CNVkit is a Python library and command-line software toolkit to infer and visualize copy number from high-throughput DNA sequencing data. It is designed for use with hybrid capture, including both whole-exome and custom target panels, and short-read sequencing platforms such as Illumina and Ion Torrent.

Google Scholar lists some of the studies where CNVkit has been used by other researchers. CNVkit stable. Quick start Install CNVkit Download the reference genome Map sequencing reads to the reference genome Build a reference from normal samples and infer tumor copy ratios Next steps. How does it work? CNVkit: Genome-wide copy number detection and visualization from targeted sequencing. Who else is using CNVkit? Cell3 Gene copy number estimation from targeted next generation sequencing of prostate cancer biopsies: Analytic validation and clinical qualification.

Evolution of metastasis revealed by mutational landscapes of chemically induced skin cancers. Nature Medicine21, — Shain, A. Nature Genetics47 10Shain, A. Read the Docs v: stable Versions latest stable v0. Apache License 2.Create your free GitHub account today to subscribe to this repository for new releases and build software alongside 40 million developers. This release contains several major enhancements particularly relevant to germline analysis.

If used in production pipelines, further evaluation and benchmarking would be wise. Control sample clustering : To make better use of larger reference sample pools, reference --cluster will correlate the given normal samples' bin-wise coverage depths to extract clusters to be used as reference profiles.

The reference. Given this "clustered reference" profile, fix --cluster will then correlate each test sample to each clustered log2 profile in the reference to choose the most relevant control pool for normalization. The batch option --cluster will perform both these steps.


Calculation of bin weights has changed. This will change your segmentation resultshopefully for the better. Details below. The bin- and segment-level results are returned as separate. This is a beta release. Please let me know how it works for you via the Issues page. If this release contains any issues that are blocking your work, try installing one of the previous stable versions 0. Essential maintenance and bug fixes, for the most part. Some key dependencies have changed, though this should be generally painless for you, and one or two regressions introduced by recent optimizations have been fixed.

This will be the last CNVkit version to run on Python 2. The next major release of pandas 0. For now, segment -m flasso is still supported if you already have cghFLasso installed.

Performance improvements and bug fixes. Improved automated testing and documentation Optimized performance of selecting genomic intervals, in particular speeding up callsegmentand segmetrics for whole genome and exome datasets.You probably already have the reference genome sequence.

High Performance Secondary Analysis of Genomic Data

Both the reference genome sequence and the annotation database must be single, uncompressed files. Gene annotations: The gene annotations file refFlat. This file can be used in the next step. CNVkit uses the bait BED file provided by the vendor of your capture kitreference genome sequence, and optionally sequencing-accessible regions along with your BAM files to:.

All of these steps are automated with the batch command. In either case, you should run this command with the reference genome sequence FASTA file to extract GC and RepeatMasker information for bias corrections, which enables CNVkit to improve the copy ratio estimates even without a paired normal sample. If your targets are missing gene names, you can add them here with the --annotate argument:. See also: Whole-genome sequencing and targeted amplicon capture. The coordinates of the target and antitarget bins, the gene names for the targets, and the GC and RepeatMasker information for bias corrections are automatically extracted from the reference.

This should usually work fine. For the careful: Run batch with just the normal samples specified as normal, yielding coverage. Inspect the coverages of all samples with the metrics command, eliminating any poor-quality samples and choosing a larger or smaller antitarget bin size if necessary. Build an updated pooled reference using batch or coverage and reference see Copy number calling pipelinecoordinating your work in a MakefileRakefile, or similar build tool.

For the power user: Run batch with all samples specified as tumor samples, using -n by itself to build a flat referenceyielding coverages, copy ratios, segments and optionally plots for all samples, both tumor and normal. Use a framework like bcbio-nextgen to coordinate the complete sequencing data analysis pipeline. CNVkit stable. To run CNVkit on your own machine, keep reading.

If your targets look like: chr1 chr1 chr1 You should now have one or BAM files corresponding to individual samples.

CNVkit 0.9.6

CNVkit uses the bait BED file provided by the vendor of your capture kitreference genome sequence, and optionally sequencing-accessible regions along with your BAM files to: Create a pooled reference of per-bin copy number estimates from several normal samples; then Use this reference in processing all tumor samples that were sequenced with the same platform and library prep.Please contact info parabricks. This is driving increased research and clinical applications. As a result, the number of human genomes sequenced is predicted to double every year and transform the diagnosis and treatment of diseases, leading to a disruptive change in modern medicine.

Parabricks brings high performance computing technologies that are tailored for NGS analyses and accelerates the standard NGS software from several days to approximately one hour. The accelerated software is a drop-in replacement of existing tools that does not sacrifice output accuracy or configurability. Parabricks accelerates existing GATK 4 best practices to generate equivalent results as the baseline. The image below Figure 1 shows the pipeline currently supported by Parabricks.

The aligned output is then coordinate sorted, followed by marking the duplicates. This is the first output of the standard pipeline in binary alignment map BAM format. Finally, a variant caller is used depending on the task at hand.

The hardware and system software configurations are summarized below. One such server can analyze 48 whole genomes at 10x coverage per day.

In comparison, a similar CPU-only solution can process only about 8 genomes per day. This 6-fold increase in throughput with the Parabricks GPU solution results in large savings in the Total Cost of Ownership by reducing hardware, IT management, cooling, power, and maintenance costs for centers processing large volumes of genomic data.

Features of Parabricks software times faster analysis : Compared to a CPU-only solution, Parabricks accelerates secondary analysis by orders of magnitude. Single Node Execution : The entire pipeline is run using one computing node and does not incur any overhead of distributing data and work across multiple servers. Turnkey Solution : Parabricks software runs on standard CPU and GPU nodes available on the cloud or on-premise, and requires no additional setup steps by the user.

Quick Tips content is self-published by the Dell Support Professionals who resolve issues daily. In order to achieve a speedy publication, Quick Tips may represent only partial solutions or work-arounds that are still in development or pending further proof of successfully resolving an issue. As such Quick Tips have not been reviewed, validated or approved by Dell and should be used with appropriate caution. Dell shall not be liable for any loss, including but not limited to loss of data, loss of profit or loss of revenue, which customers may incur by following any procedure or advice set out in the Quick Tips.Germline copy number variants CNVs and somatic copy number alterations SCNAs are of significant importance in syndromic conditions and cancer.

Massively parallel sequencing is increasingly used to infer copy number information from variations in the read depth in sequencing data. However, this approach has limitations in the case of targeted re-sequencing, which leaves gaps in coverage between the regions chosen for enrichment and introduces biases related to the efficiency of target capture and library preparation. We present a method for copy number detection, implemented in the software package CNVkit, 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 kilobase resolution genome-wide from a platform targeting as few as 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. PLoS Comput Biol 12 4 : e This is an open access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Corresponding raw sequencing data for the melanoma samples were deposited in the database of Genotypes and Phenotypes dbGaP under accession phs C cell line raw data from the Botton study are in the Supporting Information files of that article.

The authors of both studies may be contacted at boris. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Copy number changes are a useful diagnostic indicator for many diseases, including cancer. The gold standard for genome-wide copy number is array comparative genomic hybridization array CGH [ 12 ].

More recently, methods have been developed to obtain copy number information from whole-genome sequencing data [ 3 ]; reviewed by [ 4 ]. For clinical use, sequencing of genome partitions, such as the exome or a set of disease-relevant genes, is often preferred to enrich for regions of interest and sequence them at higher coverage to increase the sensitivity for calling variants [ 5 ]. MOPS [ 20 ]. However, these approaches do not use the sequencing reads from intergenic and, usually, intronic regions, limiting their potential to infer copy number across the genome.

During the target enrichment, targeted regions are captured by hybridization; however, a significant quantity of off-target DNA remains in the library, and this DNA is sequenced and represents a considerable portion of the reads.Log In.


Welcome to Biostar! Limit to: all time all time today this week this month this year. Sort by: update update views followers answers bookmarks votes rank creation. The center in scatter plot generated by CNVkit looks off. Analysis using CNVkit tool. How to understand CNVkit output. How are the log2 values in CNVkit. Flat reference vs. CNVKit interpretation of results?

CNVkit 0.9.6

Help with understanding CNVkit output. Error segmentation steps. How to use CNVKit. BAF without normal control, to do or not! CNVkit - diagram problem. Weights in cnvkit cnr output. CNVKit scatter graph will not plot individual chromosomes. Passing cnvkit output to pureCN to account for cellularity. Recent Votes.

C: comparing 3 VCF files for concordance and VENN plotting C: Filtering SNPs from haploid assembly C: comparing multiple call files to a baseline file with bcftools sec C: comparing multiple call files to a baseline file with bcftools sec A: comparing multiple call files to a baseline file with bcftools sec. I hadn't noticed that - many thanks!Sequencing data were acquired from patients underwent routine clinical targeted panel sequencing testing.

The sizes of CNVs detected are slightly larger Copy number variations covering adequate exons on autosomes can be accurately detected using targeted panel sequencing data as using CMA.

CNVs detected from sex chromosomes need further evaluation and validation. Copy number variants CNVs contribute to a large fraction of human genetic variation and have been known to play important roles in human diseases and evolution Lupski, In this study, we set to assess the analytical validity of CNV detection using CNVkit based on limited sequencing data extracted from targeted panel.

A total of patients underwent genetic testing at the Department of Medical Genetics, Shanghai Children's Medical Center from October to September were recruited in this study. Patients were informed of the risks and benefits and provided written informed consent for targeted panel sequencing. Clusters were then generated by isothermal bridge amplification using an Illumina cBot station, and sequencing was performed on an Illumina HiSeq System Illumina, Inc.

The raw data fastq file for each patient were obtained for CNV identification. The average sequencing depth of data used wasand more that Copy number variations were identified using open source software called CNVkit Talevich et al. Burrows Wheeler Alignment tool v0. Normal reference used for CNV identification were constructed using sequencing data from 10 normal males and 10 females which have previously validated without pathogenic CNVs by CMA.

No GA, Agilent technologies, Inc. Labeling and hybridization were performed following standard protocols. The derivative log ratio spread DLRS was used for quality control. Data were visualized and analyzed with Agilent CytoGenomics software.


The size accuracy of CNV detected by CNVkit is evaluated against the sizes of variants detected by CMA minimal interval referred by array probes which was considered as standard. As a result, sizes inferred by CNVkit are slightly larger average around Among them, 46 submicroscopic variants are listed according to their size from chromosome microarray analysis detection.

The proximal locations of CNV breakpoints are also evaluated. Percentage of shifting and altering are calculated based on detail coordinates generated by respective methods. Two variants with relatively larger altered or shifted percentage of margin A schematic diagram of variants category for breakpoint estimation evaluation.

Duplications on chromosome X in patient 3, were detected but unreported by the CNVkit due to gender identification error. Three duplications on chromosome X were undetected shaded rows.

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