Bioinformatics is an applied interdisciplinary field whose primary purpose is to develop and deploy computational techniques to store, organize, and aid in the analysis and interpretation of large-scale data obtained from biological systems.While rooted in the analysis of nucleotide and protein sequences, it now encompasses techniques targeting multiple data acquisition modalities and seeks to comprehend the functioning of biological systems at many different levels.Bioinformaticians need to be cognizant of diverse scientific fields: basic and molecular biology, genetics, mathematics, statistics, and computer science at a minimum, thus requiring a thoroughly interdisciplinary set of skills to successfully carry out their duties.Due to the growing importance of bioinformatics in enabling modern biomedical research, programs and core facilities have been established in most academic institutions in the developed world over the last 30 years.At present, there are relatively few research and higher education institutions in low-and middle-income countries (LMICs) that have incorporated bioinformatics into their academic curricula or are hosting bioinformatics research or service groups [1,2].There are many reasons for this, including the relative lack of research projects requiring computational analysis, lack of local expertise, lack of required infrastructure, lack of appropriate internal financial support, and the resistance of many academic institutions to the incorporation of new fields of teaching and research [3].However, there is an increasing recognition in some African countries, and other LMICs, that bioinformatics needs to be developed as an independent discipline, both in the academic curricula and in research portfolios [4,5].H3ABioNet is a Pan-African network of bioinformaticians, funded by the United States National Institutes of Health (NIH), that aims to provide technical and scientific support to the Human Heredity and Health in Africa (H3Africa) genomics research program [6].As part of its mission, H3ABioNet has developed training and capacity building projects ranging from introductory courses to advanced workshops on bioinformatics and related topics, and from

BACKGROUND: The Pan-African bioinformatics network, H3ABioNet, comprises 27 research institutions in 17 African countries. H3ABioNet is part of the Human Health and Heredity in Africa program (H3Africa), an African-led research consortium funded by the US National Institutes of Health and the UK Wellcome Trust, aimed at using genomics to study and improve the health of Africans. A key role of H3ABioNet is to support H3Africa projects by building bioinformatics infrastructure such as portable and reproducible bioinformatics workflows for use on heterogeneous African computing environments. Processing and analysis of genomic data is an example of a big data application requiring complex interdependent data analysis workflows. Such bioinformatics workflows take the primary and secondary input data through several computationally-intensive processing steps using different software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly challenging. RESULTS: H3ABioNet has built four workflows to support (1) the calling of variants from high-throughput sequencing data; (2) the analysis of microbial populations from 16S rDNA sequence data; (3) genotyping and genome-wide association studies; and (4) single nucleotide polymorphism imputation. A week-long hackathon was organized in August 2016 with participants from six African bioinformatics groups, and US and European collaborators. Two of the workflows are built using the Common Workflow Language framework (CWL) and two using Nextflow. All the workflows are containerized for improved portability and reproducibility using Docker, and are publicly available for use by members of the H3Africa consortium and the international research community. CONCLUSION: The H3ABioNet workflows have been implemented in view of offering ease of use for the end user and high levels of reproducibility and portability, all while following modern state of the art bioinformatics data processing protocols. The H3ABioNet workflows will service the H3Africa consortium projects and are currently in use. All four workflows are also publicly available for research scientists worldwide to use and adapt for their respective needs. The H3ABioNet workflows will help develop bioinformatics capacity and assist genomics research within Africa and serve to increase the scientific output of H3Africa and its Pan-African Bioinformatics Network.

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.

We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of "Knowledge Engine for Genomics" (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the "Knowledge Network" that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior.

UNLABELLED: Centrifuge is a user-friendly system to simultaneously access Arabidopsis gene annotations and intra- and inter-organism sequence comparison data. The tool allows rapid retrieval of user-selected data for each annotated Arabidopsis gene providing, in any combination, data on the following features: predicted protein properties such as mass, pI, cellular location and transmembrane domains; SWISS-PROT annotations; Interpro domains; Gene Ontology records; verified transcription; BLAST matches to the proteomes of A.thaliana, Oryza sativa (rice), Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. The tool lends itself particularly well to the rapid analysis of contigs or of tens or hundreds of genes identified by high-throughput gene expression experiments. In these cases, a summary table of principal predicted protein features for all genes is given followed by more detailed reports for each individual gene. Centrifuge can also be used for single gene analysis or in a word search mode. AVAILABILITY: http://centrifuge.unil.ch/ CONTACT: [email protected].

The search for similarity between two biological sequences lies at the core of many applications in bioinformatics. This paper aims to highlight a few of the principles that should be kept in mind when evaluating the statistical significance of alignments between sequences. The extreme value distribution is first introduced, which in most cases describes the distribution of alignment scores between a query and a database. The effects of the similarity matrix and gap penalty values on the score distribution are then examined, and it is shown that the alignment statistics can undergo an abrupt phase transition. A few types of random sequence databases used in the estimation of statistical significance are presented, and the statistics employed by the BLAST, FASTA and PRSS programs are compared. Finally the different strategies used to assess the statistical significance of the matches produced by profiles and hidden Markov models are presented.

The genomes of living organisms contain many elements, including genes coding for proteins. The portions of the genes expressed as mature mRNA, collectively known as the transcriptome, represent only a small part of the genome. The expressed sequence tag (EST) databases contain an increasingly large part of the transcriptome of many species. For this reason, these databases are probably the most abundant source of new coding sequences available today. However, the raw data deposited in the EST databases are to a large extent unorganised, unannotated, redundant and of relatively low quality. This paper reviews some of the characteristics of the EST data, and the methods that can be used to find novel protein sequences within them. It also documents a collection of databases, software and web sites that can be useful to biologists interested in mining the EST databases over the Internet, or in establishing a local environment for such analyses.