Cnv-Ldc: An Optimized Cnv Detection Method For Low Depth Of Coverage Data

Recent improvements in technologies showed much greater variance of our genome than we thought. A part of this variance is due to submicroscopic chromosomal deletions/duplications called Copy Number Variations (CNVs). For some of these CNVs, it was clearly demonstrated that they play an important role in disease susceptibility, including complex diseases and Mendelian diseases. Last advances in next-generation sequencing have made fast progress in analyzing data for CNVs, in so far as they promise to improve the sensitivity in detection. This has led to the development of several new bioinformatics approaches and algorithms for detecting CNVs from this data for the four common methods: Assembly Based, Split Read, Read-Paired mapping, and Read Depth. Here we focus on the RD method that is able to detect the exact number of CNVs in comparison with the other methods. We propose an alternative method for detecting CNVs from short sequencing reads, CNV-LDC (Copy Number Variation-Low Depth of Coverage), that complements the existing method named CNV-TV (Copy Number Variation-Total Variation). We optimize the signal modeling and threshold step to lift the performance in low depth of coverage. Results of this new approach have been compared to various recent methods on different simulated data using small and large CNVs.