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.
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.
Finds genomic rearrangements, including translocations, inversions and deletions. FACTERA can perform with high specificity without compromising sensitivity. It is able to define fusion genes and breakpoints in targeted sequencing data. This tool is applicable on paired-end and soft-clipped reads and is useful for whole genome shotgun sequencing investigation. It aligns all soft-clipped and unmapped reads against each candidate fusion sequence.
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.
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.
Conducts multiple splits at arbitrary locations in a read. Gustaf can deal with single-end and paired-end reads. It discovers local alignments of a read, and then chains local alignments into a semi-global read-to-reference alignment. This tool recognizes dispersed duplications and intra-chromosomal translocations with exact breakpoints. It utilizes standard graph algorithms to assess relationships of the alignments.
Detects structural variations (SVs) in mate pair (MP) datasets. Ulysses is a paired-end method (PEM)-based software including an SV scoring module, which improves SV detection accuracy in MP libraries. This method can annotate the full spectrum of SV, including deletions (DEL), segmental duplications (DUP), inversions (INV), small insertions (sINS, with a size smaller than the library IS), large insertions (INS), reciprocal translocations (RTs) and non-reciprocal translocations (NRT).
A probabilistic method for somatic structural variation (SV) prediction by jointly modeling discordant and concordant read counts. PSSV is specifically designed to predict somatic deletions, inversions, insertions and translocations by considering their different formation mechanisms. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer data to identify somatic SVs of key factors associated with breast cancer development.
An integrated structural variation (SV) caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes.
Recent "third-generation" sequencing technologies provide single-molecule templates and longer sequencing reads, but at the cost of higher per-nucleotide error rates. MultiBreak-SV identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.
Allows variant detection, combining mismatch, split-read, read pair, and read depth whole genome sequence (WGS) evidence. GROM is able to detect single nucleotide variants (SNVs), indels, structural variants (SVs), and copy number variants (CNVs). It can determine abnormal insert size by employing a sample of 10 million paired reads. This tool provides functions to simultaneously perform duplicate filtering.
Detects chromosomal aberrations with high specificity across a several variant types and lengths in next-generation mate pair sequencing data. SVachra calculates the distributions of the inward and outward facing mate pair types and applies independent clustering of the inward and outward facing discordant mapped reads to call chromosomal structural variants. Subsequently, it generates a highly specific breakpoint calling that aims to perform a more unbiased detection methodology.
Integrates calls from one or more breakpoint detection methods and classifies the structural variant (SV). CLOVE can build a graph data structure from the provided breakpoint information and then looks for patterns that are characteristic of more complex rearrangement types. It is able to classify complex events from the data. The tool is a flexible method to utilize in any SV calling pipeline. It can process joint inputs from multiple methods as an attractive feature.
Provides a structural variation (SV) caller for long reads. Sniffles is mainly designed for PacBio reads, but also works on Oxford Nanopore reads. SV are larger events on the genome (e.g. deletions, duplications, insertions, inversions and translocations). Sniffles can detect all of these types and more such as nested SVs (e.g. inversion flanked by deletions or an inverted duplication). Furthermore, Sniffles incorporates multiple auto tuning functions to determine data set depending parameter to reduce the overall risk of falsely infer SVs.
Provides a structural variant caller. TIDDIT detects many different types of structural variants (SVs) such as translocations, inversions, deletions, interspersed duplications, insertions and tandem duplications. It utilizes discordant pairs and split reads to detect the genomic location of structural variants, as well as the read depth information for classification and quality assessment of the variants.
Identifies structural variants from de novo assemblies. PAVFinder is able to detect translocations, inversions, duplications, insertions, deletions, simple-repeat expansions/contractions for genomic structural variants. It can be applied to transcriptomic structural variants and transcriptomic splice variants to find information such as gene fusions, partial tandem duplications (PTD), skipped exons or retained introns between others.
Detects structural variants in whole genome paired end or mate pair sequencing. qSV includes independent findings from soft clipping and discordant mapped pair analyses. It implements a localized de novo assembly of abnormal reads and split contig alignment to maximize accuracy of breakpoint, micro homology and non-template sequence detection.
Quantifies evidence for structural variation in genomic regions suspected of harboring rearrangements. SV-STAT extends existing methods by adjusting a chimeric read’s support of a structural variation by (i) the number of its soft-clipped bases and (ii) the quality of its alignment to the junction. SV-STAT is more accurate than alternative methods for determining base-pair resolved breakpoints. SV-STAT is a significant advance towards accurate detection and genotyping of genomic rearrangements from DNA sequencing data.
Standardizes the Structural Variation (SV) detection pipeline. SV-AUTOPILOT is a pipeline that can be used on existing computing infrastructure in the form of a Virtual Machine (VM) Image. It provides a “meta-tool” platform for using multiple SV-tools, to standardize benchmarking of tools, and to provide an easy, out-of-the-box SV detection program. In addition, the user can choose which of several alignment algorithms is used in their analysis.
1 - 10 of 10
Filters / Sort by