Identifies the structural variation (SV) by whole genome de novo assembly. SOAPsv aims to show that SVs reports for a greater fraction of diversity between individuals than do single nucleotide polymorphisms (SNPs). This software also demonstrates that de novo assembly can detect SVs of a large range of lengths. The SV maps of human genomes allows to initially describe the genomic patterns of SVs and their relationship with a variety of genomic features.
Identifies somatic variation in tumor genomes. SMuFin uses direct comparison with the corresponding normal samples to detect in a single run somatic single-nucleotide variants (SNV) and structural variants such as insertions, deletions, inversion and translocations of any size. This software allows to describe at base pair resolution complex scenarios of chromosomal rearrangements like chromoplexy and chromothripsis.
Allows to identify missing sequence and genetic variation. SMRT-SV generates data about DNA sequences. The distinctive feature of this tool is that native DNA is sequenced without cloning or amplification and read lengths typically exceed 5 kbp. It presents lower individual read accuracy, but a longer read length facilitates high confidence mapping across a greater percentage of the genome.
To characterize the mutational spectrum of somatic SVs in cancer, it is important to identify both simple (e.g., deletion, insertion, and inversion) and complex SVs at base-pair resolution. Meerkat predicts both germline and somatic SVs directly from short read data, focusing on complex events.
A Perl/C++ package that provides genome-wide detection of structural variants from next generation paired-end sequencing reads. BreakDancer sensitively and accurately detected indels ranging from 10 base pairs to 1 megabase pair that are difficult to detect via a single conventional approach.
Assists users to infer an underlying genotype at each structural variants (SVs). SVTyper is a Bayesian likelihood algorithm that can operate on copy-neutral events such as inversions and translocations as well as copy number variants (CNVs). It permits the production of SV genotypes, useful for meaningful variant interpretation, as well as quantitative estimates of breakpoint allele frequencies that allow inference of the fraction of tumor cells that carry a particular variant.
A tool to generate local assemblies of breakpoints genome-wide. NovoBreak is an algorithm used in cancer genomic studies to discover structural variants (both somatic and germline) breakpoints in whole-genome sequencing data. Assemblies realized by novoBreak are based on clusters of reads which share a set of short nucleotide stretches of length K (K-mers) present in a subject genome but not in the reference genome or control data.
Allows structural variant (SV) discovery. LUMPY is a general probabilistic SV discovery framework that integrates multiple SV detection signals, including those generated from read alignments or prior evidence. The software is based upon a general probabilistic representation of an SV breakpoint that allows any number of alignment signals to be integrated into a single discovery process. It can detect SV from multiple alignment signals in files from one or more samples. A simplified wrapper for standard analyses, LUMPY Express, can also be executed.
A computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. The package is composed of three modules, PEMer workflow, SV-Simulation and BreakDB. PEMer workflow is a sensitive software for detecting SVs from paired-end sequence reads. SV-Simulation randomly introduces SVs into a given genome and generates simulated paired-end reads from the ‘novel’ genome. Subsequent analysis with PEMer workflow on the simulated reads can facilitate parameterize PEMer workflow. BreakDB is a web accessible database developed to store, annotate and dsplay SV breakpoint events identified by PEMer and from other sources.
Identifies structural variant (SV) breakpoint junctions by clustering split reads. NanoSV first orders all mapped segments of each split read by their positions within the originally sequenced read. This tool utilizes split read mapping to discover all defined types of SVs. It finishes by gathering evidence form different reads supporting the same candidate breakpoint junction. NanoSV suits for Nanopore and Pacific Biosciences data.
Offers a method for the detection of structural variants (SVs). GASVPro proposes a probabilistic model, able to consider inversions and reciprocal translocations, which is based on a merging of paired-read and read depth signals. It furnishes a method able to handle reads with multiple possible alignments. This program can report: (i) uncertainty in predicted breakpoint and if a generic breakend can be classified as an homozygous or an heterozygous variant.
Allows identification of genomic rearrangements. GRIDSS is a module software suite containing tools which performs genome-wide break-end assembly prior to variant calling using a positional de Bruijn graph assembler. The GRIDSS pipeline comprises three distinct stages: extraction, assembly, and variant calling. The software identifies non-template sequence insertions, microhomologies and large imperfect homologies, and supports multi-sample analysis.
Identifies regions of the genome suspected to harbor a complex event. SVelter then resolves the structure by iteratively rearranging the local genome structure, in a randomized fashion, with each structure scored against characteristics of the observed sequencing data. SVelter is able to accurately reconstruct complex chromosomal rearrangements when compared to well-characterized genomes that have been deeply sequenced with both short and long reads. SVelter is able to interrogate many different types of rearrangements, including multi-deletion and duplication-inversion-deletion events as well as distinct overlapping variants on homologous chromosomes.
A versatile variant caller for both DNA- and RNA-sequencing data. VarDict contains many features that are distinct from other variant callers, including linear performance to depth, intrinsic local realignment, built-in capability of de-duplication, detection of polymerase chain reaction (PCR) artifacts, accepting both DNA- and RNA-seq, paired analysis to detect variant frequency shifts alongside somatic and loss of heterozygosity (LOH) variant detection and structural variant (SV) calling. VarDict facilitates application of next-generation sequencing in cancer research, enabling researchers to use one tool in place of an alternative computationally expensive ensemble of tools.
An approach that uses a 'kmer' strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants.
Detects and visualizes structural variation from paired-end mapping data. Under this scheme, abnormally mapped read pairs are clustered based on the location of a gap signature. Several important features, including local depth of coverage, mapping quality and associated tandem repeat, are used to evaluate the quality of predicted structural variation. Compared with other approaches, it can detect many more large insertions and complex variants with lower false discovery rate. Moreover, inGAP-sv, written in Java programming language, provides a user-friendly interface and can be performed in multiple operating systems.
An accurate structural variation (SV) detection method, which compares the statistics of the mapped read pairs in tumor samples with isogenic normal control samples in a distinct asymmetric manner. COSMOS also prioritizes the candidate SVs using strand-specific read-depth information. Performance tests on modeled tumor genomes revealed that COSMOS outperformed existing methods in terms of F-measure.
Detects breakpoints of large deletions and medium sized insertions from paired-end short reads. Pindel is a program that uses pattern growth algorithm to identify the break points of large deletions (1 bp–10 kb) and medium sized insertions (1–20 bp) from 36 bp paired-end short reads. The software can be useful for addressing the structural variations between individuals from next-gen high throughput sequencing.
Allows users to search both read pair and split clone sequence signatures using the mapping locations of long range sequencing read. VALOR requires split clones from different pools to cluster at the same putative inversion breakpoints. This tool can discover large genomic inversions using high throughput sequencing (HTS) technologies. It can also perform molecule recovery, clustering via structural variation (SV) graph and molecule depth filtering.