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Authors: Ayyoub Salmi 1 ; Sara El Jadid 2 ; Ismail Jamail 3 ; Taoufik Bensellak 3 ; Romain Philippe 4 ; Veronique Blanquet 4 and Ahmed Moussa 3

Affiliations: 1 Limoges University, Technology Laboratory of Information and Communication and Abdelmalek Essaadi University, France ; 2 Laboratory of Telecommunication Systems and Engineering of the Decision and Ibn Tofail University, Morocco ; 3 Technology Laboratory of Information and Communication and Abdelmalek Essaadi University, Morocco ; 4 Limoges University, France

ISBN: 978-989-758-214-1

ISSN: 2184-4305

Keyword(s): Copy Number Variation, NGS Data, Read Depth, Low Depth of Coverage.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Genomics and Proteomics ; Next Generation Sequencing ; Sequence Analysis ; Structural Variations

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)

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Paper citation in several formats:
Salmi, A.; El Jadid, S.; Jamail, I.; Bensellak, T.; Philippe, R.; Blanquet, V. and Moussa, A. (2017). CNV-LDC: An Optimized CNV Detection Method for Low Depth of Coverage Data. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1 ISSN 2184-4305, pages 37-42. DOI: 10.5220/0006111600370042

@conference{bioinformatics17,
author={Ayyoub Salmi. and Sara {El Jadid}. and Ismail Jamail. and Taoufik Bensellak. and Romain Philippe. and Veronique Blanquet. and Ahmed Moussa.},
title={CNV-LDC: An Optimized CNV Detection Method for Low Depth of Coverage Data},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={37-42},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006111600370042},
isbn={978-989-758-214-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - CNV-LDC: An Optimized CNV Detection Method for Low Depth of Coverage Data
SN - 978-989-758-214-1
IS - 2184-4305
AU - Salmi, A.
AU - El Jadid, S.
AU - Jamail, I.
AU - Bensellak, T.
AU - Philippe, R.
AU - Blanquet, V.
AU - Moussa, A.
PY - 2017
SP - 37
EP - 42
DO - 10.5220/0006111600370042

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