dose2geno – Generate Best Guess Genotypes from Dosage and Info File

CalcMatch compares two sets of pedigree files. It was initially written to compare imputed genotypes with their true/experimental counterpart but can be used to compare the concordance between any two sets of pedigree files. The input data are in standard Merlin/QTDT format

::DEVELOPER

Yun Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
C Compiler

:: DOWNLOAD

CalcMatch

:: MORE INFORMATION

GBIRP – Genotype-Based Identification of Relative Pairs

GBIRP

:: DESCRIPTION

GBIRP ( Genotype-Based Identification of Relative Pairs)is a program to compare the genotypes of many pairs of people, and identify pairs of people who may be related to each other.

::DEVELOPER

Jim Stankovich (stankovich@wehi.edu.au) @ WEHI Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Perl

:: DOWNLOAD

GBIRP

:: MORE INFORMATION

Citation:

Hum Genet. 2005 Jul;117(2-3):188-99
Identifying nineteenth century genealogical links from genotypes.
Stankovich J, Bahlo M, Rubio JP, Wilkinson CR, Thomson R, Banks A, Ring M, Foote SJ, Speed TP.

seqEM 1.0 – Genotype Calling Algorithm for Resequencing Data

seqEM 1.0

:: DESCRIPTION

SeqEM is a genotype calling algorithm for next-generation sequence data.SeqEM offers an improved, robust and flexible genotype-calling approach that can be widely applied in the next-generation sequencing studies.

:DEVELOPER

Hussman Institute for Human Genomics, University of Miami

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows

:: DOWNLOAD

seqEM

:: MORE INFORMATION

Citation

E. R. Martin, D. D. Kinnamon, M. A. Schmidt, E. H. Powell, S. Zuchner and R. W. Morris
SeqEM: an adaptive genotype-calling approach for next-generation sequencing studies
Bioinformatics (2010) 26 (22): 2803-2810

CNVEM 0.710 – Infer Carrier Status of CNVs in Large Samples from SNP Genotyping Data

CNVEM 0.710

:: DESCRIPTION

CNVEM is a Bayesian Expectation-Maximization algorithm that infers carrier status of CNVs in large samples from SNP genotyping data, such as are available in genome-wide association studies. Using Bayesian computations the program calculates the posterior probability for carrier status of known CNV in each individual of a sample by jointly analyzing genotype information and hybridization intensity. Signal intensity is modeled as a mixture of normal distributions, allowing for locus-specific and allele-specific distributions. Using an expectation maximization algorithm, these distributions are estimated and then used to infer the carrier status of each individual the boundaries of the CNV.

::DEVELOPER

Sebastian Zöllner @ the Center for Statistical Genetics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
C Complier

:: DOWNLOAD

CNVEM

:: MORE INFORMATION

If you use CNVEM please e-mail szoellne@umich.edu or fill out the registration form.

MicroMerge 2.0 – Merge Microsatellite Genotype Data Sets

MicroMerge 2.0

:: DESCRIPTION

MicroMerge automates merging of microsatellite data sets that were genotyped at different facilities or using different protocols or platforms. The software employs a Bayesian statistical model that matches allele frequencies between data sets (Presson et al. 2006). MicroMerge v2.0 enables the following aspects of control: 1) output file formats which can be handled by most statistical genetic analysis packages, 2) tailoring the algorithm to different merging scenarios, such as data sets with very different sample sizes or multiple data sets, 3) merging small data sets when a reliable set of allele frequencies are available, and 4) improving the quantity and 5) quality of merged data.

::DEVELOPER

Angela P. Presson (micromerge@genetics.ucla.edu), UCLA Human Genetics

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

Windows / Linux

:: DOWNLOAD

MicroMerge

:: MORE INFORMATION

Citation:

Angela P Presson , Eric M Sobel , Paivi Pajukanta , Christopher Plaisier , Daniel E Weeks , Karolina Åberg and Jeanette C Papp (2008)
Merging microsatellite data: enhanced methodology and software to combine genotype data for linkage and association analysis
BMC Bioinformatics 2008, 9:317

GEST98 – Analysis of Genotype x Environment Interaction

GEST98

:: DESCRIPTION

GEST98 is a computer package for the analysis of genotype x environment interaction of a quantitative trait.

::DEVELOPER

Yasuo UKAI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows

:: DOWNLOAD

GEST98

:: MORE INFORMATION

When you publish a paper including results which were obtained by utilizing Gest, then please put a short sentence in your paper telling that “Gest98 was used.” .

Haplotyper 1.0 – Genotype Format Data from an Excel File to a Format of Arlequin

Haplotyper 1.0

:: DESCRIPTION

Haplotyper (Genotype Transposer) is an excel macro that will format data from an Excel spreadsheet to a format ready to be used by the program “Arlequin“, which will reconstruct haplotype frequencies within the population, based on the genotypes given. After which, other macros will extract the haplotype information, calculate linkage disequilibrium between markers, and prepare an input sheet for the “GOLD” program, which makes a graphical display of this data. Then, a last macro can reconstruct the haplotypes of each individual in the population, using a maximum likelihood method and the haplotypes present in the population as proposed by Arlequin.

::DEVELOPER

David G. Cox

:: SCREENSHOTS

:: REQUIREMENTS

EXCEL

:: DOWNLOAD

Haplotyper

:: MORE INFORMATION

haplotyper has evolved into PHARE

GeneSeq 1.0 – LD-based Genotype Calling from Shotgun Sequencing Reads

GeneSeq 1.0

:: DESCRIPTION

GeneSeq package provides methods to call SNP genotypes and infer haplotypes for a single individual given reads produced by whole-genome shotgun sequencing using high-throughput technologies such as 454, Illumina, and ABI SOLiD. GeneSeq yields high genotype calling accuracy even from low read coverage by ecploting linkage disequilibrium patterns extracted from a reference panel such as those generated by the international Hapmap project. Genotype and haplotype inference is performed efficiently using a hierarchical factorial hidden Markov model (HF-HMM) integrating allele coverage information extracted from read data with haplotype frequencies inferred from the reference panel.

::DEVELOPER

Bioinformatics Lab , Computer Science & Engineering Dept. University of Connecticut

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

Linux

:: DOWNLOAD

GeneSeq

:: MORE INFORMATION

J. Duitama and J. Kennedy and S. Dinakar and Y. Hernandez and Y. Wu and I.I. Mandoiu,
Linkage Disequilibrium Based Genotype Calling from Low-Coverage Shotgun Sequencing Reads,
BMC Bioinformatics 12(Suppl 1):S53, 2011

GEDI 1.03 – Genotype Error Detection & Imputation

GEDI 1.03

:: DESCRIPTION

GEDI package provides methods for

* error detection in whole-genome SNP genotype data
* recovery of missing SNP genotypes
* imputation of genotypes at untyped SNPs based on reference haplotypes such as those provided by the Hapmap project
* genotype phasing through a copy of our highly scalable ENT algorithm.

GEDI handles genotype data from unrelated individuals as well as individuals related by simple pedigrees such as trios. GEDI computations rely on efficient likelihood computations based on a Hidden Markov Model of haplotype diversity in the population under study.

::DEVELOPER

Bioinformatics Lab , Computer Science & Engineering Dept. University of Connecticut

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

Linux

:: DOWNLOAD

GEDI

:: MORE INFORMATION

Citation:

J. Kennedy and I.I. Mandoiu and B. Pasaniuc,
Genotype Error Detection using Hidden Markov Models of Haplotype Diversity,
Journal of Computational Biology 15, pp. 1155-1171, 2008,