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31 January 2018

Cloud computing for genomic data analysis and collaboration [Nature Reviews Genetics]

Keywords : computational biology and bioinformatics, genetic databases, genomics, next-generation sequencing, research data

Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data.

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31 January 2018

The state of software for evolutionary biology [Molecular Biology and Evolution]

Keywords : software engineering quality, scientific computing, data analysis, numerical analysis, policy issues, evolutionary inference software

With Next Generation Sequencing data being routinely used, evolutionary biology is transforming into a computational science. Thus, researchers have to rely on a growing number of increasingly complex software. All widely used core tools in the field have grown considerably, in terms of the number of features as well as lines of code and consequently, also with respect to software complexity. A topic that has received little attention is the software engineering quality of widely used core analysis tools. Software developers appear to rarely assess the quality of their code, and this can have potential negative consequences for end-users. To this end, we assessed the code quality of 16 highly cited and compute-intensive tools mainly written in C/C ++ (e.g., MrBayes, MAFFT, SweepFinder etc.) and JAVA (BEAST) from the broader area of evolutionary biology that are being routinely used in current data analysis pipelines.(...)

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31 January 2018

Network spandrels reflect ecological assembly [Ecology Letters]

Keywords : coexistence, community assembly, interspecific competition, network structure, stability

Ecological networks that exhibit stable dynamics should theoretically persist longer than those that fluctuate wildly. Thus, network structures which are over-represented in natural systems are often hypothesised to be either a cause or consequence of ecological stability. Rarely considered, however, is that these network structures can also be by-products of the processes that determine how new species attempt to join the community. Using a simulation approach in tandem with key results from random matrix theory, we illustrate how historical assembly mechanisms alter the structure of ecological networks.(...)

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31 January 2018

History and environment shape species pools and community diversity in European beech forests [Nature Ecology & Evolution]

Keywords : biodiversity, biogeography, community ecology, plant ecology

A central hypothesis of ecology states that regional diversity influences local diversity through species-pool effects. Species pools are supposedly shaped by large-scale factors and then filtered into ecological communities, but understanding these processes requires the analysis of large datasets across several regions. Here, we use a framework of community assembly at a continental scale to test the relative influence of historical and environmental drivers, in combination with regional or local species pools, on community species richness and community completeness. Using 42,173 vegetation plots sampled across European beech forests, we found that large-scale factors largely accounted for species pool sizes.(...)

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30 January 2018

Best practices for reporting climate data in ecology [Nature Climate Change]

A large number of published ecological studies fail to include basic information about the climate data used. In the interest of reproducibility and transparency, we offer recommendations for best practices that we urge Editors, authors, and reviewers to adopt in future publications.

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