rsgcc 1.0.6 – Gini methodology-based correlation and Clustering analysis of Microarray and RNA-Seq Gene Expression data

rsgcc 1.0.6

:: DESCRIPTION

rsgcc is an R package that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses.

::DEVELOPER

Ma Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
R package

:: DOWNLOAD

rsgcc

:: MORE INFORMATION

Citation

Plant Physiol. 2012 Sep;160(1):192-203. doi: 10.1104/pp.112.201962. Epub 2012 Jul 13.
Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis.
Ma C1, Wang X.

CoDiDi – Correlation between Diversity and Diferentiation

CoDiDi

:: DESCRIPTION

CoDiDi is program for estimating Gst, Hs and their correlation coefficient from marker data. It can be used to detect the presence or absence of mutational effect on the marker based genetic differentiation statistic Gst.

:: DEVELOPER

Dr Jinliang Wang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

CoDiDi

:: MORE INFORMATION

Citation

Wang J.
Does GST underestimate genetic differentiation from marker data?
Mol Ecol. 2015 Jul;24(14):3546-58. doi: 10.1111/mec.13204. Epub 2015 May 14. PMID: 25891752.

Correlation Finder – Seek Correlations between Nucleotides in Genomic Sequences

Correlation Finder

:: DESCRIPTION

Correlation Finder is a free software which allows to seek correlations between nucleotides in genomic sequences. It computes some parameters for each analyzed correlation: the relative abundance, the conditional probability, the frequency of the words making up the correlation.

::DEVELOPER

Gruppo di Biologia Computazionale

:: SCREENSHOTS

:: REQUIREMENTS

Windows

:: DOWNLOAD

Correlation Finder

:: MORE INFORMATION

Citation:

In Silico Biol. 2005;5(5-6):465-8.
Correlation Finder.
Piva F1, Principato G.

NICE – Next-generation Intersample Correlation Emended

NICE

:: DESCRIPTION

NICE is a statistical test for correcting for expression heterogeneity inherent in expression dataset due to confounding from unmodeled factors. NICE estimates inter-sample correlation structure using only the genes with confounding effects and incorporates it as signatures of the systematic confounding effects to correct for it.

:: DEVELOPER

NICE team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / WIndows /MacOsX
R
Java

:: DOWNLOAD

NICE

:: MORE INFORMATION

Citation

Joo JW, Sul JH, Han B, Ye C, Eskin E.
Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies,
Genome Biology, 15:r61, 2014.

C3 – Correlation Clustering method for Cancer Mutation analysis

C3

:: DESCRIPTION

C3 (Cancer Correlation Clustering) identifies cancer mutation patterns from patient cohort by leveraging mutual exclusivity of mutations, patient coverage and driver network concentration principles.

::DEVELOPER

Ma Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
Python

:: DOWNLOAD

:: MORE INFORMATION

Citation

A new correlation clustering method for cancer mutation analysis.
Hou JP, Emad A, Puleo GJ, Ma J, Milenkovic O.
Bioinformatics. 2016 Dec 15;32(24):3717-3728.

MuCor – Mutation Aggregation and Correlation

MuCor

:: DESCRIPTION

MuCor is software to aggregate variant information sourced from multiple VCF files (and some others, see below) into a variety of summary files with varying levels of detail and statistics.

::DEVELOPER

blachly lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/Windows/MacOsX
Python

:: DOWNLOAD

MuCor

:: MORE INFORMATION

Citation:

MuCor: Mutation Aggregation and Correlation.
Kroll KW, Eisfeld AK, Lozanski G, Bloomfield CD, Byrd JC, Blachly JS.
Bioinformatics. 2016 Jan 23. pii: btw028

ChIPCor 1.1.0 – Measuring the Spatial Correlations of Protein Binding Sites

ChIPCor 1.1.0

:: DESCRIPTION

ChIPCor is a testing procedure for evaluating the significance of overlapping from a pair of proteins by leveraging information from publicly available data.

::DEVELOPER

ChIPCor team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
R

:: DOWNLOAD

ChIPCor

:: MORE INFORMATION

Citation:

Measuring the spatial correlations of protein binding sites.
Wei Y, Wu H.
Bioinformatics. 2016 Feb 9. pii: btw058.

SigniSite 2.1 – Residue level Genotype Phenotype Correlation in Protein Multiple Sequence Alignments

SigniSite 2.1

:: DESCRIPTION

SigniSite performs residue level genotype phenotype correlation in protein multiple sequence alignments by identifying amino acid residues significantly associated with the phenotype of the data set.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W286-91. doi: 10.1093/nar/gkt497. Epub 2013 Jun 12.
SigniSite: Identification of residue-level genotype-phenotype correlations in protein multiple sequence alignments.
Jessen LE1, Hoof I, Lund O, Nielsen M.

CCLasso – Correlation Inference for Compositional Data through Lasso

CCLasso

:: DESCRIPTION

CCLasso is a novel method based on least squares with ℓ1 penalty to infer the correlation network for latent variables of compositional data from metagenomic data

::DEVELOPER

Fang Huaying (hyfang@pku.edu.cn) , Minghua Deng

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
R

:: DOWNLOAD

CCLasso

:: MORE INFORMATION

Citation

CCLasso: Correlation Inference for Compositional Data through Lasso.
Fang H, Huang C, Zhao H, Deng M.
Bioinformatics. 2015 Jun 4. pii: btv349.

CorNet 2.01 – Correlation Network Graphs

CorNet 2.01

:: DESCRIPTION

CorNet is a R-script largest correlation network graphs for visualizing clusters of intercorrelated genes/transcripts

::DEVELOPER

Bioinformatics at AWI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/MacOsX / Windows
R Package

:: DOWNLOAD

CorNet

:: MORE INFORMATION

Citation

Hüning AK, Melzner F, Thomsen J, Gutowska MA, Krämer L, Frickenhaus S, Rosenstiel P, Pörtner HO, Philipp E, Lucassen M
Impacts of seawater acidification on mantle gene expression patterns of the Baltic Sea blue mussel: implications for shell formation and energy budget. (2012),
Marine Biology . August 2013, Volume 160, Issue 8, pp 1845-1861 doi: 10.1007/s00227-012-1930-9