Pangenomes have emerged in response to limitations associated with traditional linear reference genomes. In contrast to a traditional reference that is (usually) assembled from a single individual, pangenomes aim to represent all of the genomic variation found in a group of organisms. The term 'pangenome' is currently used to describe multiple different types of genomic information, and limited language is available to differentiate between them. This is frustrating for researchers working in the field and confusing for researchers new to the field. Here, we provide an introduction to pangenomics relevant to both prokaryotic and eukaryotic organisms and propose a formalization of the language used to describe pangenomes (see the Glossary) to improve the specificity of discussion in the field.

Chloroplasts are photosynthetic organelles in algal and plant cells that contain their own genome. Chloroplast genomes are commonly used in evolutionary studies and taxonomic identification and are increasingly becoming a target for crop improvement studies. As DNA sequencing becomes more affordable, researchers are collecting vast swathes of high-quality whole-genome sequence data from laboratory and field settings alike. Whole tissue read libraries sequenced with the primary goal of understanding the nuclear genome will inadvertently contain many reads derived from the chloroplast genome. These whole-genome, whole-tissue read libraries can additionally be used to assemble chloroplast genomes with little to no extra cost. While several tools exist that make use of short-read second generation and third-generation long-read sequencing data for chloroplast genome assembly, these tools may have complex installation steps, inadequate error reporting, poor expandability, and/or lack scalability. Here, we present CLAW (Chloroplast Long-read Assembly Workflow), an easy to install, customise, and use Snakemake tool to assemble chloroplast genomes from chloroplast long-reads found in whole-genome read libraries (https://github.com/aaronphillips7493/CLAW). Using 19 publicly available reference chloroplast genome assemblies and long-read libraries from algal, monocot and eudicot species, we show that CLAW can rapidly produce chloroplast genome assemblies with high similarity to the reference assemblies. CLAW was designed such that users have complete control over parameterisation, allowing individuals to optimise CLAW to their specific use cases. We expect that CLAW will provide researchers (with varying levels of bioinformatics expertise) with an additional resource useful for contributing to the growing number of publicly available chloroplast genome assemblies.

As bioinformatics analyses increase in size and complexity, workflow managers are becoming more popular for building pipelines [1][2][3].Workflow managers, such as Snakemake [4], Nextflow [5], and Cromwell [6] with WDL or CWL [7], empower researchers to build robust pipelines that call a series of tools and scripts to perform a bespoke analysis.Workflow managers enable non-bioinformaticians to run published pipelines with confidence, and workflow managers with graphical user interfaces such as Galaxy [8] and BioWorkflow [9] have helped nonbioinformaticians create their own simple pipelines.Earlier tools for workflow management have been around for a while, including GNU Make, ruffus [10], doit [11], rake for ruby [12], and Makeflow [13].However, the integration of cluster and cloud computing support in Snakemake, Nextflow, and Cromwell helped drive their current popularity.The use of workflow managers facilitates following the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for open scientific research [14].Interestingly, many existing bioinformatics command line tools are wrappers for a series of other software, but since that is the goal of workflow managers, they can be used instead.Examples of command line tools built on a workflow manager include Hecatomb [15,16], ATLAS [17], VirSorter2 [18], spacegraphcats [19], BlobToolKit [20], and PGAP [21].These tools all consist of two key components: a convenience launcher, which provides the command line interface for the tool and compiles the configuration from user command line arguments, and the workflow pipeline and associated files, which performs the actual analysis.Developing bioinformatics software is much quicker and easier when not reinventing the wheel.For instance, if a tool needs to parse a GenBank file, is it better to code that process manually, or simply load a library that is designed to robustly parse and validate these files?The same concept applies to how bioinformatics software runs.It is possible to write functions to compare file timestamps to add reentrancy to the software, catch error codes, and throw meaningful messages when system calls fail, validate and cleanup intermediate files, allow interaction with a job scheduling system, and perform steps in isolated containers, etc.When workflow managers were still in their infancy, the authors of several popular genome

EMBL Australia Bioinformatics Resource (EMBL-ABR) is a developing national research infrastructure, providing bioinformatics resources and support to life science and biomedical researchers in Australia. EMBL-ABR comprises 10 geographically distributed national nodes with one coordinating hub, with current funding provided through Bioplatforms Australia and the University of Melbourne for its initial 2-year development phase. The EMBL-ABR mission is to: (1) increase Australia's capacity in bioinformatics and data sciences; (2) contribute to the development of training in bioinformatics skills; (3) showcase Australian data sets at an international level and (4) enable engagement in international programs. The activities of EMBL-ABR are focussed in six key areas, aligning with comparable international initiatives such as ELIXIR, CyVerse and NIH Commons. These key areas-Tools, Data, Standards, Platforms, Compute and Training-are described in this article.

The Bioinformatics Training Platform (BTP) has been developed to provide access to the computational infrastructure required to deliver sophisticated hands-on bioinformatics training courses. The BTP is a cloud-based solution that is in active use for delivering next-generation sequencing training to Australian researchers at geographically dispersed locations. The BTP was built to provide an easy, accessible, consistent and cost-effective approach to delivering workshops at host universities and organizations with a high demand for bioinformatics training but lacking the dedicated bioinformatics training suites required. To support broad uptake of the BTP, the platform has been made compatible with multiple cloud infrastructures. The BTP is an open-source and open-access resource. To date, 20 training workshops have been delivered to over 700 trainees at over 10 venues across Australia using the BTP.

There is a clear demand for hands-on bioinformatics training. The development of bioinformatics workshop content is both time-consuming and expensive. Therefore, enabling trainers to develop bioinformatics workshops in a way that facilitates reuse is becoming increasingly important. The most widespread practice for sharing workshop content is through making PDF, PowerPoint and Word documents available online. While this effort is to be commended, such content is usually not so easy to reuse or repurpose and does not capture all the information required for a third party to rerun a workshop. We present an open, collaborative framework for developing and maintaining, reusable and shareable hands-on training workshop content.

The widespread adoption of high-throughput next-generation sequencing (NGS) technology among the Australian life science research community is highlighting an urgent need to up-skill biologists in tools required for handling and analysing their NGS data. There is currently a shortage of cutting-edge bioinformatics training courses in Australia as a consequence of a scarcity of skilled trainers with time and funding to develop and deliver training courses. To address this, a consortium of Australian research organizations, including Bioplatforms Australia, the Commonwealth Scientific and Industrial Research Organisation and the Australian Bioinformatics Network, have been collaborating with EMBL-EBI training team. A group of Australian bioinformaticians attended the train-the-trainer workshop to improve training skills in developing and delivering bioinformatics workshop curriculum. A 2-day NGS workshop was jointly developed to provide hands-on knowledge and understanding of typical NGS data analysis workflows. The road show-style workshop was successfully delivered at five geographically distant venues in Australia using the newly established Australian NeCTAR Research Cloud. We highlight the challenges we had to overcome at different stages from design to delivery, including the establishment of an Australian bioinformatics training network and the computing infrastructure and resource development. A virtual machine image, workshop materials and scripts for configuring a machine with workshop contents have all been made available under a Creative Commons Attribution 3.0 Unported License. This means participants continue to have convenient access to an environment they had become familiar and bioinformatics trainers are able to access and reuse these resources.

SUMMARY: We make the PCIT algorithm, used for detecting meaningful gene-gene associations in co-expression networks, available as an R package. Automatic detection of a suitable parallel environment is used such that scripts are portable between parallel and non-parallel environments with no modification of the script. AVAILABILITY AND IMPLEMENTATION: Source code and binaries freely available (under GPL-3) for download via CRAN at http://cran.r-project.org/package=PCIT, implemented in R and supported on Linux and MS Windows.

BACKGROUND: Whole genome association studies using highly dense single nucleotide polymorphisms (SNPs) are a set of methods to identify DNA markers associated with variation in a particular complex trait of interest. One of the main outcomes from these studies is a subset of statistically significant SNPs. Finding the potential biological functions of such SNPs can be an important step towards further use in human and agricultural populations (e.g., for identifying genes related to susceptibility to complex diseases or genes playing key roles in development or performance). The current challenge is that the information holding the clues to SNP functions is distributed across many different databases. Efficient bioinformatics tools are therefore needed to seamlessly integrate up-to-date functional information on SNPs. Many web services have arisen to meet the challenge but most work only within the framework of human medical research. Although we acknowledge the importance of human research, we identify there is a need for SNP annotation tools for other organisms. DESCRIPTION: We introduce an R package called FunctSNP, which is the user interface to custom built species-specific databases. The local relational databases contain SNP data together with functional annotations extracted from online resources. FunctSNP provides a unified bioinformatics resource to link SNPs with functional knowledge (e.g., genes, pathways, ontologies). We also introduce dbAutoMaker, a suite of Perl scripts, which can be scheduled to run periodically to automatically create/update the customised SNP databases. We illustrate the use of FunctSNP with a livestock example, but the approach and software tools presented here can be applied also to human and other organisms. CONCLUSIONS: Finding the potential functional significance of SNPs is important when further using the outcomes from whole genome association studies. FunctSNP is unique in that it is the only R package that links SNPs to functional annotation. FunctSNP interfaces to local SNP customised databases which can be built for any species contained in the National Center for Biotechnology Information dbSNP database.