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A software tool for copy number detection that uses both the targeted reads and the nonspecifically captured off-target reads to infer copy number evenly across the genome. This combination achieves both exon-level resolution in targeted regions and sufficient resolution in the larger intronic and intergenic regions to identify copy number changes. In particular, we successfully inferred copy number at equivalent to 100-kilobase resolution genome-wide from a platform targeting as few as 293 genes. After normalizing read counts to a pooled reference, we evaluated and corrected for three sources of bias that explain most of the extraneous variability in the sequencing read depth: GC content, target footprint size and spacing, and repetitive sequences. We compared the performance of CNVkit to copy number changes identified by array comparative genomic hybridization. We packaged the components of CNVkit so that it is straightforward to use and provides visualizations, detailed reporting of significant features, and export options for integration into existing analysis pipelines.
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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.
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.
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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.
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.
CoNVaDING / Copy Number Variation Detection In Next-generation sequencing Gene panels
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.
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 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.
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