CNV Workshop: an integrated platform for high-throughput copy number variation discovery and clinical diagnostics

Abstract

Background: Recent studies have shown that copy number variations (CNVs) are frequent in higher eukaryotes and associated with a substantial portion of inherited and acquired risk for various human diseases. The increasing availability of high-resolution genome surveillance platforms provides opportunity for rapidly assessing research and clinical samples for CNV content, as well as for determining the potential pathogenicity of identified variants. However, few informatics tools for accurate and efficient CNV detection and assessment currently exist.

Results: We developed a suite of software tools and resources (CNV Workshop) for automated, genome-wide CNV detection from a variety of SNP array platforms. CNV Workshop includes three major components: detection, annotation, and presentation of structural variants from genome array data. CNV detection utilizes a robust and genotype-specific extension of the Circular Binary Segmentation algorithm, and the use of additional detection algorithms is supported. Predicted CNVs are captured in a MySQL database that supports cohort-based projects and incorporates a secure user authentication layer and user/admin roles. To assist with determination of pathogenicity, detected CNVs are also annotated automatically for gene content, known disease loci, and gene-based literature references. Results are easily queried, sorted, filtered, and visualized via a web-based presentation layer that includes a GBrowse-based graphical representation of CNV content and relevant public data, integration with the UCSC Genome Browser, and tabular displays of genomic attributes for each CNV.

Conclusions: To our knowledge, CNV Workshop represents the first cohesive and convenient platform for detection, annotation, and assessment of the biological and clinical significance of structural variants. CNV Workshop has been successfully utilized for assessment of genomic variation in healthy individuals and disease cohorts and is an ideal platform for coordinating multiple associated projects.

Availability and implementation: Available on the web at: http://sourceforge.net/projects/cnv.

Figure 1

Comparison of CNV Workshop and PennCNV variant prediction sets. Depicted is a composite graph showing the fraction of CNVs predicted solely by PennCNV, CNV Workshop, and both algorithms. Each column indicates the fraction of predicted CNVs for a certain size range, and for all CNVs combined (leftmost column).

Figure 2

CNV Workshop query interface. Screenshot of the simple query interface in "annotated" mode for the CHOP CNV map database. Annotated mode enables a user to query by sample. Positional queries supported are chromosome(s), cytogenetic band(s), sequence position or range(s), and gene name(s). Most non-standard gene names are recognized and normalized to HGNC gene symbols. For the CHOP CNV map, additional searchable fields are CNV type (CNV, CNV region, or CNV block), ethnicity, and uniqueness (unique or non-unique/observed in multiple unrelated individuals).

Figure 3

Screenshot of the advanced query interface in "raw" mode for a database of autism samples. Raw mode enables a user to query by segment mean, minor (AB) allele frequency, and to filter results based upon allelic ratio statistics.

Figure 4

Presentation of query results in CNV Workshop. Depicted are results for a chromosome 16 query of sequence position range 20,300,000-20,610,000 in an autism cohort. Top panel: graphical display. Layers (top to bottom) represent the sequence position (top), cytogenetic bands, CNVs observed in the autism cohort, CNVs in the CHOP CNV map, CNVs in the Database of Genomic Variants (labels indicate the study), phenotypes of Genetic Association Database studies, and UCSC Known Genes. All glyphs hyperlink to corresponding database records. Bottom panel: tabular display for a subset of CNVs. Sortable column headers are colored red or (for current sort order) green. Each row value colored red denotes a hyperlink to a corresponding external database record. Checkboxes at far left allow a user to save certain CNVs in the MyCNV clipboard.