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.
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.
Implement a likelihood-based framework for calling single nucleotide variants and detecting de novo point mutation events in families for next-generation sequencing data. In addition to facilitating detection and genotyping of SNPs, Polymutt can interface with existing tools to improve the accuracy of more challenging short insertion deletion polymorphisms and other types of variants. Polymutt should make studies of families even more attractive because, in addition to making it easy to study rare variants and de novo mutation events, family studies will now be able to better 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.
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’.
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.
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.
Identifies candidate positions with DNA modifications by using raw signals of reads generated by the Nanopore long-read sequencing technique. NanoMod can pinpoint DNA modifications de novo and doesn’t need training data. This algorithm cannot predict specific type of modification but given large-scale training data sets, it can create prior models to detect specific type of modifications.
Automates the discovery of de novo single-nucleotide variant (SNV) mutations (DNMs) from family genotype data (provided in a VCF). forestDNM is an R package with a command-line executable. It use a classifier that has been trained to discriminate putative DNMs. It could be used to performs genetic studies in trios and families. The results of forestDNM include both germline and somatic DNMs (which would include cell line artifacts).
Detects de novo mutations (DNMs) and rare inherited mutations from next-generation sequencing (NGS) data in sporadic diseases. mirTrios can be employed in the identification of rare inherited mutations. It supports known diagnostic variants and causative gene identification, as well as the prioritisation of novel and promising candidate genes. The tool provides an intuitive interface for users to upload files directly by web page or ftp address, which can be widely used by researchers to explore the functional mutation and candidate genes in sporadic disease.
Estimates the probability of recurrence of disease causing mutations in the clinical setting. DNM recurrence calculator assesses the probability of de novo mutation (DNM) recurrence for use in genetic counselling. The software incorporates genomic position, occurrence in an older sibling, parental age, presence, the parent-of-origin, and levels of parental mosaicism as covariates. It can allow parents to be better advised on future pregnancies.
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