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SVTyper
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.
MATE-CLEVER / Mendelian-inheritance-AtTEntive CLique-Enumerating Variant finder
An approach that accurately discovers and genotypes indels longer than 30 bp from contemporary NGS reads with a special focus on family data. For enhanced quality of indel calls in family trios or quartets, MATE-CLEVER integrates statistics that reflect the laws of Mendelian inheritance. MATE-CLEVER's performance rates for indels longer than 30 bp are on a par with those of the GATK for indels shorter than 30 bp, achieving up to 90% precision overall, with >80% of calls correctly typed. In predicting de novo indels longer than 30 bp in family contexts, MATE-CLEVER even raises the standards of the GATK.
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.
HySA / Hybrid Structural variant Assembly
Integrates sequencing reads from next-generation sequencing (NGS) and single-molecule sequencing (SMS) technologies to accurately assemble and detect structural variations (SV) in human genome. By identifying homologous SV-containing reads from different technologies through a bipartite-graph-based clustering algorithm, our approach turns a whole genome assembly problem into a set of independent SV assembly problems, each of which can be effectively solved to enhance assembly of structurally altered regions in human genome.
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.
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.
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.
SWAN / Statistical Structural Variant Analysis for NGS
A statistical framework and algorithm for structural variant (SV) detection from whole genome sequencing data. SWAN integrates multiple features, including insert size, hanging read pairs and read coverage into one statistical framework and detects putative SVs through genome-wide likelihood ratio scans. SWAN remaps soft-clip/split read clusters to supplement the likelihood analysis, joins multiple sources of evidence and identifies break points whenever possible. SWAN has improved sensitivity for detecting structural variants smaller than 10 kilobases and is particularly successful at identifying deletions smaller than 500 base pairs.
Manta
Calls structural variants (SVs) and indels from mapped paired-end sequencing reads. Manta is optimized for analysis of individuals and tumor/normal sample pairs, calling SVs, medium-sized indels and large insertions within a single workflow. The method is designed for rapid analysis on standard computer hardware: NA12878 at 50x genomic coverage is analyzed in less than 20 minutes on a 20 core server, most WGS tumor-normal analyses can be completed within 2 hours. Manta combines paired and split-read evidence during SV discovery and scoring to improve accuracy, but does not require split-reads or successful breakpoint assemblies to report a variant in cases where there is strong evidence otherwise. It provides scoring models for germline variants in individual diploid samples and somatic variants in matched tumor-normal sample pairs.
Seeksv
Detects somatic structural variations (SVs) and viral integration events. Seeksv simultaneously uses split read signal, discordant paired-end read signal, read depth signal and the fragment with two ends unmapped. It can detect deletion, insertion, inversion and interchromosome transfer at single-nucleotide resolution. Unlike others methods, seeksv merges soft clipped-reads from the same breakpoint into a clipped long sequence individually and does not rely on any of the assembly software.
MetaSV
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.
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.
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.
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.
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.
Tardis
Permits to automate and discover structural variations (SVs). Tardis is a toolkit that integrates read pair, read depth, and split read (using soft clipped mappings) sequence signatures to discover several types of SV, while resolving ambiguities among different putative SVs. This application is suitable for cloud use as the memory footprint is low. It is also capable of characterizing deletions, small novel insertions, tandem duplications, inversions, and mobile element retrotransposition.
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.
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.
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