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Zootaxa ; 4564(1): zootaxa.4564.1.7, 2019 Mar 04.
Artigo em Inglês | MEDLINE | ID: mdl-31716520


Adopting the name Canis dingo for the Dingo to explicitly denote a species-level taxon separate from other canids was suggested by Crowther et al.  (2014) as a means to eliminate taxonomic instability and contention. However, Jackson et al.  (2017), using standard taxonomic and nomenclatural approaches and principles, called instead for continued use of the nomen C. familiaris for all domestic dogs and their derivatives, including the Dingo. (This name, C. familiaris, is applied to all dogs that derive from the domesticated version of the Gray Wolf, Canis lupus, based on nomenclatural convention.) The primary reasons for this call by Jackson et al.  (2017) were: (1) a lack of evidence to show that recognizing multiple species amongst the dog, including the Dingo and New Guinea Singing Dog, was necessary taxonomically, and (2) the principle of nomenclatural priority (the name familiaris Linnaeus, 1758, antedates dingo Meyer, 1793). Overwhelming current evidence from archaeology and genomics indicates that the Dingo is of recent origin in Australia and shares immediate ancestry with other domestic dogs as evidenced by patterns of genetic and morphological variation. Accordingly, for Smith et al.  (2019) to recognise Canis dingo as a distinct species, the onus was on them to overturn current interpretations of available archaeological, genomic, and morphological datasets and instead show that Dingoes have a deeply divergent evolutionary history that distinguishes them from other named forms of Canis (including C. lupus and its domesticated version, C. familiaris). A recent paper by Koepfli et al.  (2015) demonstrates exactly how this can be done in a compelling way within the genus Canis-by demonstrating deep evolutionary divergence between taxa, on the order of hundreds of thousands of years, using data from multiple genetic systems. Smith et al.  (2019) have not done this; instead they have misrepresented the content and conclusions of Jackson et al.  (2017), and contributed extraneous arguments that are not relevant to taxonomic decisions. Here we dissect Smith et al.  (2019), identifying misrepresentations, to show that ecological, behavioural and morphological evidence is insufficient to recognise Dingoes as a separate species from other domestic dogs. We reiterate: the correct binomial name for the taxon derived from Gray Wolves (C. lupus) by passive and active domestication, including Dingoes and other domestic dogs, is Canis familiaris. We are strongly sympathetic to arguments about the historical, ecological, cultural, or other significance of the Dingo, but these are issues that will have to be considered outside of the more narrow scope of taxonomy and nomenclature.

Lobos , Animais , Austrália , Cães , Nova Guiné
BMC Bioinformatics ; 19(1): 457, 2018 Nov 29.
Artigo em Inglês | MEDLINE | ID: mdl-30486782


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.

Biologia Computacional/métodos , Genômica/métodos , África , Humanos , Reprodutibilidade dos Testes
BMC Bioinformatics ; 18(1): 49, 2017 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-28107819


BACKGROUND: Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices. RESULTS: In this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers. CONCLUSIONS: MC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.

Computação em Nuvem , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala , Bases de Dados Genéticas , Genoma Humano , Humanos , Internet , Software , Fluxo de Trabalho
BMC Med Genomics ; 8: 64, 2015 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-26470712


BACKGROUND: While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10's of dollars. RESULTS: We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis of both public benchmarking and large-scale heterogeneous clinical NGS datasets. CONCLUSIONS: Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration.

Computação em Nuvem/economia , Análise Custo-Benefício , Técnicas de Genotipagem/economia , Sequenciamento de Nucleotídeos em Larga Escala/economia , Benchmarking , Genômica , Humanos
F1000Res ; 42015.
Artigo em Inglês | MEDLINE | ID: mdl-26998231


This is a summary of the activities and scientific content of the first International Society for Computational Biology Student Council symposium in Africa. This meeting organized by the students for the students took place 8th of March 2015 in Dar Es Salaam, Tanzania.

Bioinformatics ; 30(20): 2956-8, 2014 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-24982428


SUMMARY: Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow. Workflows can be created on traditional computing clusters as well as cloud-based services. AVAILABILITY AND IMPLEMENTATION: Source code is available for academic non-commercial research purposes. Links to code and documentation are provided at and CONTACT: or SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Linguagens de Programação