Whole Exome Sequencing allows the identification of two novel groups of Xeroderma pigmentosum in Tunisia, XP-D and XP-E: Impact on molecular diagnosis.

BACKGROUND: Skin cancers (SC) are complex diseases that develop from complex combinations of genetic and environmental risk factors. One of the most severe and rare genetic diseases predisposing to SC is the Xeroderma pigmentosum (XP) syndrome. OBJECTIVES: First, to identify the genetic etiology of XP and to better classify affected patients. Second, to provide early molecular diagnosis for pre-symptomatic patient and finally to offer genetic counseling for related individuals. METHODS: Whole Exome Sequencing (WES) and Run Of Homozygosity (ROH) were performed for two patients belonging to two different multiplex consanguineous families. The identified mutations were confirmed by Sanger sequencing and researched in ten Tunisian families including a total of 25 affected individuals previously suspected as having XP group V (XP-V) form. All patients had mild dermatological manifestations, absence of neurological abnormalities and late onset of skin tumors. RESULTS: Screening for functional variations showed the presence of the ERCC2 p.Arg683Gln in XP14KA-2 patient and a novel mutation, DDB2 p. (Lys381Argfs*2), in XP51-MAH-1 patient. Sanger sequencing and familial segregation showed that the ERCC2 mutation is present at a homozygous state in 10 affected patients belonging to 3 families. The second mutation in DDB2, is present at a homozygous state in 5 affected cases belonging to the same family. These two mutations are absent in the remaining 10 affected patients. The ERCC2 c.2048G > A mutation is present in a medium ROH region (class B) suggesting that it mostly arises from ancient relatedness within individuals. However, the c.1138delG DDB2 mutation is present in a large ROH region (class C) suggesting that it arises from recent relatedness. CONCLUSION: To our knowledge, this is the first study that identifies XP-D and XP-E complementation groups in Tunisia. These two groups are very rare and under-diagnosed in the world and were not reported in North Africa.

Development of Bioinformatics Infrastructure for Genomics Research.

BACKGROUND: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community. OBJECTIVES: H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis. METHODS AND RESULTS: Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. CONCLUSIONS: For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa.