The workflow of X-CNV model training and testing
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Cite:Zhang L, Shi J, Ouyang J, Zhang R, Tao Y, Yuan D, Lv C, Wang R, Ning B, Roberts R, Tong W, Liu Z, Shi T. X-CNV: genome-wide prediction of the pathogenicity of copy number variations. Genome Med. 2021 Aug 18;13(1):132. doi: 10.1186/s13073-021-00945-4. PMID: 34407882.
Key features of X-CNV Analysis Platform
The X-CNV analysis interface allows users to predict the pathogenicity of copy number variations (CNVs) by single or batch query with GRCh37/hg19 Genome Reference. The analysis interface only requires the chromosome, start and end positions, and CNV type, and calculates four categories of features, including universal, coding region, noncoding, and genome-wide features, for pathogenicity prediction. The CNV pathogenicity is ultimately measured by a meta-voting prediction (MVP) score, which represents the probability that the CNV is pathogenic.
The analysis interface platform also provides the annotations such as genes located within the CNV region, diseases (eRAM) and phenotypes (MGI) associated with the genes located within the CNV region, some known CNVs curated in dbVAR or DGV database, and expression Quantitative Trait Loci (eQTLs) for the CNV of interest.
We also provide a batch query module and a local version of X-CNV analysis for users who have multiple CNVs to predict. The users using batch query module will receive a reminder e-mail when the work has been completed. The local version can satisfy the requirements of bioiformatic users.
The resources for X-CNV can be found at https://github.com/kbvstmd/XCNV
The maximal clique algorithm
The maximal clique algorithm is employed to unify the CNVs from different genomics technology platforms and variation calling pipelines.