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RASQUAL / Robust Allele-Specific Quantitation and Quality Control
Maps quantitative trait loci (QTLs) for sequenced based cellular traits by combining population and allele-specific signals. RASQUAL is a statistical method, which integrates between-individual differences, allele specific (AS) signals and technical biases in sequencing-based cell phenotypes. It can also highlight genomic regions with problematic AS signals, enabling more informed downstream analysis. Additionally, by minimising the amount of data removed, RASQUAL avoids inadvertent removal of real signal.
Funmap / Functional Mapping
Provides assistance for analyzing quantitative trait loci (QTL) for a longitudinal trait based on functional mapping. Funmap performs a pipeline service that covers QTLs scanning, significance value computing, data loading and significant QTLs reporting. It chooses automatically the type of longitudinal curve and covariance matrix by information criterion. This pipeline is designed to work on experimental populations derived from a cross between two inbred lines.
A software package for mapping and visualizing the genetic architecture underlying complex traits for experimental populations derived from a cross between two inbred lines. It can simultaneously map quantitative trait loci (QTL) with individual effects, epistasis and QTL-environment interaction. Currently, it is able to handle data from F(2), backcross, recombinant inbred lines and double-haploid populations, as well as populations from specific mating designs (immortalized F(2) and BC(n)F(n) populations).
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Implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted p-values can be estimated at any level of significance with little computational cost. FastQTL also provides a modular base onto which new functionalities are being implemented, such as fine mapping of causal variants, conditional analysis to discover multiple independent QTLs per phenotype and interaction analysis to discover sex or disease specific QTLs.
Performs linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. solarius allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It also parses the model output and contains summary and plotting functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses, that makes the calculation of genome-wide scans more efficient.
Simulates Systems Genetics (SG) experiments in model organisms. SysGenSIM aims to evaluate and compare statistical and computational methods and their implementations for analyses of SG data. It permits to select a variety of network topologies, genetic and kinetic parameters to simulate SG data (genotyping, gene expression and phenotyping) with large gene networks with thousands of nodes. The tool is able to generate data with networks of 10 000 nodes with the use of non-linear dynamical model.
It was released in the summer of 2006 and extended the functionality of QTLExpress by adding a Linkage-Disequilibruim-Linkage-Analysis (LDLA) tool in tandem with a Haplotyping analysis to enable higher resolution detection of QTL; an Epistasis option for 2-QTL determination in F2 populations was also implemented. This functionality was offered via a web portal which offered data persistence for management of users' output and distributed execution of analyses via a computational Grid system.
Aims at mining quantitative trait loci (QTLs) for candidate genes. Marker2sequence, for each gene, within the QTL region, uses data integration technology to integrate putative gene function with associated gene ontology terms, proteins, pathways and literature. As a typical QTL region easily contains several hundreds of genes, this gene list can then be further filtered using a keyword-based query on the aggregated annotations. Marker2sequence will help breeders to identify potential candidate genes for their traits of interest.
A set of programs written in MATLAB for the analysis of QTL data from inbred line crosses. Although Pseudomarker was originally developed based on the multiple imputation genome scan algorithm, it now includes a comprehensive set of methods. The software package provides a complete workflow in QTL data analysis including data checking and visualization, one, two and multiple QTL model genome scans with covariates, estimating and plotting QTL effect and a variety of plotting and reporting functions. Pseudomarker is specially tailored to work for analyzing large number of phenotypes efficiently, which makes the analysis of eQTL (expression QTL) data possible.
Allows specific quantitative trait loci mediating heterophyllous variation to be mapped throughout the genome. HpQTL is an R package for quantitative trait locus (QTL) analysis in case of highly polygenic genetic background. It can help users to calculate the power of QTL detection, assess the precision of parameter estimation and determine the sample size optimally used for QTL detection. The software also contributes a new approach for genome-wide association studies (GWAS) aimed to dissect the programmed regulation of plant development and evolution.
PEPIS / Pipeline for estimating EPIStatic effect
A web server-based tool for analysing polygenic epistatic effects. PEPIS is based on a linear mixed model that has been used to predict the performance of hybrid rice. It includes two main sub-pipelines: the first for kinship matrix calculation, and the second for polygenic component analyses and genome scanning for main and epistatic effects. PEPIS was dedicatedly developed for epistatic genetic estimation. It will help overcome the bottleneck in genetic epistasis analysis.
Creates genetic maps out of phenotype expression data. Pheno2Geno is made for high-throughput generation of genetic markers and maps from molecular phenotypes. It selects suitable phenotypes that show clear differential expression in the 1 founders. This tool uses mixture modelling to select phenotypes showing segregation ratios close to the expected mendelian segregation ratios and transform these phenotypes into genetic markers suitable for map construction and/or saturation. Pheno2Geno also excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping.
Allows composite interval mapping (CIM) mapping of multiple or longitudinal traits in a full-sib family derived by crossing two outbred parents. MVQTLCIM is a command-based software that implements the computing for the CIM mapping method in an outbred full-sib family. The software permits the use of multithreads for performing a large number of permutations to determine the experimental threshold of Loglikelihood Ratio (LR) for a significant quantitative trait loci (QTL). It can thus facilitate studies of QTL mapping.
Reads genome data from ms file format and performs forward simulation to evaluate genomic selection scenarios. Ms2gs is a combined coalescence - gene dropping (i.e. backward–forward) simulator for complex traits. This tool is conceived for short term, recent scenarios such as those that are of interest in animal and plant breeding. It can use real sequence or chip data or generate molecular polymorphisms via the coalescence. It can generate QTL conditional on extant molecular information, such as low-density genotyping. The software requires as input both genotypic data in plink or ms formats, and a pedigree that is used to perform the gene dropping.
An algorithm for multiple quantitative trait locus (QTL) scan with epistatic interactions. PruneDIRECT algorithm has specific strengths for permutation testing. It has been integrated to a flexible, parallel computing framework for identifying multiple interacting QTL which uses the map-reduce model as implemented in Hadoop. This framework is implemented in R, a widely used software tool among geneticists. This enables users to rearrange algorithmic steps to adapt genetic models, search algorithms, and parallelization steps to their needs in a flexible way.
Integrates all functionalities required to easily and rapidly transform raw sequence data into collections of molecular quantitative trait locus (molQTLs). Qtltools is based on powerful methods that either match or improve those employed in large scale reference studies such as Genetic European Variation in Health and Disease (Geuvadis) and Genotype-Tissue Expression (GTEx). It includes multiple new and powerful statistical methods to prepare and control the quality of the data, to map proximal and distal QTLs and to integrate those with genome wide association studies (GWAS) results and functional annotations.
An R package that aims to provide a user-friendly and platform-independent tool to visualize quantitative trait loci (QTL) mapping results. The graphical functions of the QTLNetworkR are based upon lattice and grid packages, and the graphical user interface (GUI) of the QTLNetworkR is built upon RGtk2 and gWidgetsRGtk2 packages. Six functions are designed to help visualize marker interval, putative QTL, QTL-by-environment interactions, marker interval interactions, epistasis, and the predicted genetic architecture of complex traits. It is especially helpful in profiling results for multiple traits at multiple environments.
Detects quantitative trait locus (QTLs) from experimental designs in outbred populations. QTLMap was used to analyze genetic variability of traits in various experimental designs with linkage analysis approaches. It employs an algorithm for the estimation of transmission probabilities and is able to deal with high numbers of markers in single nucleotide polymorphisms (SNPs). This tool provides a choice of simulation procedures that permits the calculation of rejection thresholds.
Integrates a broad spectrum of data mining, statistical analysis, interactive visualization and modeling tools that allow QTL analysis based on advanced and sophisticated methods for maximum extraction of the mapping information from data. The software is very user friendly, fast, and takes advantage of harmonious interaction between classical and novel methods, developed for QTL analysis by the mapping community, including tools for multilocus, multiple-trait, and multiple-environment QTL analysis in controlled crosses of economically important plants and animals, as well as model organisms.
PLABQTL / PLant Breeding And Biology QTL
Localizes and characterizes Quantitative Trait Loci (QTL). PLABQTL is a program for composite interval mapping of QTL in which: (i) requirements on the hardware are moderate, (ii) analysis of experiments with a large number of entries, tested in multi-location trials, is feasible even in the presence of missing marker or phenotypic data, (iii) outliers in the marker and phenotypic data can easily be detected and (iv) LOD curves under different model assumptions can rapidly be calculated and compared.
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Animal QTLdb / Animal Quantitative Trait Loci database
Provides quantitative trait loci (QTL) data for cattle, chicken and pigs. AnimalQTLdb enables comparison of QTL results within species and across experiments. It serves as a bridge between genotypes (genes) and phenotypes (traits), which will inevitably necessitate inclusion of single nucleotide polymorphism (SNP)/genome-wide association study (GWAS) data. The database contains about 5900 cattle, 3400 chickens, 7400 pigs, 750 sheep and 90 rainbow trout data points.
A curated and integrated web-based relational database providing access to publicly available genomic, genetic and breeding data for cotton. CottonGen supercedes CottonDB and the Cotton Marker Database, with enhanced tools for easier data sharing, mining, visualization and data retrieval of cotton research data. CottonGen contains annotated whole genome sequences, unigenes from expressed sequence tags (ESTs), markers, trait loci, genetic maps, genes, taxonomy, germplasm, publications and communication resources for the cotton community. Annotated whole genome sequences of Gossypium raimondii are available with aligned genetic markers and transcripts. These whole genome data can be accessed through genome pages, search tools and GBrowse, a popular genome browser. Most of the published cotton genetic maps can be viewed and compared using CMap, a comparative map viewer, and are searchable via map search tools. Search tools also exist for markers, quantitative trait loci (QTLs), germplasm, publications and trait evaluation data. CottonGen also provides online analysis tools such as NCBI BLAST and Batch BLAST.
PGDBj / Plant Genome DataBase Japan
Aims to integrate plant genome-related information from databases (DBs) and the literature. The PGDBj is comprised of three component DBs and a cross-search engine, which provides a seamless search over the contents of the DBs. The three DBs are as follows. (i) The Ortholog DB, providing gene cluster information based on the amino acid sequence similarity. (ii) The Plant Resource DB, integrating the SABRE DB, which provides cDNA and genome sequence resources accumulated and maintained in the RIKEN BioResource Center and National BioResource Projects. (iii) The DNA Marker DB, providing manually or automatically curated information of DNA markers, quantitative trait loci and related linkage maps, from the literature and external DBs. As the PGDBj targets various plant species, including model plants, algae, and crops important as food, fodder and biofuel, researchers in the field of basic biology as well as a wide range of agronomic fields are encouraged to perform searches using DNA sequences, gene names, traits and phenotypes of interest. The PGDBj will return the search results from the component DBs and various types of linked external DBs.
Provides organized and retrievable quantitative phenotype data curated from different rat research laboratories. PhenoMiner comprises results from National BioResource Project in Japan (NBRP), the PhysGen projects and the biomedical literature manually curated by Rat Genome Database (RGD). Data are searchable across multiple studies by using the four ontologies: rat strain ontology (RS), clinical measurement ontology (CMO), measurement method ontology (MMO) and experimental condition ontology (XCO).
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