GeneSetScan 0.021 beta – Scan Genome-wide SNP data for Gene-set Association Analysis

GeneSetScan 0.021 beta

:: DESCRIPTION

GeneSetScan offers a general approach to scan genome-wide SNP data for gene-set association analyses

::DEVELOPER

Bioinformatics Program, Division of Biomedical Statistics and Informatics, Mayo Clinic Research

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 GeneSetScan

:: MORE INFORMATION

Citation

Genet Epidemiol. 2012 Jan;36(1):3-16. doi: 10.1002/gepi.20632. Epub 2011 Dec 7.
Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies.
Schaid DJ1, Sinnwell JP, Jenkins GD, McDonnell SK, Ingle JN, Kubo M, Goss PE, Costantino JP, Wickerham DL, Weinshilboum RM.

SNiPlay v3 – SNP and Polymorphism Analysis

SNiPlay v3

:: DESCRIPTION

SNiPlay is a web-based tool for SNP and polymorphism analysis.From sequencing traces, alignment or allelic data given as input, it detects SNP and insertion/deletion events.

::DEVELOPER

SNiPlay team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

SNiPlay3: a web-based application for exploration and large scale analyses of genomic variations.
Dereeper A, Homa F, Andres G, Sempere G, Sarah G, Hueber Y, Dufayard JF, Ruiz M.
Nucleic Acids Res. 2015 Jun 3. pii: gkv351.

BMC Bioinformatics. 2011 May 5;12:134. doi: 10.1186/1471-2105-12-134.
SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects.
Dereeper A, Nicolas S, Le Cunff L, Bacilieri R, Doligez A, Peros JP, Ruiz M, This P.

dChip 2011.12 – Analysis & Visualization of Gene Expression & SNP Microarrays

dChip 2011.12

:: DESCRIPTION

DNA-Chip Analyzer (dChip) is a Windows software package for probe-level (e.g. Affymetrix platform) and high-level analysis of gene expression microarrays and SNP microarrays.

Gene expression or SNP data from various microarray platforms can also be analyzed by importing as external dataset. At the probe level, dChip can display and normalize the CEL files, and the model-based approach allows pooling information across multiple arrays and automatic probe selection to handle cross-hybridization and image contamination. High-level analysis in dChip includes comparing samples, hierarchical clustering, view expression and SNP data along chromosome, LOH and copy number analysis of SNP arrays, and linkage analysis. In these functions the gene information and sample information are correlated with the analysis results.

::DEVELOPER

Started in Wing Wong Lab , Developed & Maintained by Cheng Li Lab.

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows
  • Linux with Wine
  • Mac with Virtual PC

:: DOWNLOAD

dChip

:: MORE INFORMATION

Please cite Li and Wong 2001a if dChip results are used in manuscripts, and cite Lin et al. 2004 if dChip SNP analysis functions are used.

VanillaICE 1.52.0 – Hidden Markov model for inferring Copy Number Alterations from SNP Arrays

VanillaICE  1.52.0

:: DESCRIPTION

VanillaICE is a Hidden Markov Models for characterizing chromosomal alterations in high throughput SNP arrays

::DEVELOPER

Division of Biostatistics and Bioinformatics – Johns Hopkins University Oncology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ WIndows/ MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 VanillaICE

:: MORE INFORMATION

Citation

Ann Appl Stat. 2008 Jun 1;2(2):687-713.
Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays.
Scharpf RB1, Parmigiani G, Pevsner J, Ruczinski I.

crlmm 1.48.0 – Genotype Calling and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays

crlmm 1.48.0

:: DESCRIPTION

The R package crlmm implements a multilevel model that adjusts for batch effects and provides allele-specific estimates of copy number.

::DEVELOPER

Division of Biostatistics and Bioinformatics – Johns Hopkins University Oncology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ WIndows/ MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 crlmm

:: MORE INFORMATION

Citation

J Stat Softw. 2011 May 1;40(12):1-32.
Using the R Package crlmm for Genotyping and Copy Number Estimation.
Scharpf RB1, Irizarry RA, Ritchie ME, Carvalho B, Ruczinski I.

GEC 1.0 – Address Multiple-testing Issue with dependent SNPs

GEC 1.0

:: DESCRIPTION

The GEC (Genetic Type I error calculator) is a Java-based application developed to address multiple-testing issue with dependent Single-nucleotide polymorphisms (SNPs).  Based on this new measure, several popular multiple-testing methods including Bonferroni, Holm, Simes correction was improved to evaluate significance level of SNP p-values in genome-wide association studies.

::DEVELOPER

Precision Medicine Genomics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • Java

:: DOWNLOAD

 GEC

:: MORE INFORMATION

Citation

Miao-Xin Li, Juilian M.Y. Yeung, Stacey Cherny and Pak C Sham.
Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets.
Hum Genet. 2012 May;131(5):747-56.

Snpdat – A Simple High Throughput Analysis Tool for Annotating SNPs

Snpdat

:: DESCRIPTION

SNPdat (SNP Data Analysis Tool) is a high throughput analysis tool that can provide a comprehensive annotation of both novel and known single nucleotide polymorphisms (SNPs). It is specifically designed for use with organisms which are either not supported by other tools or have a small number of annotated SNPs available, however it can also be used to analyse datasets from organisms which are densely sampled for SNPs. It can be used for analysis of any organism with a draft sequence and annotation. SNPdat makes possible analyses involving non-model organisms that are not supported by the vast majority of SNP annotation tools currently available.

::DEVELOPER

The Creevey Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

SNPdat

:: MORE INFORMATION

Citation

BMC Bioinformatics, 14, 45 2013 Feb 8
Snpdat: Easy and Rapid Annotation of Results From De Novo Snp Discovery Projects for Model and Non-Model Organisms
Anthony G Doran , Christopher J Creevey

SATlotyper 0.1.5 – Haplotype Inference from unphased SNP data in Heterozygous Polyploids based on SAT

SATlotyper 0.1.5

:: DESCRIPTION

 SATlotyper is a software tool designed for inferring haplotypes from polyploid and polyallelic unphased SNP data.

::DEVELOPER

Max Planck Institute for Molecular Plant Physiology

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/MacOsX/Windows
  • Java

:: DOWNLOAD

  SATlotyper

:: MORE INFORMATION

Citation

Haplotype inference from unphased SNP data in heterozygous polyploids based on SAT.
Neigenfind J, Gyetvai G, Basekow R, Diehl S, Achenbach U, Gebhardt C, Selbig J, Kersten B.
BMC Genomics. 2008 Jul 30;9:356.

VarGeno v1.0.3 – SNP Genotyping from Whole Genome Sequencing data

VarGeno v1.0.3

:: DESCRIPTION

VarGeno is a method for SNP genotyping from Illumina whole genome sequencing data. VarGeno builds upon LAVA by improving the speed of k-mer querying as well as the accuracy of the genotyping strategy.

::DEVELOPER

Medvedev Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

VarGeno

:: MORE INFORMATION

Citation:

Bioinformatics, 35 (3), 415-420 2019 Feb 1
Toward Fast and Accurate SNP Genotyping From Whole Genome Sequencing Data for Bedside Diagnostics
Chen Sun , Paul Medvedev

Syzygy 1.2.7 – SNP and Indel Calling for pooled and individual Targeted Resequencing Studies

Syzygy 1.2.7

:: DESCRIPTION

Syzygy is a targeted sequencing post processing analysis tool that allows: 1. SNP and indel detection; 2. Allele frequency estimation; 3. Single-marker association test; 4. Group-wise marker test association; 5. Experimental QC summary (%dbSNP, Ts/Tv, Ns/S); 6. Power to detect variant.

::DEVELOPER

Rivas Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  Syzygy

:: MORE INFORMATION

Citation

Rivas et al. (2011),
Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease“,
Nature Genetics 43, 1066–1073 (2011)