pClust 1.0 – Parallel Identification of Dense Protein Clusters

pClust 1.0

:: DESCRIPTION

 PClust is a scalable parallel software for detecting dense subgraphs.

::DEVELOPER

Ananth Kalyanaraman

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Compiler

:: DOWNLOAD

 pClust

:: MORE INFORMATION

Citation

C. Wu, A. Kalyanaraman.
An efficient parallel approach for identifying protein families in large-scale metagenomic data sets.
Proc. ACM/IEEE Supercomputing Conference (SC’08), Austin, TX, November 15-21. pp. 1-10. 2008

AdaPatch 1.1 – Searche for Dense and Spatially Distinct Clusters of sites

AdaPatch 1.1

:: DESCRIPTION

AdaPatch searches for dense and spatially distinct clusters of sites under positive selection on the surface of proteins.

::DEVELOPER

Algorithmic Bioinformatics, Heinrich-Heine-Universität Düsseldorf

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  AdaPatch

:: MORE INFORMATION

Citation

Christina Tusche, Lars Steinbrück and Alice C. McHardy
Detecting Patches of Protein Sites of Influenza A Viruses under Positive Selection
Mol Biol Evol (2012) 29 (8): 2063-2071.

fineSTRUCTURE 4.0.1 – Identify Population Structure using Dense Sequencing Data

fineSTRUCTURE 4.0.1

:: DESCRIPTION

fineSTRUCTURE is a fast and powerful algorithm for identifying population structure using dense sequencing data.  By using the output of ChromoPainter as a (nearly) sufficient summary statistic, it is able to perform model-based Bayesian clustering on large datasets, including full resequencing data, and can handle up to 1000s of individuals.

::DEVELOPER

Daniel Lawson

:: SCREENSHOTS

fineSTRUCTURE

:: REQUIREMENTS

  • Linux / Windows with  MinGW/ MacOsX

:: DOWNLOAD

  fineSTRUCTURE

:: MORE INFORMATION

Citation

Lawson, Hellenthal, Myers, and Falush (2012),
Inference of population structure using dense haplotype data“,
PLoS Genetics, 8 (e1002453).

SMK 2011v4 – Mining Low-support Discriminative Patterns from Dense and High-dimensional Data

SMK 2011v4

:: DESCRIPTION

SMK (SupMaxK , a family of anti-monotonic measures) aims at the efficient discovery of discriminative patterns from biological data with high density and high dimensionality (e.g. Gene Expression data, and SNP data), and especially for the discovery of those patterns with relatively low-support but high discriminative power (e.g. odds ratio, information gain, p-value etc), which complements existing discriminative pattern mining algorithms.

::DEVELOPER

Data mining for biomedical informatics at the UMN

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Matlab

:: DOWNLOAD

 SMK

:: MORE INFORMATION

Citation

Gang Fang, Gaurav Pandey, Wen Wang, Manish Gupta, Michael Steinbach and Vipin Kumar
Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data
IEEE Transaction on Knowledge and Data Engineering (TKDE).2012 (vol. 24 no. 2)pp. 279-294