Socrates 0.95 – SOft Clip re-alignment To IdEntify Structural Variants

Socrates 0.95

:: DESCRIPTION

Socrates is a highly efficient and effective method for detecting genomic rearrangements in tumours that utilises split-read data. Socrates features single nucleotide resolution, high sensitivity, and high specificity in simulated data.

::DEVELOPER

WEHI Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Socrates

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jan 22. [Epub ahead of print]
Socrates: identification of genomic rearrangements in tumour genomes by re-aligning soft clipped reads.
Schröder J1, Hsu A, Boyle SE, Macintyre G, Cmero M, Tothill RW, Johnstone RW, Shackleton M, Papenfuss AT.

pdCSM-PPI – Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors

pdCSM-PPI

:: DESCRIPTION

pdCSM-PPI is a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Rodrigues CHM, Pires DEV, Ascher DB.
pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors.
J Chem Inf Model. 2021 Nov 22;61(11):5438-5445. doi: 10.1021/acs.jcim.1c01135. Epub 2021 Nov 1. PMID: 34719929.

V-Phaser 2.0 / V-Profiler 1.0 – Variant Calling and Visualization tools to Identify Biological Mutations in Diverse populations

V-Phaser 2.0 / V-Profiler 1.0

:: DESCRIPTION

V-Phaser is a tool to call variants in genetically heterogeneous populations from ultra-deep sequence data. V-Phaser combines information regarding the covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity.

V-Profiler takes a read alignment and a list of accepted variants at each location in the alignment (such as would be generated by V-Phaser) and analyzes the intra-host diversity of a genome. This can be done at the nucleotide level over the whole sequence, at the codon level for each gene specified in a list, and at the haplotype level for any region delimited (note that the region must not exceed a read length, and is preferably of shorter length such as an epitope or a loop of interest).

::DEVELOPER

Computational R&D, The Broad Institute, Cambridge, MA

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 V-Phaser  / V-Profiler

:: MORE INFORMATION

Citation

BMC Genomics. 2013 Oct 3;14:674. doi: 10.1186/1471-2164-14-674.
V-Phaser 2: variant inference for viral populations.
Yang X, Charlebois P, Macalalad A, Henn MR, Zody MC.

PLoS Comput Biol. 2012;8(3):e1002417. doi: 10.1371/journal.pcbi.1002417. Epub 2012 Mar 15.
Highly sensitive and specific detection of rare variants in mixed viral populations from massively parallel sequence data.
Macalalad AR, Zody MC, Charlebois P, Lennon NJ, Newman RM, Malboeuf CM, Ryan EM, Boutwell CL, Power KA, Brackney DE, Pesko KN, Levin JZ, Ebel GD, Allen TM, Birren BW, Henn MR.

PClouds 1.0 – Identify Repeat Structure in large Eukaryotic Genomes

PClouds 1.0

:: DESCRIPTION

The (PClouds) P-clouds package is designed to identify repeat structure in large eukaryotic genomes using oligonucleotide counts.

::DEVELOPER

Pollock Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • C++ Compiler

:: DOWNLOAD

  PClouds

:: MORE INFORMATION

Citation

W. Gu, T. A. Castoe, D. J. Hedges, M. A. Batzer, and D. D. Pollock,
Identification of repeat structure in large genomes using repeat probability clouds .”
Anal Biochem. 2008 Sep 1;380(1):77-83. Epub 2008 May 20.

Meerkat 0.189 – Identify Structural Variations

Meerkat 0.189

:: DESCRIPTION

Meerkat is designed to identify structure variations (SVs) from paired end high throughput sequencing data. It predicts SVs from discordant read pairs (pairs that mapped to reference genome in unexpected way).

::DEVELOPER

Peter Park’s lab at CBMI, Harvard Medical School

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Meerkat

:: MORE INFORMATION

Citation

Cell. 2013 May 9;153(4):919-29. doi: 10.1016/j.cell.2013.04.010.
Diverse mechanisms of somatic structural variations in human cancer genomes.
Yang L, Luquette LJ, Gehlenborg N, Xi R, Haseley PS, Hsieh CH, Zhang C, Ren X, Protopopov A, Chin L, Kucherlapati R, Lee C, Park PJ.

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.