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      A map of copy number variations in the Tunisian population: a valuable tool for medical genomics in North Africa

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          Abstract

          Copy number variation (CNV) is considered as the most frequent type of structural variation in the human genome. Some CNVs can act on human phenotype diversity, encompassing rare Mendelian diseases and genomic disorders. The North African populations remain underrepresented in public genetic databases in terms of single-nucleotide variants as well as for larger genomic mutations. In this study, we present the first CNV map for a North African population using the Affymetrix Genome-Wide SNP (single-nucleotide polymorphism) array 6.0 array genotyping intensity data to call CNVs in 102 Tunisian healthy individuals. Two softwares, PennCNV and Birdsuite, were used to call CNVs in order to provide reliable data. Subsequent bioinformatic analyses were performed to explore their features and patterns. The CNV map of the Tunisian population includes 1083 CNVs spanning 61.443 Mb of the genome. The CNV length ranged from 1.017 kb to 2.074 Mb with an average of 56.734 kb. Deletions represent 57.43% of the identified CNVs, while duplications and the mixed loci are less represented. One hundred and three genes disrupted by CNVs are reported to cause 155 Mendelian diseases/phenotypes. Drug response genes were also reported to be affected by CNVs. Data on genes overlapped by deletions and duplications segments and the sequence properties in and around them also provided insights into the functional and health impacts of CNVs. These findings represent valuable clues to genetic diversity and personalized medicine in the Tunisian population as well as in the ethnically similar populations from North Africa.

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          BEDTools: a flexible suite of utilities for comparing genomic features

          Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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                Author and article information

                Contributors
                lilia.romdhane@fsb.rnu.tn
                Journal
                NPJ Genom Med
                NPJ Genom Med
                NPJ Genomic Medicine
                Nature Publishing Group UK (London )
                2056-7944
                8 January 2021
                8 January 2021
                2021
                : 6
                : 3
                Affiliations
                [1 ]GRID grid.418517.e, ISNI 0000 0001 2298 7385, Biomedical Genomics and Oncogenetics Laboratory (LR16IPT05), , Institut Pasteur de Tunis, ; Tunis, Tunisia
                [2 ]GRID grid.419508.1, ISNI 0000 0001 2295 3249, Department of Biology, , Faculty of Science of Bizerte, ; Jarzouna, Tunisia
                [3 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Genetic Medicine, , Weill Cornell Medicine, ; New York, NY USA
                [4 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Microbiology and Immunology, , Weill Cornell Medicine, ; New York, NY USA
                [5 ]GRID grid.418818.c, ISNI 0000 0001 0516 2170, Genetic Intelligence Laboratory, Weill Cornell Medicine in Qatar, Education City, , Qatar Foundation, ; Doha, Qatar
                [6 ]GRID grid.416973.e, ISNI 0000 0004 0582 4340, Department of Genetic Medicine, , Weill Cornell Medical College in Qatar, ; Doha, Qatar
                [7 ]Department of Human Genetics, Sidra Medicine, Doha, Qatar
                Author information
                http://orcid.org/0000-0001-5310-7272
                Article
                166
                10.1038/s41525-020-00166-5
                7794582
                33420067
                cced0226-8a0c-4731-9438-918e25495503
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 March 2020
                : 18 November 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100008982, Qatar National Research Fund (QNRF);
                Award ID: NPRP 08-083-3-031
                Award ID: PPM1-1229-150022
                Award ID: PPM1-1229-150022
                Award Recipient :
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                © The Author(s) 2021

                molecular medicine,medical genomics
                molecular medicine, medical genomics

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