tigre 1.46.0 / tigreBrowser 1.1 – Transcription Factor Inference through Gaussian process Reconstruction of Expression

tigre 1.46.0 / tigreBrowser 1.1

:: DESCRIPTION

tigre is an R/Bioconductor package for inference of transcription factor activity and ranking candidate target genes from gene expression time series.

tigreBrowser is a web-based browser for tigre ranking results. It allows easy viewing, sorting and filtering of visualisations of models produced by tigre.

:: DEVELOPER

Antti Honkela

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / windows/ MacOsX
BioCOnductor
R package
Python

:: DOWNLOAD

tigre / tigreBrowser

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Apr 1;27(7):1026-7. doi: 10.1093/bioinformatics/btr057. Epub 2011 Feb 7.
tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor.
Honkela A1, Gao P, Ropponen J, Rattray M, Lawrence ND.

unifiedWMWqPCR 1.28.0 – Unified Wilcoxon-Mann Whitney Test for testing differential Expression in qPCR data

unifiedWMWqPCR 1.28.0

:: DESCRIPTION

unifiedWMWqPCR implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data.

::DEVELOPER

Jan R. De Neve & Joris Meys <Joris.Meys at UGent.be>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
R package
BioConductor

:: DOWNLOAD

unifiedWMWqPCR

:: MORE INFORMATION

Citation

unifiedWMWqPCR: the unified Wilcoxon-Mann-Whitney test for analyzing RT-qPCR data in R.
De Neve J, Meys J, Ottoy JP, Clement L, Thas O.
Bioinformatics. 2014 May 2. pii: btu313.

CEM 0.9.1 – Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads

CEM 0.9.1

:: DESCRIPTION

CEM is an algorithm to assemble transcripts and estimate their expression levels from RNA-Seq reads.

::DEVELOPER

Wei Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

CEM

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Nov 15;28(22):2914-21. doi: 10.1093/bioinformatics/bts559. Epub 2012 Oct 11.
Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads.
Li W1, Jiang T.

biRte 1.18.0 – Bayesian Inference of Regulatory Influence on Expression

biRte 1.18.0

:: DESCRIPTION

biRte is an software for bayesian prediction of TF and miRNA activities from different expression data entities; interfaces nem for gene regulatory network inference.

::DEVELOPER

biRte team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows/ MacOsX
R
BioConductor

:: DOWNLOAD

biRte

:: MORE INFORMATION

Citation

Bioinformatics. 2015 Jun 25. pii: btv379.
biRte: Bayesian Inference of Context Specific Regulator Activities and Transcriptional Networks.
Fröhlich H

PPIXpress 1.23 – Specific Protein Interaction Networks from Transcript Expression

PPIXpress 1.23

:: DESCRIPTION

PPIXpress exploits expression data at the transcript-level and is thus able to also reveal alterations in protein connectivity caused by alternative splicing.

::DEVELOPER

PPIXpress team

:: SCREENSHOTS

:: REQUIREMENTS

Linux / MacOsX / Windows
Java

:: DOWNLOAD

PPIXpress

:: MORE INFORMATION

Citation

PPIXpress: construction of condition-specific protein interaction networks based on transcript expression.
Will T, Helms V.
Bioinformatics. 2015 Oct 27. pii: btv620.

PERT 1.0 – Expression Deconvolution under Varied Environmental Conditions

PERT 1.0

:: DESCRIPTION

The PERT algorithm is designed for expression deconvolution of samples of mixed cell types, where the expression profiles of most of the individual cell types are available and we simply want to estimate the proportion of each mixed sample that is attributable to each individual cell type.

::DEVELOPER

QUON-TITATIVE BIOLOGY LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux /
Octave

:: DOWNLOAD

PERT

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2012;8(12):e1002838. doi: 10.1371/journal.pcbi.1002838. Epub 2012 Dec 20.
PERT: a method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions.
Qiao W1, Quon G, Csaszar E, Yu M, Morris Q, Zandstra PW.

DGEclust 20171006 – Hierarchical non-parametric Bayesian Clustering of Digital Expression data

DGEclust 20171006

:: DESCRIPTION

DGEclust is a program for clustering and differential expression analysis of expression data generated by next-generation sequencing assays, such as RNA-seq, CAGE and others.

::DEVELOPER

Dimitris Vavoulis

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

DGEclust

:: MORE INFORMATION

Citation:

Genome Biol. 2015 Feb 20;16:39. doi: 10.1186/s13059-015-0604-6.
DGEclust: differential expression analysis of clustered count data.
Vavoulis DV, Francescatto M, Heutink P, Gough J.

Periscope 20150806 – A Predictor for Soluble Protein Expression in the Periplasm of Escherichia coli

Periscope 20150806

:: DESCRIPTION

Constructed based on the rising popularity of periplasmic expression, Periscope is a sequence-based predictor with a two-stage architecture that estimates the expression level and yield of soluble protein in the periplasm of E. coli.

::DEVELOPER

Periscope team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux
Java

:: DOWNLOAD

Periscope

:: MORE INFORMATION

Citation

Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli.
Chang CC, Li C, Webb GI, Tey B, Song J, Ramanan RN.
Sci Rep. 2016 Mar 2;6:21844. doi: 10.1038/srep21844.

miRlastic 1.0 – Integrative Analysis of miRNA and mRNA Expression data

miRlastic 1.0

:: DESCRIPTION

miRlastic is a novel method which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network.

::DEVELOPER

Computational Cell Maps Group

:: SCREENSHOTS

N/a

:: REQUIREMENTS

Windows/ Linux
R

:: DOWNLOAD

miRlastic

:: MORE INFORMATION

Citation

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma.
Sass S, Pitea A, Unger K, Hess J, Mueller NS, Theis FJ.
Int J Mol Sci. 2015 Dec 18;16(12):30204-22. doi: 10.3390/ijms161226230.

GeneTIER – Expression-based Candidate Gene Prioritisation

GeneTIER

:: DESCRIPTION

GeneTIER replaces the knowledge-based inference traditionally used in candidate disease gene prioritization, instead using experimental data from tissue-specific gene expression datasets.

::DEVELOPER

Ian’s DNA@Leeds

:: SCREENSHOTS

N/a

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

GeneTIER: prioritization of candidate disease genes using tissue-specific gene expression profiles.
Antanaviciute A, Daly C, A Crinnion L, F Markham A, M Watson C, T Bonthron D, M Carr I.
Bioinformatics. 2015 Apr 9. pii: btv196.