TreeQTL 2.0 – Hierarchical error control for eQTL studies

TreeQTL 2.0

:: DESCRIPTION

TreeQTL implements a hierarchical multiple testing procedure which allows control of appropriate error rates defined relative to a grouping of the eQTL hypotheses.

::DEVELOPER

Christine B. Peterson (cbpeterson@gmail.com)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOSX / Linux
  • R package

:: DOWNLOAD

TreeQTL

:: MORE INFORMATION

Citation

Peterson CB, Bogomolov M, Benjamini Y, Sabatti C.
TreeQTL: hierarchical error control for eQTL findings.
Bioinformatics. 2016 Aug 15;32(16):2556-8. doi: 10.1093/bioinformatics/btw198. Epub 2016 Apr 19. PMID: 27153635; PMCID: PMC4978936.

Volume Measures – Volume Measures for Linkage Disequilibrium

Volume Measures

:: DESCRIPTION

Volume Measures is the C code for calculating a number of volume measures of linkage disequilibrium.

::DEVELOPER

Yuguo Chen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOSX / Linux
  • C Compiler

:: DOWNLOAD

 Volume Measures

:: MORE INFORMATION

Citation

Chen, Y., C. Lin, C. Sabatti (2006)
Volume Measures for Linkage Disequilibrium,
BMC Genetics 7:54.

Piet 0.1.0 – DNA CNV Analysis tools based on fused Lasso type of Model

Piet 0.1.0

:: DESCRIPTION

Piet provides some segmentation tools, using fused lasso, group fused lasso and generalized fused lasso (GFL), for analysis of individual or multiple sequences of CNV data.

::DEVELOPER

Piet team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOSX / Linux
  • R package

:: DOWNLOAD

 Piet

:: MORE INFORMATION

Citation

Z. Zhang, K. Lange, C. Sabatti (2012)
Reconstructing DNA copy number by joint segmentation of multiple sequences

EMMAX Beta – Efficient Mixed-Model Association eXpedited

EMMAX Beta

:: DESCRIPTION

EMMAX is a statistical test for large scale human or model organism association mapping accounting for the sample structure. In addition to the computational efficiency obtained by EMMA algorithm, EMMAX takes advantage of the fact that each loci explains only a small fraction of complex traits, which allows us to avoid repetitive variance component estimation procedure, resulting in a significant amount of increase in computational time of association mapping using mixed model.

::DEVELOPER

Hyun Min Kang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 EMMAX

:: MORE INFORMATION

Citation

Nat Genet. 2010 Apr;42(4):348-54. Epub 2010 Mar 7.
Variance component model to account for sample structure in genome-wide association studies.
Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, Sabatti C, Eskin E.

MEGA-V – Mutation Enrichment Gene set Analysis of Variants

MEGA-V

:: DESCRIPTION

MEGA-V is an open-source R application with a Shiny web interface. It identifies gene sets with a significantly higher number of variants in a cohort of interest (cohort A) as compared to (1) a control cohort (cohort B) or (2) a random distribution generated using Monte Carlo.

::DEVELOPER

the Ciccarelli Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux
  • R

:: DOWNLOAD

MEGA-V

:: MORE INFORMATION

Citation:

Gambardella G, Cereda M, Benedetti L, Ciccarelli FD.
MEGA-V: detection of variant gene sets in patient cohorts.
Bioinformatics. 2017 Apr 15;33(8):1248-1249. doi: 10.1093/bioinformatics/btw809. PMID: 28003259; PMCID: PMC5408849.

HARSH 0.21 – Haplotype Inference using Reference and Sequencing Data

HARSH 0.21

:: DESCRIPTION

HARSH (HAplotype inference using Reference and Sequencing tecHnology) is a method to infer the haplotype using haplotype reference panel and high throughput sequencing data. It is based on a novel probabilistic model and Gibbs sampler method.

::DEVELOPER

ZarLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Python

:: DOWNLOAD

 HARSH

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Sep 15;29(18):2245-52. doi: 10.1093/bioinformatics/btt386.
Leveraging reads that span multiple single nucleotide polymorphisms for haplotype inference from sequencing data.
Yang WY, Hormozdiari F, Wang Z, He D, Pasaniuc B, Eskin E.

GraphIBD 0.1.0 – Fast IBD Association Testing given Genome-wide SNP data

GraphIBD 0.1.0

:: DESCRIPTION

GraphIBD is a free, open-source IBD association testing software for genome-wide association study analysis. GraphIBD requires an IBD detection method such as Beagle FastIBD to run first. Then GraphIBD builds upon the IBD information to test if the IBD segments show association to the traits.

::DEVELOPER

Buhm Han

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX/Windows
  • Java

:: DOWNLOAD

 GraphIBD

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Dec 13.
Fast pairwise IBD association testing in genome-wide association studies.
Han B, Kang EY, Raychaudhuri S, de Bakker PI, Eskin E.

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.

SNPYGoat 1.0 – Identify Several Goat Y-chromosomal Haplotypes

SNPYGoat 1.0

:: DESCRIPTION

SNPYGoat Software allows users of the SNPYGoat multiplex system to rapidly identify several goat Y-chromosomal haplotypes  Y1A, Y1B, Y1C and Y2 by automatically comparing the obtained profile with a reference database.

::DEVELOPER

Filipe Pereira

:: SCREENSHOTS

: REQUIREMENTS

  • Windows

:: DOWNLOAD

SNPYGoat

:: MORE INFORMATION

Citation

Pereira F, Carneiro J, Soares P, Maciel S, Nejmeddine F, Lenstra JA, Gusm?o L, Amorim A
A multiplex primer extension assay for the rapid identification of paternal lineages in domestic goat (Capra hircus): laying the foundations for a detailed caprine Y chromosome phylogeny
Molecular Phylogenetics and Evolution. 2008. 49:663-668