Tag: Temporal

deepTS – Exploring Transcriptional Switches from pairwise, temporal, and population RNA-Seq data

deepTS

:: DESCRIPTION

deepTS is a powerful and flexible web-based Galaxy platform for identifying, visualizing and analyzing transcriptional switch (TS) events from pairwise, temporal and population transcriptome data.

::DEVELOPER

Ma Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

deepTS

:: MORE INFORMATION

Citation

Qiu Z, Chen S, Qi Y, Liu C, Zhai J, Xie S, Ma C.
Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS.
Brief Bioinform. 2021 May 20;22(3):bbaa137. doi: 10.1093/bib/bbaa137. PMID: 32728687.

mirnaTA 1.2.3 – miRNA Temporal Analyzer

mirnaTA 1.2.3

:: DESCRIPTION

mirnaTA is a bioinformatics tool which can be used to identify differentially expressed miRNAs in temporal studies.

::DEVELOPER

mirnaTA team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl
  • R

:: DOWNLOAD

 mirnaTA

:: MORE INFORMATION

Citation:

miRNA Temporal Analyzer (mirnaTA): a bioinformatics tool for identifying differentially expressed microRNAs in temporal studies using normal quantile transformation.
Cer RZ, Herrera-Galeano JE, Anderson JJ, Bishop-Lilly KA, Mokashi VP.
Gigascience. 2014 Oct 13;3:20. doi: 10.1186/2047-217X-3-20.

TPM – Temporal Pattern Mining Algorithm

TPM

:: DESCRIPTION

TPM algorithm clusters any time-series data set, specifically iTRAQ LC-MS/MS data sets. The data points that have a similar behavior over the time course are clustered together.

::DEVELOPER

Epithelial Systems Biology Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Fahad Saeed, Trairak Pisitkun, Mark Knepper and Jason D Hoffert,
“Mining Temporal Patterns from iTRAQ Mass Spectrometry (LC-MS/MS) Data“,
The Proceedings of the ISCA 3rd International Conference on Bioinformatics and Computational Biology (BiCoB), Vol 1. pp 152-159 New Orleans, Louisiana, USA, March 23-25, 2011 (ISBN: 978-1-880843-81-9)

NACEP 20140602 – Network-based Comparison of Temporal Gene Expression Patterns

NACEP 20140602

:: DESCRIPTION

NACEP (Network-based comparison of temporal gene expression patterns) is a model-based, open source tool for time-course data analysis. It explicitly uses co-expression network information in comparison of temporal gene expression data.

::DEVELOPER

Zhong Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  NACEP

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Dec 1;26(23):2944-51. Epub 2010 Sep 30.
Network-based comparison of temporal gene expression patterns.
Huang W, Cao X, Zhong S.

Gene-warp 3.2 – Generate Alignment Matrices, Explore Temporal Asynchrony in Expression of Orthologs

Gene-warp 3.2

:: DESCRIPTION

Gene-warp is a tool to select ortholog pairs with best alignments and to explore alignment paths (not expression profiles!) shared by many genes. The program takes as an input two related datasets, containing profiles of orthologous genes.

::DEVELOPER

Dmitri Papatsenko

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

  Gene-warp

:: MORE INFORMATION

Citation

Goltsev Y, Papatsenko D.
“Time warping of evolutionary distant temporal gene expression data based on noise suppression”
BMC Bioinformatics. 2009 Oct 26;10:353.

Event-mapper 2.53 – Find Temporal on/off Switches shared by many Genes

Event-mapper 2.53

:: DESCRIPTION

Event-mapper is a tool to map gene expression events, such as on/off switches on a space define by the event time and duration. Massive changes in gene expression will appear as peaks (or clusters) in the output. E-mapper Input is a time-series data set, treated with RZ-smooth. E-mapper also requires an input pattern (sample patterns are provided). To plot and explore Event-mapper data you need to process it with Glob-mapper.

::DEVELOPER

Dmitri Papatsenko

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 Event-mapper

:: MORE INFORMATION

Citation

Goltsev Y, Papatsenko D.
“Time warping of evolutionary distant temporal gene expression data based on noise suppression”
BMC Bioinformatics. 2009 Oct 26;10:353.

 

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