which allows an user to automatically identify and estimate differentially expressed genes.

::DEVELOPER

BioinfoLab

:: SCREENSHOTS

:: REQUIREMENTS

Linux/ Windows/ MacOsX
MatLab

:: DOWNLOAD

BATS

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2008 Oct 6;9:415. doi: 10.1186/1471-2105-9-415.
BATS: a Bayesian user-friendly software for analyzing time series microarray experiments.
Angelini C1, Cutillo L, De Canditiis D, Mutarelli M, Pensky M.

BiGGEsTS 1.0.5 – Biclustering Analysis of Time Series Gene Expression Data

BiGGEsTS 1.0.5

:: DESCRIPTION

BiGGEsTS (Biclustering Gene Expression Time Series) is a free and open source software tool providing an integrated environment for the biclustering analysis of time series gene expression data. It offers a complete set of operations for retrieving potentially relevant information from the gene expression data, relying either on visualization or additional techniques for manipulating and processing this particular kind of data.

::DEVELOPER

BiGGEsTS team

:: SCREENSHOTS

:: REQUIREMENTS

Linux / Windows/ MacOsX
Java

:: DOWNLOAD

BiGGEsTS

:: MORE INFORMATION

Citation

BMC Res Notes. 2009 Jul 7;2:124. doi: 10.1186/1756-0500-2-124.
BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data.
Gonçalve JP, Madeira SC, Oliveira AL.

maSigPro 1.62.0 – R package for the analysis of Microarray and RNA-seq Time Series data

maSigPro 1.62.0

:: DESCRIPTION

maSigPro (MicroArray Significant Profiles)is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray experiments.

::DEVELOPER

The Genomics of Gene Expression Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/Windows/MacOsX
R Package
BioConductor

:: DOWNLOAD

maSigPro

:: MORE INFORMATION

Citation

Next maSigPro: updating maSigPro Bioconductor package for RNA-seq time series.
Nueda MJ, Tarazona S, Conesa A.
Bioinformatics. 2014 Jun 3. pii: btu333.

Bioinformatics. 2006 May 1;22(9):1096-102. Epub 2006 Feb 15.
maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments.
Conesa A1, Nueda MJ, Ferrer A, Talón M.

OLYMPUS – Hybrid Clustering method in Time Series Gene Expression

OLYMPUS

:: DESCRIPTION

OLYMPUS is an automated hybrid clustering method in the field of time series gene expression analysis.

::DEVELOPER

the Biosignal Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows/ MacOsX
MatLab

:: DOWNLOAD

OLYMPUS

:: MORE INFORMATION

Citation

OLYMPUS: an automated hybrid clustering method in time series gene expression. Case study: host response after Influenza A (H1N1) infection.
Dimitrakopoulou K, Vrahatis AG, Wilk E, Tsakalidis AK, Bezerianos A.
Comput Methods Programs Biomed. 2013 Sep;111(3):650-61. doi: 10.1016/j.cmpb.2013.05.025.