Focuses on variant discovery and genotyping. GATK provides a toolkit, developed at the Broad Institute, composed of several tools and able to support projects of any size. The application compiles an assortment of command line allowing one to analyze of high-throughput sequencing (HTS) data in various formats such as SAM, BAM, CRAM or VCF. The website includes multiple documentation for guiding users.
A software tool for analyzing de novo mutations from familial and somatic tissue sequencing data. DeNovoGear uses likelihood-based error modeling to reduce the false positive rate of mutation discovery in exome analysis and fragment information to identify the parental origin of germ-line mutations.
A platform-independent mutation caller for targeted, exome, and whole-genome resequencing data generated on Illumina, SOLiD, Life/PGM, Roche/454, and similar instruments. The newest version, VarScan 2, is written in Java, so it runs on most operating systems. It can be used to detect different types of variation: 1) germline variants (SNPs and indels) in individual samples or pools of samples, 2) multi-sample variants (shared or private) in multi-sample datasets (with mpileup), 3) somatic mutations, LOH events, and germline variants in tumor-normal pairs and 4) somatic copy number alterations (CNAs) in tumor-normal exome data.
Builds genetic maps and conducts population genomics and phylogeography. Stacks is a software system developed to work with restriction enzyme-based data, such as RAD-seq. The software produces core population genomic summary statistics and single nucleotide polymorphism (SNP)-by-SNP statistical tests. It aims to be a key resource to empower researchers to efficiently perform ecological and evolutionary genomic studies in model organisms and particularly in organisms with minimal or no genomic resources.
Consists of a linkage-disequilibrium framework to genotype inference in parents-offspring trios. TrioCaller implements a method to call genotypes and infer haplotypes for whole genome shotgun sequencing data collected in trios, unrelated individuals, or parent-offspring pairs. The software can facilitate genotype calling and haplotype inference for sequencing projects.
Performs variant discovery on Amazon's Web Service (AWS) cloud or on local high-performance computing clusters. GenomeVIP is a genomics analysis pipeline for cloud computing with germline and somatic calling on amazon’s cloud. It provides a collection of analysis tools and computational frameworks for streamlined discovery and interpretation of genetic variants. The server and runtime environments can be customized, updated, or extended.
Investigates developmental epigenomes and transcriptomes that are related to De novo mutations (DNMs) in developmental disorders. EpiDenovo is a database for exploring the associations between embryonic epigenetic regulation and DNMs in developmental disorders, including neuropsychiatric disorders and congenital heart disease. This resource is based on the epigenomes of publicly available chromatin immunoprecipitation sequencing (ChIP-seq) and chromatin accessibility data during the embryonic development of mammals, including humans and mice.
A computational tool for calculating probability of variants in family-based sequencing data. It is still challenging to call rare variants. In family-based sequencing studies, information from all family members should be utilized to more accurately identify new germline mutations. FamSeq serves this purpose by providing the probability of an individual carrying a variant given his/her entire family’s raw measurements. FamSeq accommodates de novo mutations and can perform variant calling at chrX.
Classifies candidates as true or false de novo mutations. DNMFilter uses gradient boosting as the classification algorithm to filter De Novo Mutations (DNMs) identified in parent–offspring trios. It could maintain the high sensitivity and significantly reduce false positive DNMs when coupled with commonly used DNM detection approaches. The tool is a valuable complement for existing DNM detection approaches and can be employed to filter out false positive DNM calls, which eventually leads to a reasonable size of highly confident DNM call set for experimental validation and further analysis.
Increases accuracy in the de novo variant calling process and delets false negatives. novoCaller is based on a rigorous statistical approach that utilizes population frequency and pedigree data. It can construct calls from VCF files that contain allele depth information. This tool enables the discovery of technical artifacts present in the sequencing and alignment process using unrelated samples.
Implements a likelihood-based framework for calling single nucleotide variants and detecting de novo point mutation events in families for next-generation sequencing (NGS) data. Polymutt simplifies detection and genotyping of single nucleotide polymorphisms (SNPs). It aims to facilitate the study of families, rare variants and de novo mutation events. It also intends to transform sequence data into accurate genotypes.
Deduces the identity-by-descent (IBD) sharing among family members directly from sequencing data. Polymutt2 can be useful for the inference of individual genotypes for small to moderate pedigrees and for gene mapping of rare variants for complex disease. This tool employs deduced IBD sharing to evaluate variant quality, refine individual genotypes, and construct haplotypes along the genome. This second version of Polymutt improve its performance for small to moderate pedigrees.
A reliable big data-based computational toolset for efficiently manipulating genome-wide variants, annotations and every-site coverage in NGS studies. SeqHBase uses a heuristic framework of inheritance information for detecting de novo, inherited homozygous or compound heterozygous mutations that may be disease-contributing in trios, nuclear families and/or extended families. It shows very good performance on three different examples of family based sequencing data and is scalable by virtue of its basis on MapReduce framework.
Differentiates patient-specific from cohort-specific alterations.j MICADo is based on the well-known representation of next generation sequencing (NGS) sequencing reads, de Bruijn graphs (DBG). It permits to circumvent the alignment step required by most single nucleotide variation (SNV) callers. This tool can avoid additional biases due to the alignment itself. It is useful in targeted sequencing with high background noise from cohorts of patients.
Organizes, annotates, filters and diagnoses patients with Mendelian Disorders using Exome and Genome sequencing data or experimental validation and possible diagnosis. Mendel,MD combines several types of filter options and uses regularly updated databases to facilitate exome and genome annotation, the filtering process and the selection of candidate genes and variants. The software provides a limited list of good candidates that can always be manually investigated by researchers and doctors using their research and clinical skills.
Enables genotyping and variant annotation of resequencing data produced by second generation next generation sequencing (NGS) technologies. CoVaCS is an automated system that provides tools for variant calling and annotation along with a pipeline for the analysis of whole genome shotgun (WGS), whole exome sequencing (WES) and targeted resequencing data (TGS). The software allows non-specialists to perform all steps from quality trimming to variant annotation.
Detects and visualizes mutations in a given file. MutScan creates read pile-up visualizations that can be used for mutations validation. Additionally, the software includes features for scanning a set of variants and allows to visualize them by generating a page for each variant. The application is suited for running long reads measuring at least 50 bp. Users can also use BAM or CRAM files converted to the required format before processing.
Sifts true de novo mutations (DNMs) from noise. HAPDeNovo drastically eliminates false positive DNMs without decreasing the detection rate of true positives. It can be used to re-calibrate the DNM quality based on read coverage and sequencing quality for each haplotype. This tool follows three steps: (1) variant calling and phasing; (2) haplotype-specific genotyping; (3) removing false positive DNMs.
Identifies, characterizes, and quantifies various sources of erroneous high-throughput sequencing (HTS) error. MERIT is a comprehensive pipeline designed for in-depth quantification of HTS calls, specifically for ultra-deep applications. This application considers the genomic context of errors and shows a significant relationship between error rates and their sequence contexts, including the nucleotides immediately at their 5’ and 3’.
Permits researchers to elucidate the relationship between neighboring sequence and mutagenesis. ENU project can differentiate mutations that occurred by normal cellular processes from those induced by a potent mutagen. It consists of a machine learning classification that discovers a rule to assign novel objects to one of several classes. This tool provides a means for generic recognition of mutations that do not match an established reference sample.