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Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.

The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration.

The disruptive potential of mobile phones in catalyzing development is increasingly being recognized. However, numerous gaps remain in access to phones and their influence on health care utilization. In this cross-sectional study from India, we assess the gaps in women's access to phones, their influencing factors, and their influence on health care utilization. Data drawn from the 2015 National Family Health Survey (NFHS) in India included a national sample of 45,231 women with data on phone access. Survey design weighted estimates of household phone ownership and women's access among different population sub-groups are presented. Multilevel logistic models explored the association of phone access with a wide range of maternal and child health indicators. Blinder-Oaxaca (BO) decomposition is used to decompose the gaps between women with and without phone access in health care utilization into components explained by background characteristics influencing phone access (endowments) and unexplained components (coefficients), potentially attributable to phone access itself. Phone ownership at the household level was 92·8% (95% CI: 92·6-93·0%), with rural ownership at 91·1% (90·8-91·4%) and urban at 97.1% (96·7-97·3%). Women's access to phones was 47·8% (46·7-48·8%); 41·6% in rural areas (40·5-42·6%) and 62·7% (60·4-64·8%) in urban. Phone access in urban areas was positively associated with skilled birth attendance, postnatal care and use of modern contraceptives and negatively associated with early antenatal care. Phone access was not associated with improvements in utilization indicators in rural settings. Phone access (coefficient components) explained large gaps in the use of modern contraceptives, moderate gaps in postnatal care and early antenatal care, and smaller differences in the use of skilled birth attendance and immunization. For full antenatal car, phone access was associated with reducing gaps in utilization. Women of reproductive age have significantly lower phone access use than the households they belong to and marginalized women have the least phone access. Existing phone access for rural women did not improve their health care utilization but was associated with greater utilization for urban women. Without addressing these biases, digital health programs may be at risk of worsening existing health inequities.

Background The role of the human microbiome in health and disease is an emerging and important area of research; however, there is a concern that African populations are under-represented in human microbiome studies. We, therefore, conducted a systematic survey of African human microbiome studies to provide an overview and identify research gaps. Our secondary objectives were: (i) to determine the number of peer-reviewed publications; (ii) to identify the extent to which the researches focused on diseases identified by the World Health Organization [WHO] State of Health in the African Region Report as being the leading causes of morbidity and mortality in 2018; (iii) to describe the extent and pattern of collaborations between researchers in Africa and the rest of the world; and (iv) to identify leadership and funders of the studies. Methodology We systematically searched Medline via PubMed, Scopus, CINAHL, Academic Search Premier, Africa-Wide Information through EBSCOhost, and Web of Science from inception through to 1st April 2020. We included studies that characterized samples from African populations using next-generation sequencing approaches. Two reviewers independently conducted the literature search, title and abstract, and full-text screening, as well as data extraction. Results We included 168 studies out of 5515 records retrieved. Most studies were published in PLoS One (13%; 22/168), and samples were collected from 33 of the 54 African countries. The country where most studies were conducted was South Africa (27/168), followed by Kenya (23/168) and Uganda (18/168). 26.8% (45/168) focused on diseases of significant public health concern in Africa. Collaboration between scientists from the United States of America and Africa was most common (96/168). The first and/or last authors of 79.8% of studies were not affiliated with institutions in Africa. Major funders were the United States of America National Institutes of Health (45.2%; 76/168), Bill and Melinda Gates Foundation (17.8%; 30/168), and the European Union (11.9%; 20/168). Conclusions There are significant gaps in microbiome research in Africa, especially those focusing on diseases of public health importance. There is a need for local leadership, capacity building, intra-continental collaboration, and national government investment in microbiome research within Africa. -p-9t9CgT8xQHC5cjmG_wi Video Abstract

The Human Leukocyte Antigen (HLA) region plays an important role in autoimmune and infectious diseases. HLA is a highly polymorphic region and thus difficult to impute. We, therefore, sought to evaluate HLA imputation accuracy, specifically in a West African population, since they are understudied and are known to harbor high genetic diversity. The study sets were selected from 315 Gambian individuals within the Gambian Genome Variation Project (GGVP) Whole Genome Sequence datasets. Two different arrays, Illumina Omni 2.5 and Human Hereditary and Health in Africa (H3Africa), were assessed for the appropriateness of their markers, and these were used to test several imputation panels and tools. The reference panels were chosen from the 1000 Genomes (1kg-All), 1000 Genomes African (1kg-Afr), 1000 Genomes Gambian (1kg-Gwd), H3Africa, and the HLA Multi-ethnic datasets. HLA-A, HLA-B, and HLA-C alleles were imputed using HIBAG, SNP2HLA, CookHLA, and Minimac4, and concordance rate was used as an assessment metric. The best performing tool was found to be HIBAG, with a concordance rate of 0.84, while the best performing reference panel was the H3Africa panel, with a concordance rate of 0.62. Minimac4 (0.75) was shown to increase HLA-B allele imputation accuracy compared to HIBAG (0.71), SNP2HLA (0.51) and CookHLA (0.17). The H3Africa and Illumina Omni 2.5 array performances were comparable, showing that genotyping arrays have less influence on HLA imputation in West African populations. The findings show that using a larger population-specific reference panel and the HIBAG tool improves the accuracy of HLA imputation in a West African population.

Background As sequencing technology improves, the concept of a single reference genome is becoming increasingly restricting. In the case of Mycobacterium tuberculosis , one must often choose between using a genome that is closely related to the isolate, or one that is annotated in detail. One promising solution to this problem is through the graph based representation of collections of genomes as a single genome graph. Though there are currently a handful of tools that can create genome graphs and have demonstrated the advantages of this new paradigm, there still exists a need for flexible tools that can be used by researchers to overcome challenges in genomics studies. Results We present GenGraph, a Python toolkit and accompanying modules that use existing multiple sequence alignment tools to create genome graphs. Python is one of the most popular coding languages for the biological sciences, and by providing these tools, GenGraph makes it easier to experiment and develop new tools that utilise genome graphs. The conceptual model used is highly intuitive, and as much as possible the graph structure represents the biological relationship between the genomes. This design means that users will quickly be able to start creating genome graphs and using them in their own projects. We outline the methods used in the generation of the graphs, and give some examples of how the created graphs may be used. GenGraph utilises existing file formats and methods in the generation of these graphs, allowing graphs to be visualised and imported with widely used applications, including Cytoscape, R, and Java Script. Conclusions GenGraph provides a set of tools for generating graph based representations of sets of sequences with a simple conceptual model, written in the widely used coding language Python, and publicly available on Github.

The mitogen-activated protein kinase (MAPK) pathways are crucial regulators of the cellular processes that fuel the malignant transformation of normal cells. The molecular aberrations which lead to cancer involve mutations in, and transcription variations of, various MAPK pathway genes. Here, we examine the genome sequences of 40,848 patient-derived tumours representing 101 distinct human cancers to identify cancer-associated mutations in MAPK signalling pathway genes. We show that patients with tumours that have mutations within genes of the ERK-1/2 pathway, the p38 pathways, or multiple MAPK pathway modules, tend to have worse disease outcomes than patients with tumours that have no mutations within the MAPK pathways genes. Furthermore, by integrating information extracted from various large-scale molecular datasets, we expose the relationship between the fitness of cancer cells after CRISPR mediated gene knockout of MAPK pathway genes, and their dose-responses to MAPK pathway inhibitors. Besides providing new insights into MAPK pathways, we unearth vulnerabilities in specific pathway genes that are reflected in the re sponses of cancer cells to MAPK targeting drugs: a revelation with great potential for guiding the development of innovative therapies.Musalula Sinkala et al. report that collectively, specific mutations of genes in 5 MAPK pathways are associated with worse patient survival. They identify gene components that are pharmacologically vulnerable to pathway inhibitors in cancer cell models, suggesting potential clinical applications of these findings.

Bioinformatics is recognized as part of the essential knowledge base of numerous career paths in biomedical research and healthcare. However, there is little agreement in the field over what that knowledge entails or how best to provide it. These disagreements are compounded by the wide range of populations in need of bioinformatics training, with divergent prior backgrounds and intended application areas. The Curriculum Task Force of the International Society of Computational Biology (ISCB) Education Committee has sought to provide a framework for training needs and curricula in terms of a set of bioinformatics core competencies that cut across many user personas and training programs. The initial competencies developed based on surveys of employers and training programs have since been refined through a multiyear process of community engagement. This report describes the current status of the competencies and presents a series of use cases illustrating how they are being applied in diverse training contexts. These use cases are intended to demonstrate how others can make use of the competencies and engage in the process of their continuing refinement and application. The report concludes with a consideration of remaining challenges and future plans.

With more microbiome studies being conducted by African-based research groups, there is an increasing demand for knowledge and skills in the design and analysis of microbiome studies and data. However, high-quality bioinformatics courses are often impeded by differences in computational environments, complicated software stacks, numerous dependencies, and versions of bioinformatics tools along with a lack of local computational infrastructure and expertise. To address this, H3ABioNet developed a 16S rRNA Microbiome Intermediate Bioinformatics Training course, extending its remote classroom model. The course was developed alongside experienced microbiome researchers, bioinformaticians, and systems administrators, who identified key topics to address. Development of containerised workflows has previously been undertaken by H3ABioNet, and Singularity containers were used here to enable the deployment of a standard replicable software stack across different hosting sites. The pilot ran successfully in 2019 across 23 sites registered in 11 African countries, with more than 200 participants formally enrolled and 106 volunteer staff for onsite support.

The outcome of infection by Mycobacterium tuberculosis (Mtb) depends greatly on how the host responds to the bacteria and how the bacteria manipulates the host, which is facilitated by protein-protein interactions. Thus, to understand this process, there is a need for elucidating protein interactions between human and Mtb, which may enable us to characterize specific molecular mechanisms allowing the bacteria to persist and survive under different environmental conditions. In this work, we used the interologs method based on experimentally verified intra-species and inter-species interactions to predict human-Mtb functional interactions. These interactions were further filtered using known human-Mtb interactions and genes that are differentially expressed during infection, producing 190 interactions. Further analysis of the subcellular location of proteins involved in these human-Mtb interactions confirms feasibility of these interactions. We also conducted functional analysis of human and Mtb proteins involved in these interactions, checking whether these proteins play a role in infection and/or disease, and enriching Mtb proteins in a previously predicted list of drug targets. We found that the biological processes of the human interacting proteins suggested their involvement in apoptosis and production of nitric oxide, whereas those of the Mtb interacting proteins were relevant to the intracellular environment of Mtb in the host. Mapping these proteins onto KEGG pathways highlighted proteins belonging to the tuberculosis pathway and also suggested that Mtb proteins might use the host to acquire nutrients, which is in agreement with the intracellular lifestyle of Mtb. This indicates that these interactions can shed light on the interplay between Mtb and its human host and thus, contribute to the process of designing novel drugs with new biological mechanisms of action.