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GMDR / Generalized Multifactor Dimensionality Reduction
Identifies gene-by-gene and gene-by-environment interactions. GMDR allows users to perform analyses for detection of multifactor interactions with large-scale data. The software implements a set of methods on the analysis of interactions with diverse study designs such as case-control design, family based design or a combination of both. GMDR also provides features such as large-scale data management and preprocessing. It can assist in revealing genetic architecture in terms of gene-gene interactions underlying complex traits.
A bioinformatics web app for identifying network-based biomarkers that most correlate with patient survival data. SurvNet is a valuable bioinformatic tool for identifying network-based biomarkers that most correlate with patient survival data. SurvNet takes three input files: one biological network file as the searching platform (one human protein interaction network is provided as default), one molecular profiling file (e.g., array-based gene expression or DNA methylation data or mutation data), and one patient survival data file. Given user-defined parameters, SurvNet will automatically identify sub-networks that most correlate with patient survival data and display the results in a visually appealing manner.
Verifies sample identities from FASTQ, BAM or VCF files. NGSCheckMate uses a model-based method to compare allele read fractions at known single-nucleotide polymorphisms (SNPs), considering depth-dependent behavior of similarity metrics for identical and unrelated samples. It is effective for a variety of data types, including exome sequencing, whole-genome sequencing, RNAseq, ChIP-seq, targeted sequencing and single-cell whole-genome sequencing, with a minimal requirement for sequencing depth. The tool can be used as a quality control step in next-generation sequencing (NGS) studies.
Includes all the necessary functions to build and test predictors from expression data. Phenopredict is an R package that use expression data and phenotype information from any study to build a phenotype predictor. This package allows for (i) regions to be selected for the phenotype of interest, (ii) a predictor to be built for either continuous or categorical variables, (iii) the predictor in to be tested on the training data to assess resubstitution error, (iv) data to be extracted from a new data set for the same regions upon with the predictor was built, and (v) phenotype prediction in new data set.
A genome analysis software package. TimeZone is designed to detect footprints of positive selection for functionally adaptive point mutations. The uniqueness of TimeZone lies in its ability to predict recent adaptive mutations that are overlooked by conventional microevolutionary tools. TimeZone analyzes adaptive footprints in either individual genes or in sets of genomes. It allows three major workflows: (i) extraction of orthologous gene sets from multiple genomes; (ii) alignment and phylogenetic analysis of genes; and (iii) identification of candidate genes under positive selection for point mutations, taking into account the effect of recombination events.
Offers a method for association-detection testing of samples with related individuals from structured populations. ROADTRIPS is able to correct pedigree and population structure thanks to a covariance matrix, including admixture. It includes features for handling missing data, for including either unaffected controls and controls of unknown phenotype into the process. This tool is able to deliver an enriched analysis by incorporating data about pedigree structure of the sampled individuals.
GNOVA / GeNetic cOVariance Analyzer
Estimates annotation-stratified genetic covariance between traits using genome-wide association studies (GWAS) summary statistics. GNOVA provides accurate covariance estimates and powerful statistical inference that are robust to linkage disequilibrium (LD) and sample overlap. It was applied to estimate genetic correlations for 50 complex traits using publicly available GWAS summary statistics. The results show that the tool is more powerful when genetic correlation is moderate comparing to LD score regression (LDSC).
rMFilter / region Match-based Filter
Identifies chimeric noisy long reads based on short token matches within local genomic regions. rMFilter can accelerate structure variation (SV) calling pipelines without loss of effectiveness. It is able to fast filter the reads to substantially improve the overall speed of long read-based SV calling pipelines. The first step of the tool consists in the localization of a local region which has the highest number of short token (k-mer) matches. The second step aims to reduce false negatives.
Performs investigation of RNA editing from next-generation sequencing (NGS) data. REDItools contains three main scripts to study RNA editing using both RNA-Seq and DNA-Seq data from the same sample/individual or RNA-Seq data alone: (1) REDItoolDnaRNA.py for detecting RNA editing candidates, (2) REDItoolKnown.py for exploring the RNA editing potential of RNA-Seq experiments, and (3) REDItoolDenovo.py for performing de novo detection of RNA editing candidates. It also includes some accessory scripts and allows to annotate all candidate positions using relevant databases.
Provides assistance for analyzing and visualizing deep amplicon sequencing data. jMHC aims to deal with genotyping of major histocompatibility complex and can be appropriate to use for processing amplicons derived from other multigene families or for genotyping other polymorphic systems. It first extracts target sequences from all reads, including the complete tag sequence, and then generates a table which contains sequence variants and the number of reads, and lastly creates files with all sequence variants ordered.
Provides translations of biological database identifiers. Synergizer offers a web app and an API accessible both programmatically and interactively. For each authority, it uses a peg that is specific to that authority. The Synergizer’s schema has been designed to preserve the provenance of all synonym relationships, and to accommodate new sources of synonym information over time. It also supports synonyms from two different authorities Ensembl and NCBI, and holds a total of just over 20 million synonym relations covering over 70 species and over 150 namespaces.
Allows users to extract sets of clusters of orthologous groups (COGs) of proteins associated with a phenotype from COG phylogenetic profiles and a phenotype profile. NetCAR is a class association rule mining algorithm permits the extraction of relevant genes (COGs) that cannot be elucidated by simple pairwise comparisons. Moreover, it mays be able to mine co-regulatory gene network modules relevant with a target physiological observation, from microarray data with many more genes than expression arrays.
Estimates the multiplicity of infection (MOI) locally in the genome and overall to obtain a global estimate. estMOI is a suite of Perl scripts consisting of a sequence-based genome-wide approach to determine MOI, which considers all possible local estimates based on combinations of alleles from read pairs. It allows the identification of genic regions of high multiplicity, thereby informing the development of new assays for inference. It was implemented, for assessment, on several collections of P.falciparum with Illumina deep sequencing data.
Allows hierarchical genotype classification of clonal pathogens based on canonical single nucleotide polymorphisms (SNPs). CanSNPer is a genotype classification pipeline which stores information on canonical SNPs, extracts known canonical SNPs from a query draft sequence for classification of the pathogen isolate and can generate a visual representation of all SNPs in the canonical SNP tree for the query sequence. CanSNPer was designed to easily extract known canonical SNPs from a query draft sequence for classification of the pathogen isolate.
A fast, flexible and easy-to-use tool for multi-category genetic association studies. By providing a wide range of use options, it allows the user to tailor their analysis to their data and experimental design. For instance, if the user wishes to carry out model selection at a risk variant, but wishes to account for the effect of a second risk variant in linkage disequilibrium, then Trinculo’s conditional regression option will handle this automatically. Other use cases, such a multinomial fine-mapping or ordinal logistic regression, are also included in the software.
EPS / Empirical Bayes approach to integrating Pleiotropy and tissue-Specific information
A statistical approach that can integrate pleiotropy information from GWAS data and tissue-specific gene expression data. Compared with some existing approaches, such as linear mixed models, which require genotype data at the individual level, EPS only requires summary statistics for analysis. EPS enables rigorous hypothesis testing of pleiotropy and tissue-specific risk gene expression patterns. All of the model parameters can be adaptively estimated from the developed expectation–maximization (EM) algorithm.
MeRP / Mendelian Randomization Pipeline
Facilitates rapid, causal inference analysis through automating key steps in developing and analyzing genetic instruments obtained from publicly available data. MeRP uses the National Human Genome Research Institute catalog of associations to generate instrumental variable trait files and provides methods for filtering of potential confounding associations as well as linkage disequilibrium. MeRP generates estimated causal effect scores via a MR-score analysis using summary data for disease endpoints typically found in the public domain.
A workflow to visualize and explore ploidy levels in a newly sequenced genome, exploiting short read data. The analysis workflow consists in four steps: (i) it stores, in a data structure, for each position, the number of reads supporting different nucleotides; (ii) traverses the data structure, ignoring positions where a single nucleotide was observed and where the most frequent nucleotide had a frequency larger; (iii) putative allele percentages at each position are ordered from lowest to highest; (iv) finally, a histogram is generated to help on deciding which is the possible ploidy level of the organism under study. PloidyNGS is a useful tool that allows to visually assess the ploidy of organisms for which there is Next Generation Sequencing (NGS) short-read data available.
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Provides an interactive web-tool for interrogations. pGWAS is a genome-proteome network and a web server that offers the results of a genome-wide association study with protein levels (pGWAS). It identifies 539 independent, genome-wide significant single nucleotide polymorphism (SNP)-to-protein associations. Using a highly multiplexed, aptamer-based, affinity proteomics platform, it quantifies levels of 1,124 proteins in blood plasma samples from 1,000 German individuals and 338 Arab or Asian individuals. It provides a basis for novel approaches to pharmaceutical and diagnostic applications.
ORIO / Online Resource for Integrative Omics
Integrates the whole genome data accessible to life scientists with minimal computational expertise. ORIO is a web-based resource with an intuitive user interface for rapid analysis and integration of next generation sequencing (NGS) data. It first iteratively finds read coverage values at each genomic feature for each NGS dataset. Data are then integrated using clustering-based approaches, giving hierarchical relationships across NGS datasets and separating individual genomic features into groups.
Identifies region-specific single nucleotide polymorphisms (SNPs) in which the polymorphic nucleotide creates a restriction fragment length polymorphism (RFLP) that can be readily assayed at the benchtop using restriction enzyme digestion of SNP-containing PCR products. SNP2RFLP permits user-defined queries that maximize the informative markers for a specific application, and allows to retrieve an adequate and manageable number of markers. This tool facilitates fine-mapping in a region containing a mutation of interest.
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