MODA – Network Motif Discovery in Biological Networks

MODA

:: DESCRIPTION

MODA is an efficient algorithm for network motif discovery in biological networks.

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 MODA

:: MORE INFORMATION

Citation

Genes Genet Syst. 2009 Oct;84(5):385-95.
MODA: an efficient algorithm for network motif discovery in biological networks.
Omidi S1, Schreiber F, Masoudi-Nejad A.

Dimont – de-novo Motif Discovery tool

Dimont

:: DESCRIPTION

Dimont is a universal tool for de-novo motif discovery. Dimont has successfully been applied to ChIP-seq, ChIP-exo and protein-binding microarray (PBM) data.

::DEVELOPER

Jstacs Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux/  MacOSX
  • Java

:: DOWNLOAD

 Dimont

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Nov;41(21):e197. doi: 10.1093/nar/gkt831. Epub 2013 Sep 20.
A general approach for discriminative de novo motif discovery from high-throughput data.
Grau J1, Posch S, Grosse I, Keilwagen J.

FastMotif – Sequence Motif Discovery

FastMotif

:: DESCRIPTION

FastMotif is a sequence motif discovery algorithm for large DNA datasets, based on spectral methods

::DEVELOPER

FastMotif team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 FastMotif

:: MORE INFORMATION

Citation

Bioinformatics. 2015 Apr 16. pii: btv208.
FastMotif: Spectral Sequence Motif Discovery.
Colombo N, Vlassis N

GRAM 0.6 – Discovery of Gene Modules and Regulatory Networks

GRAM 0.6

:: DESCRIPTION

GRAM (Genetic RegulAtory Modules) identifies modules, collections of genes that share common regulators as well as expression profiles.

::DEVELOPER

the Gifford Laboratory

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Java

:: DOWNLOAD

GRAM

:: MORE INFORMATION

Citation

Nat Biotechnol. 2003 Nov;21(11):1337-42. Epub 2003 Oct 12.
Computational discovery of gene modules and regulatory networks.
Bar-Joseph Z, Gerber GK, Lee TI, Rinaldi NJ, Yoo JY, Robert F, Gordon DB, Fraenkel E, Jaakkola TS, Young RA, Gifford DK.

CNVnator 0.3 – CNV Discovery and Genotyping from Depth of Read Mapping

CNVnator 0.3

:: DESCRIPTION

CNVnator is a tool for Copy number variation (CNV) discovery and genotyping from depth of read mapping.

::DEVELOPER

Gerstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 CNVnator

:: MORE INFORMATION

Citation:

CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing.
Abyzov A, Urban AE, Snyder M, Gerstein M.
Genome Res. 2011 Jun;21(6):974-84. Epub 2011 Feb 7.

GeneNT 1.4.1 – Relevance or Dependency network and Signaling Pathway Discovery

GeneNT 1.4.1

:: DESCRIPTION

GeneNT is a R package to estimate co-expression gene networks

::DEVELOPER

Dongxiao Zhu, Ph.D

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/MacOsX/ Linux
  • R package

:: DOWNLOAD

 GeneNT

:: MORE INFORMATION

Citation:

Network constrained clustering for gene microarray data.
Zhu D, Hero AO, Cheng H, Khanna R, Swaroop A.
Bioinformatics. 2005 Nov 1;21(21):4014-20.

GLFD – Guided Latent Factor Discovery

GLFD

:: DESCRIPTION

GLFD is an exploratory data analysis method. Its purpose is to find latent factors that act in combination with clinical factors to control feature expression.

::DEVELOPER

Tianwei Yu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R

:: DOWNLOAD

GLFD

:: MORE INFORMATION

Citation

BMC Genomics. 2011 Nov 16;12:563. doi: 10.1186/1471-2164-12-563.
Improving gene expression data interpretation by finding latent factors that co-regulate gene modules with clinical factors.
Yu T, Bai Y.

Heinz 2.0 / xHeinz 1.0 – Single / Cross-species Module Discovery

Heinz 2.0 /xHeinz 1.0

:: DESCRIPTION

Heinz is a tool for single-species active module discovery.

xHeinz is a software solver that searches for active subnetwork modules that are conserved between two species. It uses a branch-and-cut algorithm that finds provably optimal or near-optimal solutions.

::DEVELOPER

Centrum Wiskunde & Informatica (CWI)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX

:: DOWNLOAD

Heinz / xHeinz

:: MORE INFORMATION

Citation

xHeinz: An algorithm for mining cross-species network modules under a flexible conservation model.
El-Kebir M, Soueidan H, Hume T, Beisser D, Dittrich M, Müller T, Blin G, Heringa J, Nikolski M, Wessels LF, Klau GW.
Bioinformatics. 2015 May 27. pii: btv316

Hydra 0.5.3 / Hydra-Multi – Structural Variation Discovery with Paired-end-mapping

Hydra 0.5.3 / Hydra-Multi

:: DESCRIPTION

Hydra detects structural variation (SV) breakpoints by clustering discordant paired-end alignments whose “signatures” corroborate the same putative breakpoint. Hydra can detect breakpoints caused by all classes of structural variation. Moreover, it was designed to detect variation in both unique and duplicated genomic regions; therefore, it will examine paired-end reads having multiple discordant alignments.

Hydra-Multi is a paired-end read structural variant discovery tool that is capable of integrating signals from hundreds of samples.

::DEVELOPER

The Quinlan Lab Ira Hall Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  Hydra / Hydra-Multi

:: MORE INFORMATION

Citation:

Population-based structural variation discovery with Hydra-Multi.
Lindberg MR, Hall IM, Quinlan AR.
Bioinformatics. 2014 Dec 2. pii: btu771.

Genome-wide mapping and assembly of structural variant breakpoints in the mouse genome
Aaron R. Quinlan1, Royden A. Clark1, Svetlana Sokolova1, Mitchell L. Leibowitz1, Yujun Zhang2, Matthew E. Hurles2, Joshua C. Mell3 and Ira M. Hall
Genome Res. 2010. 20: 623-635

Lumpy 0.3.0 – Structural Variant Discovery

Lumpy 0.3.0

:: DESCRIPTION

Lumpy is a new probabilistic framework that we have developed to integrate multiple structural variation signals such as discordant paired-end alignments and split-read alignments. While it is clear that integrating all SV signals is important for sensitive discovery, most existing (including our own Hydra) tools only exploit one signal. Lumpy integrates multiple signals in order to improve sensitivity and breakpoint resolution. This is especially important for cancer genome analysis where tumor heterogeneity causes potentially important rearrangements occur with less supporting alignments in the sampled DNA.

::DEVELOPER

The Quinlan Lab ,Ira Hall Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C ++ Compiler
  • the GNU Scientific Library (GSL).

:: DOWNLOAD

  Lumpy

:: MORE INFORMATION

Citation

Layer RM, Quinlan AR, Hall IM,
LUMPY: A probabilistic framework for structural variant discovery.
arXiv:1210.2342v2 [q-bio.GN]