A tool designed for efficient and accurate variant-detection in high-throughput sequencing data. By using local realignment of reads and local assembly it achieves both high sensitivity and high specificity. Platypus can detect SNPs, MNPs, short indels, replacements and (using the assembly option) deletions up to several kb. It has been extensively tested on whole-genome, exon-capture, and targeted capture data.
Assists users in detecting copy number variants (CNVs) and alterations genome-wide from high-throughput sequencing. CNVkit is a python library that removes technical biases and infers copy number preferentially from stably expressed genes, highly expressed genes, and genes whose expression is known to be closely connected to copy number. This library is designed to operate on a batch of samples for which gene expression data is available.
A method that includes a multifactor normalization and annotation technique enabling the detection of large copy number changes from amplicon sequencing data. This approach was validated on high and low amplicon density datasets and demonstrated that ONCOCNV can achieve a precision comparable with that of array CGH techniques in detecting copy number aberrations. Thus, ONCOCNV applied on amplicon sequencing data would make the use of additional array CGH or SNP array experiments unnecessary.
Provides a normalization and copy number variations (CNV) calling procedure for whole exome sequencing (WES) data. CODEX provides a R package for improving sensibility and specificity in detecting both common and rare CNV. It was designed to be applied for two experimental cases: in case-control to detect CNVs that are enriched in the case samples or to profile all CNVs in all samples when there are no control samples.
A tool for the detection of copy number aberrations from targeted sequencing. All currently available methods are based on exonic depth of coverage, and suffer from the problems that bait efficiencies are non-uniform and that exons are irregularly distributed over the genome. By exploiting the off-target sequence reads, CopywriteR bypasses these problems. It allows for extracting DNA copy number profiles of a high quality comparable to those of ‘dedicated’ techniques such as SNP array, arrayCGH and low-coverage whole-genome sequencing techniques.
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 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.
A comprehensive analysis platform for the processing, analysis and visualization of structural variation based on sequencing data or genomic microarrays, enabling the rapid identification of disease loci or genes. Vivar allows you to scale your analysis with your work load over multiple (cloud) servers, has user access control to keep your data safe but still easy to share, and is easy expandable as analysis techniques advance.
Assists users in discovering and scoring structural variants (SVs), medium-sized indels and large insertions. Manta was developed to discover variants from a sequencing assay’s paired and split-read mapping information. It automates estimation of insert size distribution and exclusion of high depth reference compression regions. This method also includes scoring models for germline analysis of diploid individuals and somatic analysis of tumor-normal sample pairs.
Aims to rapidly identify copy number variations (CNVs) responsible for inherited diseases among amplicons sequencing data generated by next-generation sequencing (NGS). Cov'Cop simultaneously analyzes all the patients of the run's coverage file provided by the sequencer, using a two-stage algorithm containing correction and normalization levels. It provides an easily understandable output, showing with various colors, potentially deleted and duplicated amplicons.
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 copy number variations (CNVs) – random forest from targeted next-generation sequencing (NGS). CNV-RF includes a machine learning component for the identification of CNVs and confirms positive deletions and duplications via a quantitative polymerase chain reaction (PCR) method. It can also identify single-nucleotide variants, small indels for targeted gene panels and clinically significant regions of deletions and duplications.
Serves as small (single-exon) copy number variation (CNV) detection tool in high coverage next-generation sequencing (NGS) data. CoNVaDING exploits a group of possible control samples that are utilized for read-depth normalization on all autosomal targets and on all targets per gene. It calculates the ratio score for the read depth of the sample and a distribution score for the number of standard deviations. The software provides also three quality control (QC) metrics.
Finds structural variant breakpoints in Illumina paired-end next-generation sequencing (NGS) data. SoftSearch is a breakpoint detection tool for paired-end NGS instruments that uses multiple sequence features to infer breaks point, for characterizing location and type of structural variants. The software can identify large Insertions, large deletions, inversions, tandem duplications, novel sequence insertion locations, and chromosomal translocations.
Allows analysis and integrative visualization of copy number variants (CNVs). GenomeCAT is a standalone application that provides comprehensive tools for the analysis of DNA CNVs. The software facilitates the evaluation of their biological relevance in the context of genome annotations and results obtained from different experiment types. Moreover, it can act as an interface to other software tools since results generated in GenomeCAT can be exported in standard file formats.
Uses capture next-generation sequencing (NGS) data. SeqCNV identifies the copy number ratio and copy number variant (CNV) boundary by extracting the read depth information and utilizing the maximum penalized likelihood estimation (MPLE) model. It was applied to both bacterial artificial clone (BAC) and human patient NGS data to identify CNVs. It shows a significant improvement in both sensitivity and specificity. The tool appears to be a robust way to identify CNVs of different size using capture NGS data.
A set of algorithms and heuristics to identify indels from whole genome resequencing datasets using paired-end reads. indelMINER uses a split-read approach to identify the precise breakpoints for indels of size less than a user specified threshold, and supplements that with a paired-end approach to identify larger variants that are frequently missed with the split-read approach.
Applies statistical methods to detect copy number variations (CNV) in a sample by comparing the read counts from next generation sequencing (NGS) performed after PCR-enrichment of regions of interest, typically a set of genes with known or expected relevance for the sample, e.g. genes that play a role in cancer. Counts from a normal control (ideally matched normal from the same individual, e.g. healthy tissue) are required.
Detects large duplications and deletions in targeted sequencing data. Convector is built on a machine learning algorithm and suits for sequencing data created with polymerase chain reaction (PCR)-based enrichment step. This approach relies on PCR efficiency correlations for subsets of amplicons in highly multiplex PCR. It has no limitations on copy number variation (CNV) site size and does not need control datasets or priory knowledge on potential CNVs sites location.
A package for the detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. exomeCopy implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. Simulations show high sensitivity for detecting heterozygous and homozygous CNVs, outperforming normalization and state-of-the-art segmentation methods.
Estimates tumor purity, copy number, loss of heterozygosity (LOH), and status of single nucleotide variants (SNVs). PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for tumor samples without matching normal samples. It integrates standard GATK-based pipelines, utilizes standard Bioconductor infrastructure for data import and export, supports both matched and unmatched samples, and was tested on targeted panels.
Permits the utilization of a collection of non-matched normal tissue samples. CNVPanelizer employs a non-parametric bootstrap subsampling of the available reference samples to identify the distribution of read counts from targeted sequencing. It allows to classify the copy number aberrations on the gene level.
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