Title: CNV-LDC: an optimised method for copy number variation discovery in low depth of coverage data

Authors: Ayyoub Salmi; Sara El Jadid; Ismail Jamail; Taoufik Bensellak; Romain Philippe; Veronique Blanquet; Ahmed Moussa

Addresses: System and Data Engineering Team, Abdelmalek Essaadi University, Tangier, Morocco ' Laboratory of Telecommunication Systems and Engineering of the Decision, Ibn Tofail University, Kenitra, Morocco ' System and Data Engineering Team, Abdelmalek Essaadi University, Tangier, Morocco ' System and Data Engineering Team, Abdelmalek Essaadi University, Tangier, Morocco ' Animal Molecular Genetics Unit, Limoges University, Limoges, France ' Animal Molecular Genetics Unit, Limoges University, Limoges, France ' System and Data Engineering Team, Abdelmalek Essaadi University, Tangier, Morocco

Abstract: Recent advances in sequencing technologies led to an increasing number of highly accurate ways of identifying and studying copy number variations (CNVs). Many methods and software packages have been developed for the detection of CNVs, generally these methods are based on four approaches: Assembly Based, Split Read, Read-Paired mapping and Read Depth. In this paper, we introduce 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). To evaluate the performance of our method we compared it with some of the commonly used methods that are freely available and use different approaches to identify CNVs: Pindel, CNVnator and DELLY2. We used for this comparative study simulated data to gain control over deletions and duplications, then we used real data from the 1000 genome project to further test the quality of detected CNVs.

Keywords: copy number variation; NGS data; read depth; low depth of coverage.

DOI: 10.1504/IJDMB.2018.10017728

International Journal of Data Mining and Bioinformatics, 2018 Vol.21 No.2, pp.169 - 181

Received: 14 Oct 2017
Accepted: 02 Oct 2018

Published online: 27 Nov 2018 *

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