Tag: Identify

BreakFusion 1.0.1 – Identify Gene Fusions from paired-end RNA-Seq data

BreakFusion 1.0.1

:: DESCRIPTION

BreakFusion is a software that combines the strength of reference alignment followed by read-pair analysis and de novo assembly to achieve a good balance in sensitivity, specificity and computational efficiency.

::DEVELOPER

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

 BreakFusion

:: MORE INFORMATION

Citation:

Ken Chen et al.
BreakFusion: targeted assembly-based identification of gene fusions in whole transcriptome paired-end sequencing data
Bioinformatics (2012) 28 (14): 1923-1924.

Screen & Clean – Software for Identifying Genome-Wide Associations

Screen & Clean

:: DESCRIPTION

Screen & Clean is a program that identifies associations between SNP allele count data and a continuous or binary phenotype. The core function is a screen that identifies the first K SNPs to enter an L1-penalized regression of the phenotype on the allele counts, where K is chosen by a stability criterion. The program includes several optional procedures that are turned off by default, including a pre-screen using marginal regression p-values, a second screen for pairwise interaction effects, and a multivariate regression clean of the screened SNPs. K may also be chosen directly by the user.

::DEVELOPER

The Devlin lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 Screen & Clean

:: MORE INFORMATION

Citation:

Wu, Devlin, Ringquist, Trucco, and Roeder
“Screen and Clean: A Tool for Identifying Interactions in Genome-Wide Association Studies”
Genet Epidemiol. 2010 April; 34(3): 275–285.

COMODO 2.0 – Identify Conserved Coexpression Modules between Organisms

COMODO 2.0

:: DESCRIPTION

COMODO (COnserved MODules across Organisms) is a coclustering procedure to identify conserved expression modules between two species. The method uses as input microarray data and a gene homology map and provides as output pairs of conserved modules and searches for the pair of modules for which the number of sharing homologs is statistically most significant relative to the size of the linked modules.

::DEVELOPER

Kathleen Marchal

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 COMODO

:: MORE INFORMATION

Citation

COMODO: an adaptive coclustering strategy to identify conserved coexpression modules between organisms.
Zarrineh P, Fierro AC, Sánchez-Rodríguez A, De Moor B, Engelen K, Marchal K.
Nucleic Acids Res. 2011 Apr;39(7):e41. Epub 2010 Dec 10.

BoBro 2.1 – Identifying cis Regulatory Motifs in Prokaryotes

BoBro 2.1

:: DESCRIPTION

BoBro (BOTTLENECK BROKEN TOOL) is a software  for prediction of cis-regulatory motifs in a given set of promoter sequences.

::DEVELOPER

Qin Ma  , Bioinformatic and Mathematical Biosciences Lab, The Ohio State University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOs/ Windows

:: DOWNLOAD

 BoBro

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Sep 15;29(18):2261-8. doi: 10.1093/bioinformatics/btt397. Epub 2013 Jul 10.
An integrated toolkit for accurate prediction and analysis of cis-regulatory motifs at a genome scale.
Ma Q1, Liu B, Zhou C, Yin Y, Li G, Xu Y.

Nucleic Acids Res. 2011 Apr;39(7):e42. Epub 2010 Dec 11.
A new framework for identifying cis-regulatory motifs in prokaryotes.
Li G, Liu B, Ma Q, Xu Y.

DDN – Identify Condition-specific Topological Changes in Biological Networks

DDN

:: DESCRIPTION

DDN (Differential Dependence Networks) is a software of analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. We propose a local dependency model to represent the local structures of a network by a set of conditional probabilities.

::DEVELOPER

Computational Bioinformatics & Bio-imaging Laboratory (CBIL)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  DDN

:: MORE INFORMATION

Citation:

Bioinformatics. 2011 Apr 1;27(7):1036-8. doi: 10.1093/bioinformatics/btr052. Epub 2011 Feb 3.
DDN: a caBIG® analytical tool for differential network analysis.
Zhang B1, Tian Y, Jin L, Li H, Shih IeM, Madhavan S, Clarke R, Hoffman EP, Xuan J, Hilakivi-Clarke L, Wang Y.

Differential dependency network analysis to identify condition-specific topological changes in biological networks.
Zhang B, Li H, Riggins RB, Zhan M, Xuan J, Zhang Z, Hoffman EP, Clarke R, Wang Y.
Bioinformatics. 2009 Feb 15;25(4):526-32. Epub 2008 Dec 26.

MixDB 1.0r – Identify Mixture MS/MS Spectra from more than one Peptide

MixDB 1.0r

:: DESCRIPTION

MixDB is a database search tool that able to identify mixture MS/MS spectra from more than one peptide.

::DEVELOPER

CCMS The Center for Computational Mass Spectrometry

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/windows
  • Java
  • Perl

:: DOWNLOAD

   MixDB

:: MORE INFORMATION

Citation:

Peptide identification by database search of mixture tandem mass spectra.
Wang, J., Bourne, P. E., Bandeira, N.
Mol. Cell. Proteomics, 2011

CMDS 1.0 – Identify Recurrent DNA Copy Number Changes

CMDS 1.0

:: DESCRIPTION

CMDS (Correlation Matrix Diagonal Segmentation ) is a R & C programs for DNA copy number analysis: current copy number aberration indentification in multiple samples (with no need of single-sample calling). Developed for a quick analysis of high resolution and large population data.

::DEVELOPER

Qunyuan Zhang  (qunyuan@wustl.edu)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  CMDS

:: MORE INFORMATION

Citation:

CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data
Qunyuan Zhang, Li Ding, David E. Larson, Daniel C. Koboldt, Michael D. McLellan, Ken Chen, Xiaoqi Shi, Aldi Kraja, Elaine R. Mardis, Richard K. Wilson, Ingrid B. Boreki and Michael A. Province
Bioinformatics (2009)doi: 10.1093/bioinformatics/btp

CCH 20141012 – Identify Genomic Regions of Shared Ancestry

CCH 20141012

:: DESCRIPTION

CCH (Combinatorial Conflicting Homozygosity) uses dense Single Nucleotide Polymorphism (SNP) genotypes to identify regions of the genome inherited from a common ancestor among any or all subsets of a group. Analysis is rapid and can identify loci containing genes for dominant traits. CCH is robust to the presence of phenocopies and can detect undisclosed shared common ancestry.

::DEVELOPER

CCH team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux/ MacOsX
  • Python

:: DOWNLOAD

 CCH

:: MORE INFORMATION

Citation

Combinatorial Conflicting Homozygosity (CCH) analysis enables the rapid identification of shared genomic regions in the presence of multiple phenocopies.
Levine AP, Connor TM, Oygar DD, Neild GH, Segal AW, Maxwell PH, Gale DP.
BMC Genomics. 2015 Mar 10;16:163. doi: 10.1186/s12864-015-1360-4.

TSDfinder – Identify Transposon Boundaries

TSDfinder

:: DESCRIPTION

TSDfinder is a program that defines the boundaries of a retrotransposed element based on the presence of TSDs (target site duplications). The TSDfinder program consists of several steps, including merging and determining the boundaries of a retrotransposed element, obtaining sequences surrounding the retrotransposed element, detecting potential TSDs, and scoring TSDs.

::DEVELOPER

 TDSfinder

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 TSDfinder 

:: MORE INFORMATION

Citation:

Szak ST, Pickeral OK, Makalowski W, Boguski MS, Landsman D, Boeke JD.
Molecular archeology of L1 insertions in the human genome
Genome Biol. 2002; 3(10): research0052.1–research0052.18.

Diametrical Clustering – Identify Anti-correlated Gene Clusters

Diametrical Clustering

:: DESCRIPTION

Diametrical clustering is a software that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i) re-partitioning the genes and (ii) computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a ‘diametric’ cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method.

::DEVELOPER

Usman Roshan

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Diametrical Clustering

:: MORE INFORMATION

Citation

I. S. Dhillon, E. M. Marcotte, U. Roshan,
“Diametrical Clustering for identifying anti- correlated gene clusters“,
Bioinformatics, 19, pp 1612-1619, 2003