Authors:
Ayyoub Salmi
1
;
2
;
Sara El Jadid
3
;
Ismail Jamail
1
;
Taoufik Bensellak
1
;
Romain Philippe
2
;
Veronique Blanquet
2
and
Ahmed Moussa
1
Affiliations:
1
Technology Laboratory of Information and Communication, Abdelmalek Essaadi University, Morocco
;
2
Limoges University, France
;
3
Laboratory of Telecommunication Systems and Engineering of the Decision, Ibn Tofail University, Morocco
Keyword(s):
Copy Number Variation, NGS Data, Read Depth, Low Depth of Coverage.
Abstract:
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.
(More)