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CNV
| caller | orig pub | from | study | source |
|---|---|---|---|---|
| bic-seq2 | 2016 | Harvard, Park lab | study | source |
| clamms | 2016 | Regeneron Genetics Center | study | source |
| cnvkit | 2016 | UCSF | study | source |
| erds-pe | 2016 | Harbin Institute of Technology, China | study | source |
| ioncopy | 2016 | Charité University Hospital, Berlin | study | source |
| popsv | 2016 | McGill U Canada, Bourque lab | study | source |
| srbreak | 2016 | University of Otego, New Zealand | study | source |
| triocnv | 2016 | Harbin Institute of Technology, China | study | source |
| as-genseng | 2015 | UNC Chapel Hill | study | source |
| codex | 2015 | U Penn Philadelphia | study | source |
| conserting | 2015 | St Jude | study | source |
| copywriter | 2015 | Netherlands Cancer Institute | study | source |
| falcon | 2015 | UC Davis | study | source |
| grom-rd | 2015 | Grigoriev-Lab | study | source |
| modsara | 2015 | Yale, Zhang lab | study | source |
| abscn-seq | 2014 | UCSD, Messer | study | source |
| adtex | 2014 | U Melbourne, Halgamuge | study | source |
| canoes | 2014 | Columbia U | study | source |
| climat | 2014 | U Hefei, China | study | source |
| cnvcapseq | 2014 | Imperial College London | study | source |
| cnvoffseq | 2014 | Imperial College London, Coin | study | source |
| cnvrd2 | 2014 | University of Otego Dunedin, New Zealand | study | source |
| m-hmm | 2014 | NHGRI | study | source |
| oncocnv | 2014 | Curie Institute | study | source |
| patterncnv | 2014 | Mayo | study | source |
| pyloh | 2014 | UC Irvine | study | source |
| qdnaseq | 2014 | VU University Medical Center | study | source |
| cnvem | 2013 | UCLA | study | source |
| excavator | 2013 | U Florence | study | source |
| fishingcnv | 2013 | McGill U Canada, Majewski | study | source |
| matchclip | 2013 | U Pennsylvania, Philadelphia | study | source |
| oncosnpseq | 2013 | Imperial College London | study | source |
| patchwork | 2013 | Upsala University, Sweden | study | source |
| theta2 | 2013 | Brown, Raphael | study | source |
| absolute | 2012 | Broad, Getz | study | source |
| apolloh | 2012 | British Colombia Cancer Agency | study | source |
| cnanorm | 2012 | U Leeds | study | source |
| cnvhitseq | 2012 | Imperial College London | study | source |
| conifer | 2012 | U Wash Seattle | study | source |
| contra | 2012 | Peter MacCallum Cancer Centre | study | source |
| cops | 2012 | Institute of Applied Bioinformatics, Bangalore India | study | source |
| erds | 2012 | Duke University | study | N/A |
| exomedepth | 2012 | U Cambridge, Negentsev | study | source |
| magnolya | 2012 | Netherlands Bioinformatics Centre | study | source |
| seqcbs | 2012 | Stanford University | study | source |
| xhmm | 2012 | Mt Sinai, Purcell | study | source |
| bic-seq | 2011 | Harvard | study | source |
| cnvnator | 2011 | Yale, Gerstein | study | source |
| exomecnv | 2011 | Dana-Farber Cancer Institute | study | source |
| exomecopy | 2011 | U Oslo | study | source |
| jointslm | 2011 | Careggi Hospital, Italy | study | source |
| readdepth | 2011 | Baylor College of Medicine | study | source |
| cnaseg | 2010 | Li Ka Shing Centre, UK | study | source |
| cnver | 2010 | U Toronto | study | source |
| copyseq | 2010 | EMBL | study | source |
| freec | 2010 | Institut Curie, Barillot | study | source |
| novelseq | 2010 | Simon Fraser University, Canada | study | source |
| rsw-seq | 2010 | Harvard Medical School, Park lab | study | source |
| cmds | 2009 | WashU St Louis, Province | study | source |
| cnv-seq | 2009 | National University of Singapore | study | source |
| rdxplorer | 2009 | Cold Spring Harbor | study | source |
| segseq | 2009 | Broad Institute | study |
Notes: somatic calls.
Notes: exome input. exome capture data, normalizes GC content
Algorithm: HMM/mixture model
Description: Copy number estimation using Lattice-Aligned Mixture Models. Evaluate the adherence of CNV calls from CLAMMS and four other algorithms to Mendelian inheritance patterns on a pedigree
Notes: targeted sequencing input. somatic calls. targeted reads, uses off-target reads
Used by: biocondor
Algorithm: CBS algorithm (circular binary segmentation)
Description: CNV detection that takes advantage of both on– and off-target sequencing reads and applies a series of corrections to improve accuracy in copy number calling.
Algorithm: paired HMM
Notes: for panel/amplicon, tumor population only; no normal controls used
Algorithm: amplicon read depth population statistics calling from tumor-only cohort.
Description: estimate a null distribution of copy numbers using outlier-robust statistics and assess the significance of CNAs by comparison with this null distribution. In this way, p-values are obtained for each amplicon in each tumor that are subsequently corrected for multiple hypothesis testing. ... For all simulated situations, CN gains of 5 and more can be detected with high sensitivity and specificity. Detection of CN gains of 4 is feasible in some situations, for example when the number of genes under investigation is low.
Notes: population-based calls.
Description: Population-based detection of structural variation from High-Throughput Sequencing
Algorithm: read-depth first for CNV-region detection, followed by split read analysis to locate breakpoints.
Compared to: Pindel, DELLY, MATCHCLIP, SoftSearch, CNVnator
Description: combines a read-depth-based approach and a split-read-based approach to identify breakpoints for different duplication/deletion events inside a large CNVR. The strength of this pipeline comes through its use of multiple samples in one CNV genotype group to identify common breakpoints for that group. It is able to use both single-end and paired-end reads from HTS data.
Notes: trio calls.
Notes: combines allele-specific RC with total RC
Algorithm: HMM/model total and allele specific separate
Description: incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data
Notes: exome only. population-based calls. includes terms that specifically remove biases due to GC content, exon length and capture and amplification efficiency, and latent systematic artifacts
Algorithm: Poisson latent factor/recursive segmentation
Description: relies on the availability of multiple samples processed using the same sequencing pipeline. Unlike current approaches, CODEX uses a Poisson log-linear model that is more suitable for discrete count data. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and capture and amplification efficiency, and latent systematic artifacts
Notes: Produces CNV/SV calls. dep on bambino, picard
Algorithm: regression tree segmentation
Description: integrate read-depth change with structural variation (SV) identification through an iterative process of segmentation by read depth, segment merging, and localized SV detection. recursive partitioning techniques to find the transition point for read depth changes.
Notes: for targeted sequencing. reference-free
Algorithm: CBS algorithm, uses off-target sequencing reads
Description: exploiting ‘off-target’ sequence reads. CopywriteR allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels
Notes: somatic calls. calculates allele-specific copy numbers, deduce clonal history
Algorithm: bivariate mixed Binomial, Bayesian criterion for count estimates
Description: based on a change-point model on a bivariate mixed Binomial process, which explicitly models the copy numbers of the two chromosome haplotypes and corrects for local allele-specific coverage biases. By using the Binomial distribution rather than a normal approximation, falcon more effectively pools evidence from sites with low coverage
Notes: wgs, no control req
Description: excessive coverage masking, GC bias mean and variance normalization, GC weighting, dinucleotide repeat bias detection and adjustment, and a size-varying sliding window CNV search.
Algorithm: screening and ranking algo
Notes: estimates purity & ploidy. exome only.
Notes: estimates purity and ploidy. exome only. testing somatic calling.
Algorithm: HMM
Description: uses two Hidden Markov Models to predict copy number and genotypes and computationally resolves polyploidy/aneuploidy, normal cell contamination and signal baseline shift. Our method makes explicit detection on chromosome arm level events, which are commonly found in tumour samples
Notes: exome only.
Algorithm: models sequence coverage using the negative binomial distribution
Notes: estimates LOH. robust to contamination and aneuploidy
Description: takes integrated analysis of read count and allele frequency derived from sequenced tumor samples, and provides extensive data processing procedures including GC-content and mappability correction of read count and quantile nor-malization of B allele frequency
Notes: targeted resequencing input.
Description: cnvCapSeq integrates evidence from both RD and read pairs (RP) to achieve high breakpoint resolution regardless of coverage uniformity
Description: normalization framework for off-target read depth that is based on local adaptive singular value decomposition (SVD). This method is designed to address the heterogeneity of the underlying data and allows for accurate and precise CNV detection and genotyping in off-target regions.
Validated vs: cnvnator, cn.mops
Description: first uses observed read-count ratios to refine segmentation results in one population. Then a linear regression model is applied to adjust the results across multiple populations, in combination with a Bayesian normal mixture model to cluster segmentation scores into groups for individual CN counts.
Algorithm: HMM
Notes: amplicon input.
Description: defining a method to normalize read coverage with a small set of normal control samples and (ii) assigning statistical significance to putative CNAs resulting from the segmentation of normalized profiles
Notes: exome only. somatic calls. WIG format bams for speed
Algorithm: compares paired samples
Description: accounts for the read coverage variations between exons while leveraging the consistencies of this variability across different samples
Notes: estimates LOH, purity, ploidy.
Used by: biocondor
Description: deconvolve read mixture to identify reads associated with tumor cells or a particular subclone of tumor cells. Integrate somatic copy number alterations and loss of heterozygosity in a unified probabilistic framework.
Notes: shallow depth ok, robust to FFPE
Algorithm: read-depth, no paired analysis needed
Description: use maximum likelihood to estimate locations and copy numbers of copied regions and implement an expectation-maximization (EM) algorithm
Notes: exome only.
Algorithm: HMM
Description: uses ratio of tumour and matched normal average read depths at each exonic region, to predict the copy gain or loss
Notes: exome only. efficient processing
Algorithm: read-count/HMM based with 3-step normalization, segmentation
Description: combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number state
Description: compares coverage depth in a test sample against a background distribution of control samples and uses principal component analysis to remove batch effects
Description: Our method searches for reads that potentially span the breakpoints of a CNV by screening CIGAR strings. If a long S part is at the 3′(right)-side, we can use its alignment to determine the 5′(left)-side of the breakpoint, and vice versa. Our method searches for two reads that span the same CNV with the long soft-clipped parts at the either end in order to locate both breakpoints of the CNV. To ensure the two reads indeed cover the same CNV, we require that they overlap in a certain orientation and their common string includes both of the soft-clipped parts.
Algorithm: mixed Binomial model for multiple tumor genotypes contaminated with normal cells, HMM resolves most likely set of mixtures.
Notes: uses WGS input.
Notes: estimates purity & ploidy. somatic calls. estimates purplo, efficient
Used by: biocondor
Algorithm: maximum likelihood mixture decomposition problem
Description: infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations
Notes: estimates LOH.
Algorithm: estimates loss of heterozygosity
Description: detect subclonal heterogeneity and somatic homozygosity, and it can calculate statistical sensitivity for detection of specific aberrations
Notes: estimates LOH. somatic calls.
Algorithm: estimates loss of heterozygosity.
Description: a hidden Markov model (HMM) for predicting somatic loss of heterozygosity and allelic imbalance in whole tumour genome sequencing data.
Description: identify the multi-modality of the distribution of smoothed ratios. Then we use the estimates of the mean (modes) to identify underlying ploidy and the contamination level, and finally we perform the correction.
Notes: somatic calls.
Description: jointly models evidence from RD, RPs and SRs at the population level. pool information across individual samples and reconcile copy number differences among data sources
Notes: exome only. population-based calls.
Description: this method can be used to reliably predict (94% overall precision) both de novo and inherited rare CNVs involving three or more consecutive exons
Notes: exome only.
Algorithm: CBS algorithm
Description: calls copy number gains and losses for each target region based on normalized depth of coverage. Our key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation
Notes: available?
Description: starts from read depth (RD) information, and integrates other information including paired end mapping (PEM) and soft-clip signature to call CNVS
Notes: exome only. de novo? mendelian
Used by: biocondor
Description: Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
Notes: reference-free.
Description: enables copy number variation (CNV) detections without using a reference genome. Magnolya directly compares two next-generation sequencing datasets.
Description: based on a simple and flexible inhomogeneous Poisson Process model for sequenced reads. We derive the score and generalized likelihood ratio statistics for this model to detect regions where the read intensity shifts in the target sample, as compared to a reference. We construct a modified Bayes information criterion (mBIC) to select the appropriate number of change points and propose Bayesian point-wise confidence intervals as a way to assess the confi- dence in the copy number estimates.
Notes: exome only.
Algorithm: PCA/HMM
Description: uses principal component analysis (PCA) normalization and a hidden Markov model (HMM) to detect and genotype copy number variation (CNV) from normalized read-depth data from targeted sequencing experiments.
Notes: somatic
Description: Combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline copy number variations
Notes: no control req
Used by: metasv
Algorithm: read coverage
Description: CNVnator is able to discover CNVs in a vast range of sizes, from a few hundred bases to megabases in length
Notes: exome only.
Description: a statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies, from mapped short sequence reads
Notes: exome only.
Description: an HMM for predicting copy number state in exome and other targeted sequencing data using observed read counts and positional covariates
Notes: population-based.
Notes: no control req
Notes: somatic calls.
Description: supplements the depth-of-coverage with paired-end mapping information, where mate pairs mapping discordantly to the reference serve to indicate the presence of variation.
Notes: no control req
Description: The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells.
Description: discover the content and location of long novel sequence insertions
Notes: somatic calls.
Description: correlation matrix diagonal segmentation (CMDS), identifies RCNAs based on a between-chromosomal-site correlation analysis.
Notes: somatic calls
Notes: no contol req
Description: copy number variants (CNV) detection in whole human genome sequence data using read depth (RD) coverage. CNV detection is based on the Event-Wise Testing (EWT) algorithm
Notes: somatic calls