Tag: partition

MOSAIC 1.1 – GO network Annotation and Partition in Cytoscape

MOSAIC 1.1

:: DESCRIPTION

Mosaic performs network annotation and interactive partitioning driven by the Gene Ontology. The Mosaic algorithm works by first annotating the network with GO terms, followed by partitioning the network into a series of subnetworks based on the biological process annotation of nodes.

::DEVELOPER

the National Resource for Network Biology (NRNB)

:: SCREENSHOTS

MOSAIC

:: REQUIREMENTS

  • Linux / MacOsX  /Windows
  • Java
  • Cytoscape

:: DOWNLOAD

 MOSAIC

:: MORE INFORMATION

Citation:

Bioinformatics. 2012 Jul 15;28(14):1943-4. doi: 10.1093/bioinformatics/bts278. Epub 2012 May 9.
Mosaic: making biological sense of complex networks.
Zhang C1, Hanspers K, Kuchinsky A, Salomonis N, Xu D, Pico AR.

Partition 2.0 / PartitionML / PartitionView – maximum likelihood / Identifying Population Sub-division & Assigning Individuals to Populations

Partition 2.0 / PartitionML / PartitionView

:: DESCRIPTION

Partition is a model-based statistical software package for identifying population sub-division and assigning individuals to populations, on the basis of their genotypes at co-dominant marker loci. The underlying population genetic model is appropriate for out-crossing diploid organisms.

PartitionML is a program that searches for the best possible partition of a sample into independent panmictic clusters and simultaneously assign individuals to them using a maximum likelihood (ML) criterion.

PartitionView will read the  partition output file, as it is being written, and will display the path of the Markov chain (see below) as it evolves in real time.

::DEVELOPER

Partition team

:: SCREENSHOTS

Partition

PartitionView

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 Partition  / PartitionML

:: MORE INFORMATION

Citation

Heredity (Edinb). 2009 Jul;103(1):32-45. doi: 10.1038/hdy.2009.29. Epub 2009 Apr 1.
An agglomerative hierarchical approach to visualization in Bayesian clustering problems.
Dawson KJ1, Belkhir K.

Heredity (Edinb). 2002 Jul;89(1):27-35.
Heterozygote deficiencies in small lacustrine populations of brook charr Salvelinus Fontinalis Mitchill (Pisces, Salmonidae): a test of alternative hypotheses.
Castric V1, Bernatchez L, Belkhir K, Bonhomme F.

BaCoCa 1.1 – Heuristic software tool for Parallel Assessment of Sequence Biases in hundreds of Gene and Taxon Partitions

BaCoCa 1.1

:: DESCRIPTION

BaCoCa (BAse COmposition CAlculator) is a user-friendly software that combines multiple statistical approaches (like RCFV and C value calculations) to identify biases in aligned sequence data which potentially mislead phylogenetic reconstructions.

::DEVELOPER

Centre for Molecular Biodiversity Research (ZMB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX/Windows
  • Perl
  • R

:: DOWNLOAD

 BaCoCa

:: MORE INFORMATION

Citation

Mol Phylogenet Evol. 2014 Jan;70:94-8. doi: 10.1016/j.ympev.2013.09.011. Epub 2013 Sep 25.
BaCoCa–a heuristic software tool for the parallel assessment of sequence biases in hundreds of gene and taxon partitions.
Kück P1, Struck TH.

HEIDI 0.1 – Partition the total Heritability into the Contributions of Genomic Regions

HEIDI 0.1

:: DESCRIPTION

HEIDI (Heritability EstImations DIstributed) is a linear mixed model based approach to partition the total heritability into the contributions of genomic regions.

::DEVELOPER

ZarLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 HEIDI

:: MORE INFORMATION

Citation

Am J Hum Genet. 2013 Apr 4;92(4):558-64. doi: 10.1016/j.ajhg.2013.03.010.
Improving the accuracy and efficiency of partitioning heritability into the contributions of genomic regions.
Kostem E, Eskin E.

MonoClad – Find meaningful partitions using Semi-supervised Class Discovery

MonoClad

:: DESCRIPTION

MonoClaD (Monotone Class Discovery) finds meaningful partitions using semi-supervised class discovery.

::DEVELOPER

Laboratory of Computational Biology at the Technion.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

 MonoClaD

:: MORE INFORMATION

Citation

Clinically driven semi-supervised class discovery in gene expression data.
Steinfeld I, Navon R, Ardigò D, Zavaroni I, Yakhini Z.
Bioinformatics. 2008 Aug 15;24(16):i90-7.