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SV-STAT / Structural Variation detection by STAck and Tail

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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.

Digit / Detection and Identification of Genomic Interchromosomal Translocations

Implements a novel measure of mapping ambiguity to discover interchromosomal structural variations (SVs) from matepair and pair-end sequencing data. Digit is a software pipeline that robustly handles the high numbers of artifacts present in mate-pair sequencing and reduces the false positive rate while maintaining sensitivity. It generates a self-updating library of common translocations and allows for the investigation of patient- or group-specific events, making it suitable for discovering and cataloging chromosomal translocations associated with specific groups, traits, diseases or population structures.

PSSV

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.

inGAP-sv

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.

Sniffles

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.

BreaKmer

forum (1)
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.

SV-Bay

Detects structural variants in cancer using whole genome sequencing data with or without matched normal control sample. SV-Bay does not only use information about abnormal read mappings but also assesses changes in the copy number profile and tries to associate these changes with candidate SVs. The likelihood of each novel genomic adjacency is evaluated using a Bayesian model. In its final step, SV-Bay annotates genomic adjacencies according to their type and, where possible, groups detected genomic adjacencies into complex SVs as balanced translocations, co-amplifications, and so on. A comparison of SV-Bay with BreakDancer, Lumpy, DELLY and GASVPro demonstrated its superior performance on both simulated and experimental datasets.

SV-AUTOPILOT / Structural Variation AUTOmated PIpeLine Optimization Tool

Obsolete
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.

MUMdex / Maximal Unique Matchdex

A package for aligning sequences to a reference genome. MUMdex consists of an aligner, an alignment format, an analysis software and a portable population database of common structural variants to aid filtering. The aligner saves read pair information in an indexed lossless compact binary format as MUMs plus the sequence not covered by MUMs. MUMdex computes a numerical invariant for each pair of MUMs and, depending of the value, signals either genome rearrangements (inversions, translocations or indels) or problems in the reference genome. It can also detect single nucleotide polymorphisms (SNPs), but less efficiently than standard methods.

Ulysses

A tool that achieves drastically higher detection accuracy than existing tools, both on simulated and real mate-pair sequencing datasets from the 1000 Human Genome project. Ulysses achieves high specificity over the complete spectrum of variants by assessing, in a principled manner, the statistical significance of each possible variant (duplications, deletions, translocations, insertions and inversions) against an explicit model for the generation of experimental noise. This statistical model proves particularly useful for the detection of low frequency variants. SV detection performed on a large insert Mate-Pair library from a breast cancer sample revealed a high level of somatic duplications in the tumor and, to a lesser extent, in the blood sample as well.

SVmine

Employs a two-stage process to evaluate and refine structural variation (SV) predictions. SVmine is an algorithm for further mining of SV predictions from multiple algorithms to improve the sensitivity, specificity and breakpoint resolution of SV detection. It first performs quality evaluation and filters low quality SV predictions. Then, it refines breakpoint positions of the high quality SVs by performing precise “sandwich” realignments of soft-clipped reads. The realignment strategy used by SVmine can also be generalized to Pacbio long read data.