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Control-FREEC / Control-FREE Copy number and allelic content caller
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Automatically detects copy number alterations (CNAs) and loss of heterozygosity (LOH) regions using next-generation sequencing (NGS) data. Control-FREEC consists of three steps: (i) calculation and segmentation of copy number profiles, (ii) calculation and segmentation of smoothed BAF profiles; and (iii) prediction of final genotype status. The software can call genotype status including when no control experiment is available and/or the genome is polyploid. It also corrects for GC-content and mappability biases.
forestSV
Integrates prior knowledge about the characteristics of structural variants (SVs). forestSV is a statistical learning approach, based on Random Forests (RFs) that leads to improved discovery in high throughput sequencing (HTS) data. This application offers high sensitivity and specificity coupled with the flexibility of a data-driven approach. It is particularly well suited to the detection of rare variants because it is not reliant on finding variant support in multiple individuals.
PEMer
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.
GRIDSS / Genomic Rearrangement IDentification Software Suite
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.
GenomeVIP / Genome Variant Investigation Platform
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.
SvABA / Structural variation and indel Analysis By Assembly
Detects structural variants (SVs) from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. SvABA’s performance was evaluated on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs, and substantially improved detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (< 1,000 bp) templated-sequence insertions copied from distant genomic regions. SvABA was applied to 344 cancer genomes from 11 cancer types, and found that templated-sequence insertions occur in approximately 4% of all somatic rearrangements. Finally, SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized SVs.
SVelter
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.
SV2 / support-vector structural-variant genotyper
Permits genotyping deletions and tandem duplications from paired-end whole genome sequencing (WGS) data. SV2 consists of a supervised support vector machine (SVM) classifier that employs read depth, discordant paired-ends, and split-reads to work. It includes variant calls from multiple structural variant discovery algorithms into a unified call set with low rates of false discoveries. This tool aims to ease genotyping, likelihood estimation and analysis of structural variation (SV) association.
SlideSort-BPR
Identifies breakpoints from a next-generation sequencing (NGS) dataset without knowledge of a reference genome. SlideSort-BPR performs direct comparisons of reads from different samples. It determines the reads associated to the breakpoints by finding the groups of reads that are “unbalanced” between two sets of samples. It is robust to the presence of repeats in the genome and to several sequencing errors and can find the NGS reads associated with breakpoints between samples from normal cells and samples from cancer cells.
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.
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.
NAIBR / Novel Adjacency Identification with Barcoded Reads
Recognizes novel adjacencies resulting from structural variants in an individual genome from linked-read sequencing data. NAIBR combines a split-read type signal from linked-reads with signals of structural variants in the underlying paired-reads in the data. It was tested on the detection of somatic structural variants in tumor cell line HCC1954T. This tool enables the identification of novel adjacencies arising from small structural variants.
Wham / WHole-genome Alignment Metrics
A structural variant (SV) caller that integrates several sources of mapping information to identify SVs. Wham classifies SVs using a flexible and extendable machine-learning algorithm (random forest). Wham is not only accurate at identifying SVs, but its association test can identify shared SVs enriched in a cohort of diseased individuals compared to a background of healthy individuals. Wham is designed for paired-end Illumina libraries with standard insert sizes (~300bp-500bp). It integrates mate-pair mapping, split read mapping, soft-clipping, alternative alignment and consensus sequence based evidence to predict SV breakpoints with single-nucleotide accuracy. Wham can be easily run as a stand-alone tool or as part of gkno or bcbio-nextgen pipelines.
SVClassify
Calculates annotations from one or more aligned bam files from many high-throughput sequencing technologies, and then builds a one-class model using these annotations to classify candidate structural variants (SVs) as likely true or false positives. SVClassify method gives the highest scores to SVs that are insertions or large homozygous deletions, and have accurate breakpoints. Deletions smaller than 100-bps often have low scores with our method, so other methods like svviz are likely to give better results for very small SVs.
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