TimeTP 1.0 – Influence Maximization in Time bounded network Identifies Transcription Factors Regulating Perturbed Pathways

TimeTP 1.0

:: DESCRIPTION

TimeTP is a novel time-series analysis method for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein-protein interaction network to locate TFs triggering the perturbation.

::DEVELOPER

Bio & Health Informatics Lab , Seoul National University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

TimeTP

:: MORE INFORMATION

Citation

Jo K, Jung I, Moon JH, Kim S.
Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways.
Bioinformatics. 2016 Jun 15;32(12):i128-i136. doi: 10.1093/bioinformatics/btw275. PMID: 27307609; PMCID: PMC4908359.

GPLVM – Time-structured Gene-expression data

GPLVM

:: DESCRIPTION

GPLVM (Gaussian Process Latent Variable Models) is a novel framework to analyse single-cell qPCR expression data from differnt developmental stages.

::DEVELOPER

Institute of Computational Biology, German Research Center for Environmental Health (GmbH)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / MacOsX
MatLab

:: DOWNLOAD

GPLVM

:: MORE INFORMATION

Citation

A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst.
Buettner F, Theis FJ.
Bioinformatics. 2012 Sep 15;28(18):i626-i632. doi: 10.1093/bioinformatics/bts385.

TIME – Temporal Insights into Microbial Ecology

TIME

:: DESCRIPTION

TIME is a web application which enables microbiome researchers to visually explore, make logical inferences, and gather insights from time series microbiome datasets.

::DEVELOPER

TIME team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

WEb browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Baksi KD, Kuntal BK, Mande SS.
‘TIME’: A Web Application for Obtaining Insights into Microbial Ecology Using Longitudinal Microbiome Data.
Front Microbiol. 2018 Jan 24;9:36. doi: 10.3389/fmicb.2018.00036. Erratum in: Front Microbiol. 2020 Nov 11;11:605295. PMID: 29416530; PMCID: PMC5787560.

SANTA-SIM 1.0 – Simulate the Evolution of Population of Gene Sequences Forwards through Time

SANTA-SIM 1.0

:: DESCRIPTION

SANTA-SIM is a forward-time simulator for gene sequences modeling a variety of mutation and selection processes.

::DEVELOPER

SANTA-SIM team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / MacOS
Java

:: DOWNLOAD

SANTA-SIM

:: MORE INFORMATION

Citation:

SANTA-SIM: simulating viral sequence evolution dynamics under selection and recombination.
Jariani A, Warth C, Deforche K, Libin P, Drummond AJ, Rambaut A, Matsen Iv FA, Theys K.
Virus Evol. 2019 Mar 8;5(1):vez003. doi: 10.1093/ve/vez003.

TimeTree 3.1 – Species Divergence Times on the Mobile Phone

TimeTree 3.1

:: DESCRIPTION

TIMETREE is a public resource for knowledge on the timescale and evolutionary history of life.A search utility allows exploration of the thousands of divergence times among organisms in the published literature. A tree-based (hierarchical) system is used to identify all published molecular time estimates bearing on the divergence of two chosen taxa, such as species, compute summary statistics, and present the results.

::DEVELOPER

Blair Hedges, Sudhir Kumar at Arizona and Penn State Universities

:: SCREENSHOTS

:: REQUIREMENTS

iPhone/iPad

:: DOWNLOAD

TimeTree for iPhone/ for iPad

:: MORE INFORMATION

Citation

Kumar S & Hedges SB (2011)
TimeTree2: species divergence times on the iPhone.
Bioinformatics 27:2023-2024

EB-HMM 1.0-1 – Identification of Genes Differentially Expressed across 2 or more Conditions over Time

EB-HMM 1.0-1

:: DESCRIPTION

EB-HMM is a tools for comparing multiple biological conditions with time course microarray data using the Hidden Markov modeling

::DEVELOPER

Kendziorski Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / MacOsX
R package

:: DOWNLOAD

EB-HMM

:: MORE INFORMATION

Citation

Yuan and Kendziorski,
Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions
Journal of the American Statistical Association 101(476): 1323-1332;