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Dichapetalum cymosum produces the toxic fluorinated metabolite, fluoroacetate, presumably as a defence mechanism. Given the rarity of fluorinated metabolites in nature, the biosynthetic origin and function of fluoroacetate have been of particular interest. However, the mechanism for fluorination in D. cymosum was never elucidated. More importantly, there is a severe lack in knowledge on a genetic level for fluorometabolite-producing plants, impeding research on the subject. Here, we report on the first transcriptome for D. cymosum and investigate the wound response for insights into fluorometabolite production. Mechanical wounding studies were performed and libraries of the unwounded (control) and wounded (30 and 60 min post wounding) plant were sequenced using the Illumina HiSeq platform. A combined reference assembly generated 77,845 transcripts. Using the SwissProt, TrEMBL, GO, eggNOG, KEGG, Pfam, EC and PlantTFDB databases, a 69% annotation rate was achieved. Differential expression analysis revealed the regulation of 364 genes in response to wounding. The wound responses in D. cymosum included key mechanisms relating to signalling cascades, phytohormone regulation, transcription factors and defence-related secondary metabolites. However, the role of fluoroacetate in inducible wound responses remains unclear. Bacterial fluorinases were searched against the D. cymosum transcriptome but transcripts with homology were not detected suggesting the presence of a potentially different fluorinating enzyme in plants. Nevertheless, the transcriptome produced in this study significantly increases genetic resources available for D. cymosum and will assist with future research into fluorometabolite-producing plants.

Competency frameworks have proved to be a powerful tool for curriculum development and assessment across many subject domains, and the field of computational biology is no exception. Efforts from the ISCB to develop and successively refine a set of competencies for bioinformatics education and various associated mapping tools have provided a framework for bringing competency-based design principles broadly to education and training of a wide range of professionals in need of some level of mastery of the principles and practice of computational biology. This document seeks to provide some basic guidance for education and training professionals in the field in how to use this framework effectively. It includes a basic background on competency-based education and the history of the ISCB competency framework specifically, leading up to the Version 3 framework considered here. It then follows with some basic principles of applying competency-based education and an illustration of how they apply to different tasks in curriculum development. Appendices and various linked documents provide further elaboration and helpful guidance on the ISCB competencies specifically and some ways in which versions of them have been used already to develop diverse forms of bioinformatics education and training experience. Our target readerships are trainers and educators working in computational biology or more broadly in the molecular life sciences, medicine, and other disciplines that use biomolecular data, including those working in academia, industry and the public sector., These guidelines are available as a google doc at https://docs.google.com/document/d/1hgBEq-C8HvmxxQ2grYiqNXt-agM1LWzW673j6uQHHmI/edit#heading=h.1x0y3vxuz6a7

South Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny (separation of Nguni, Sotho-Tswana and Tsonga speakers). Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ~400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa., Despite linguistic and geographic diversity in South Eastern Bantu-speaking (SEB) groups of South Africa, genetic variation in these groups has not been investigated in depth. Here, the authors analyse genome-wide data from 5056 individuals, providing insights into demographic history across SEB groups.

South Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny (separation of Nguni, Sotho-Tswana and Tsonga speakers). Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ∼400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa.

Pancreatic cancer accounts for 2.8% of new cancer cases worldwide and is projected to become the second leading cause of cancer-related deaths by 2030. Patients of African ancestry appear to be at an increased risk for pancreatic ductal adenocarcinoma (PDAC), with more severe disease and outcomes. The purpose of this study was to map the proteomic and genomic landscape of a cohort of PDAC patients of African ancestry. Thirty tissues (15 tumours and 15 normal adjacent tissues) were obtained from consenting South African PDAC patients. Optimisation of the sample preparation method allowed for the simultaneous extraction of high-purity protein and DNA for SWATH-MS and OncoArray SNV analyses. We quantified 3402 proteins with 49 upregulated and 35 downregulated proteins at a minimum 2.1 fold change and FDR adjusted p-value (q-value) ≤ 0.01 when comparing tumour to normal adjacent tissue. Many of the upregulated proteins in the tumour samples are involved in extracellular matrix formation (ECM) and related intracellular pathways. In addition, proteins such as EMIL1, KBTB2, and ZCCHV involved in the regulation of ECM proteins were observed to be dysregulated in pancreatic tumours. Downregulation of pathways involved in oxygen and carbon dioxide transport were observed. Genotype data showed missense mutations in some upregulated proteins, such as MYPN, ESTY2 and SERPINB8. Approximately 11% of the dysregulated proteins, including ISLR, BP1, PTK7 and OLFL3, were predicted to be secretory proteins. These findings help in further elucidating the biology of PDAC and may aid in identifying future plausible markers for the disease.

The Southern African Human Genome Programme is a national initiative that aspires to unlock the unique genetic character of southern African populations for a better understanding of human genetic diversity. In this pilot study the Southern African Human Genome Programme characterizes the genomes of 24 individuals (8 Coloured and 16 black southeastern Bantu-speakers) using deep whole-genome sequencing. A total of ~16 million unique variants are identified. Despite the shallow time depth since divergence between the two main southeastern Bantu-speaking groups (Nguni and Sotho-Tswana), principal component analysis and structure analysis reveal significant (p, African populations show a high level of genetic diversity and extensive regional admixture. Here, the authors sequence the whole genomes of 24 South African individuals of different ethnolinguistic origin and find substantive genomic divergence between two southeastern Bantu-speaking groups.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03286-9.

Genetic variation and susceptibility to disease are shaped by human demographic history and adaptation. We can now study the genomes of extant Africans and uncover traces of population migration, admixture, assimilation and selection by applying sophisticated computational algorithms. There are four major ethnolinguistic divisions among present day Africans: Hunter-gatherer populations in southern and central Africa; Nilo-Saharan speakers from north and northeast Africa; Afro-Asiatic speakers from north and east Africa; and Niger-Congo speakers who are the predominant ethnolinguistic group spread across most of sub-Saharan Africa. The enormous ethnolinguistic diversity in sub-Saharan African populations is largely paralleled by extensive genetic diversity and until a decade ago, little was known about detailed origins and divergence of these groups. Results from large-scale population genetic studies, and more recently whole genome sequence data, are unravelling the critical role of events like migration and admixture and environmental factors including diet, infectious diseases and climatic conditions in shaping current population diversity. It is now possible to start providing quantitative estimates of divergence times, population size and dynamic processes that have affected populations and their genetic risk for disease. Finally, the availability of ancient genomes from Africa provides historical insights of unprecedented depth. In this review, we highlight some key interpretations that have emerged from recent African genome studies.

This publication hasn't any creative commons license associated. This deposit is composed by the main article plus the supplementary materials of the publication. This deposit is composed by a publication in which the IGC's authors have had the role of collaboration (it's a collaboration publication). This type of deposit in ARCA is in restrictedAccess (it can't be in open access to the public), and can only be accessed by two ways: either by requesting a legal copy from the author (the email contact present in this deposit) or by visiting the following link: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005715#sec022 Africa is not unique in its need for basic bioinformatics training for individuals from a diverse range of academic backgrounds. However, particular logistical challenges in Africa, most notably access to bioinformatics expertise and internet stability, must be addressed in order to meet this need on the continent. H3ABioNet (www.h3abionet.org), the Pan African Bioinformatics Network for H3Africa, has therefore developed an innovative, free-of-charge "Introduction to Bioinformatics" course, taking these challenges into account as part of its educational efforts to provide on-site training and develop local expertise inside its network. A multiple-delivery-mode learning model was selected for this 3-month course in order to increase access to (mostly) African, expert bioinformatics trainers. The content of the course was developed to include a range of fundamental bioinformatics topics at the introductory level. For the first iteration of the course (2016), classrooms with a total of 364 enrolled participants were hosted at 20 institutions across 10 African countries. To ensure that classroom success did not depend on stable internet, trainers pre-recorded their lectures, and classrooms downloaded and watched these locally during biweekly contact sessions. The trainers were available via video conferencing to take questions during contact sessions, as well as via online "question and discussion" forums outside of contact session time. This learning model, developed for a resource-limited setting, could easily be adapted to other settings. National Human Genome Research Institute; Office of the Director; National Institutes of Health grant: (U41HG006941). info:eu-repo/semantics/publishedVersion

The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exercises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a framework for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exercise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so., Author summary Many programs have been developed to boost the technical and computational skills of scientists working in low to medium income countries (LMIC), who often struggle to remain competitive with their peers in more developed parts of the world. Typically, these programs rely on intensive workshops where students acquire and exercise these skills under the supervision of experienced trainers. However, when trainees return to their home institutions, even after extensive exposure to state of the art techniques, they often find it difficult to put the skills they have acquired into practice and to establish themselves as fully independent practitioners. We have attempted to build a framework through which teams of scientists in African research groups can demonstrate that they have acquired the necessary skills to analyze different types of genomic datasets. Three teams of scientists who have successfully submitted to this assessment exercise report their positive experiences. Many potential participants have so far declined the opportunity, and we discuss the reasons for their reluctance as well as possible ways to facilitate their engagement and provide them with incentives. We argue that assessments such as this could be part of any program aiming to develop technical skills in scientists wishing to support genomic research programs.

The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous computing environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. Building on the network’s Standard Operating Procedures (SOPs) for common genomic analyses, H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Hackathon in 2016, with the purpose of translating those SOPs into analysis pipelines able to run on heterogeneous computing environments and meeting the needs of H3Africa research projects. This paper describes the preparations for this hackathon and reflects upon the lessons learned about its impact on building the technical and scientific expertise of African researchers. The workflows developed were made publicly available in GitHub repositories and deposited as container images on Quay.io.

International audience; The application of genomics technologies to medicine and biomedical research is increasing in popularity, made possible by new high-throughput genotyping and sequencing technologies and improved data analysis capabilities. Some of the greatest genetic diversity among humans, animals, plants, and microbiota occurs in Africa, yet genomic research outputs from the continent are limited. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive the development of genomic research for human health in Africa, and through recognition of the critical role of bioinformatics in this process, spurred the establishment of H3ABioNet, a pan-African bioinformatics network for H3Africa. The limitations in bioinformatics capacity on the continent have been a major contributory factor to the lack of notable outputs in high-throughput biology research. Although pockets of high-quality bioinformatics teams have existed previously, the majority of research institutions lack experienced faculty who can train and supervise bioinformatics students. H3ABioNet aims to address this dire need, specifically in the area of human genetics and genomics, but knock-on effects are ensuring this extends to other areas of bioinformatics. Here, we describe the emergence of genomics research and the development of bioinformatics in Africa through H3ABioNet.

Background Pseudoxanthoma elasticum (PXE) is an autosomal recessive metabolic disorder with ectopic mineralization in the skin, eyes and cardiovascular system. PXE is caused by mutations in ABCC6 . Objective To examine 54 unrelated South African PXE patients for ABCC6 PXE causing mutations. Methods Patients were screened for mutations in ABCC6 using two strategies. The first involved a comprehensive screening of all the ABCC6 exons and flanking regions by dHPLC or sequencing whereas the second involved screening patients only for the common PXE mutations. The ABCC6 gene was screened in ten white and ten black healthy unrelated South Africans in order to examine the level of common non-PXE associated variation. Results The Afrikaner founder mutation, R1339C, was present in 0.41 of white ABCC6 PXE alleles, confirming the founder effect and its presence in both Afrikaans- (34/63 PXE alleles) and English-speakers (4/28). Eleven mutations were detected in the white patients (of European origin), including two nonsense mutations, 6 missense mutations, two frameshift mutations and a large deletion mutation. The five "Coloured" patients (of mixed Khoisan, Malay, European and African origin) included three compound heterozygotes with R1339C as one of the mutations. The three black patients (sub-Saharan African origin) were all apparent homozygotes for the R1314W mutation. Blacks showed a trend towards a higher degree of neurtral variation (18 variants) when compared to whites (12 variants). Conclusion Delineation of the ABCC6 mutation profile in South African PXE patients will be used as a guide for molecular genetic testing in a clinical setting and for genetic counselling.

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.HighlightsH3ABioNet is building capacity to enable analysis of genomic data in Africa.Infrastructure has been built for clinical and genomic data storage, management, and analysis.New algorithms and pipelines for African genomic data analysis have been developed.Data are being harmonized using ontologies to enable easy search and retrieval.Genomics training is implemented using various online and face-to-face approaches.

The diverse, rapidly developing discipline of bioinformatics has become an integral component of all areas of biomedical research in recent years. The ability to manage, analyse and interpret large complex datasets hinges on having access to skilled researchers who have received adequate training in specialised technical areas. Currently in Africa, individuals with these specialised skills are sparsely scattered across the continent and there is an urgent need to develop sustainable training programs to improve bioinformatics capacity. As part of the Human Heredity and Health in Africa (H3Africa) initiative, a pan-African bioinformatics network for H3Africa (H3ABioNet) was established to develop bioinformatics infrastructure together with a sustainable high quality training program. Here, we discuss the various training events and approaches developed by H3ABioNet to address the lack of bioinformatics capacity in Africa, together with the valuable lessons learnt.

Background Population differentiation is the result of demographic and evolutionary forces. Whole genome datasets from the 1000 Genomes Project (October 2012) provide an unbiased view of genetic variation across populations from Europe, Asia, Africa and the Americas. Common population-specific SNPs (MAF > 0.05) reflect a deep history and may have important consequences for health and wellbeing. Their interpretation is contextualised by currently available genome data. Results The identification of common population-specific (CPS) variants (SNPs and SSV) is influenced by admixture and the sample size under investigation. Nine of the populations in the 1000 Genomes Project (2 African, 2 Asian (including a merged Chinese group) and 5 European) revealed that the African populations (LWK and YRI), followed by the Japanese (JPT) have the highest number of CPS SNPs, in concordance with their histories and given the populations studied. Using two methods, sliding 50-SNP and 5-kb windows, the CPS SNPs showed distinct clustering across large genome segments and little overlap of clusters between populations. iHS enrichment score and the population branch statistic (PBS) analyses suggest that selective sweeps are unlikely to account for the clustering and population specificity. Of interest is the association of clusters close to recombination hotspots. Functional analysis of genes associated with the CPS SNPs revealed over-representation of genes in pathways associated with neuronal development, including axonal guidance signalling and CREB signalling in neurones. Conclusions Common population-specific SNPs are non-randomly distributed throughout the genome and are significantly associated with recombination hotspots. Since the variant alleles of most CPS SNPs are the derived allele, they likely arose in the specific population after a split from a common ancestor. Their proximity to genes involved in specific pathways, including neuronal development, suggests evolutionary plasticity of selected genomic regions. Contrary to expectation, selective sweeps did not play a large role in the persistence of population-specific variation. This suggests a stochastic process towards population-specific variation which reflects demographic histories and may have some interesting implications for health and susceptibility to disease.

Keratolytic winter erythema (KWE), also known as Oudtshoorn skin disease, is characterised by a cyclical disruption of normal epidermal keratinisation affecting primarily the palmoplantar skin with peeling of the palms and soles, which is worse in the winter. It is a rare monogenic, autosomal dominant condition of unknown cause. However, due to a founder effect, it occurs at a prevalence of 1/7 200 among South African Afrikaans-speakers. In the mid-1980s, samples were collected from affected families for a linkage study to pinpoint the location of the KWE gene. A genome-wide linkage analysis, using microsatellite markers, identified the KWE critical region on chromosome 8p23.1-p22. Subsequent genetic studies focused on screening candidate genes in this critical region; however, no pathogenic mutations that segregated exclusively with KWE were identified. The cathepsin B (CTSB) and farnesyl-diphosphate farnesyltransferase 1 (FDFT1) genes revealed no potentially pathogenic variants, nor did they show differential gene expression in affected skin. Mutation detection in additional candidate genes also failed to identify the KWE-associated variant, suggesting that the causal variant may be in an uncharacterised functional region. Bioinformatic analysis revealed highly conserved regions within the KWE critical region and a custom tiling array was designed to cover this region and to search for copy number variation. Although the study did not identify a variant that segregates exclusively with KWE, it provided valuable insight into the complex KWE-linked region. Next-generation sequencing approaches are being used to comb the region, but the causal variant for this interesting hyperkeratotic palmoplantar phenotype still remains elusive.

FOXP2 is a member of the P subfamily of FOX transcription factors, the DNA-binding domain of which is the winged helix forkhead domain (FHD). In this work we show that the FOXP2 FHD is able to bind to various DNA sequences, including a novel sequence identified in this work, with different affinities and rates as detected using surface plasmon resonance. Combining the experimental work with molecular docking, we show that high-affinity sequences remain bound to the protein for longer, form a greater number of interactions with the protein and induce a greater structural change in the protein than low-affinity sequences. We propose a binding model for the FOXP2 FHD that involves three types of binding sequence: low affinity sites which allow for rapid scanning of the genome by the protein in a partially unstructured state; moderate affinity sites which serve to locate the protein near target sites and high-affinity sites which secure the protein to the DNA and induce a conformational change necessary for functional binding and the possible initiation of downstream transcriptional events.