reconCNV | visualize CNV data from targeted capture based sequencing | Genomics library

The easiest way to get started with reconCNV is via conda. Using conda ensures you are running Python 3.6 (using which reconCNV was coded) and all dependencies are installed within an virtual environment. This avoids dependency conflicts with existing programs. If conda is not available on your system, a minimal installer can be installed using Miniconda.
Clone the reconCNV repository git clone https://github.com/rghu/reconCNV.git To use the Docker container see instructions under Usage ...
Create your virtual environment. In the below example we are creating a virtual environment called "reconCNV". conda env create -f environment.yml
Activate your virtual environment. conda activate reconCNV
You are now ready to use reconCNV!
Once you are done using the virtual environment you can exit it. conda deactivate
At the minimum we need the ratio file and genome file to generate a plot using reconCNV. First make sure values of keys within the "column_names" field in the JSON configuration file matches those seen on the header of ratio and genome files. In this example we will use copy number analysis performed using CNVkit. Illumina sequencing for the HT-29 colon cancer cell line was performed using a 124 gene hybridization-based capture panel. data/sample_data directory contains the input files required for this example. See Input section below for details. ratio file: data/sample_data/HT-29.cnr - contains coordinates and log2(FC) of bins. segmentation file: data/sample_data/HT-29.cns - contains coordinates and log2(FC) of copy number segments. gene file: data/sample_data/HT-29.genemetrics.cns - contains coordinates and gene-level CNV log2(FC). VCF file: data/sample_data/HT-29.vcf - contains information on genotyped SNP loci. annotation file: data/hg19_COSMIC_genes_model.txt genome file: data/hg19_genome_length.txt - contains chromosome length and cumulative genomic length of chromosomes. Create output "results" directory. Run the command below to generate a HTML file with the visualization that can be opened on any modern web browser preferably Google Chrome. In this example we have generated two plots representing the CNV data for HT-29 cell line using genome coordinates as well as bin indices (sequential lineup of bins). Various tools (top left corner in the image below) can be used to interact with the data. Tools Pan: used for dragging the plot. Box Select: used to perform a rectangular selection on the x-axis highlighting a genomic region. The selection simultaneously occurs on the "Bin Data" table as well. Box Zoom: used to perform a rectangular zoom to a region of the plot. Wheel Zoom: used to zoom in and out based on the current location of the mouse in the x-axis. Tap Tool: click on any plot feature to open UCSC genome browser with those genome coordinates. Reset Plot: return the plot to its original view. Save View: export a PNG file of the current view. Zoom In: zoom in by clicking the tool to the center of the plot. Zoom Out: zoom out by clicking the tool to the center of the plot. Crosshair: enable/disable display of crosshairs. Hover: enable/disable display of annotation when hovering over plot features. Now provide the segmentation file data/sample_data/HT-29.cns to annotate the copy number segments to the output HTML file. Next, when we provide the gene file data/sample_data/HT-29.genemetrics.cns to reconCNV we get a feature to select genes of interest for subsetting/filtering using a multiselect box. The user can also select multiple genes of interest to filter for by holding the cntrl (Windows/Linux) or cmd (Mac) and selecting the gene names from the list. It also has quick selections such as filtering only amplifications and losses or simply just losses or amplifications satisfying the log2(FC) threshold set in the configuration file. We can also plot the SNP frequencies by providing reconCNV a VCF file. This feature was designed to retrieve potential heterozygous SNP sites from a tumor only analysis by genotyping polymorphic sites. Thresholds for identifying robust SNP loci can be modified in the configuration file (for more details see Input section). We can also add an annotation track to reconCNV. In this case we are adding exon level annotation for all RefSeq transcripts of COSMIC genes. Note: Annotations appear once log2(FC) genome plots are zoomed in to a gene under investigation.