LotuS2: an ultrafast and highly accurate tool for amplicon sequencing analysis.

BACKGROUND: Amplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (less OTU scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences. RESULTS: In LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines, yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxon composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified taxa and a higher fraction of reads assigned to true taxa (48% and 57% at species; 83% and 98% at genus level, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reported 16S sequences. CONCLUSION: LotuS2 is a lightweight and user-friendly pipeline that is fast, precise, and streamlined, using extensive pre- and post-ASV/OTU clustering steps to further increase data quality. High data usage rates and reliability enable high-throughput microbiome analysis in minutes. AVAILABILITY: LotuS2 is available from GitHub, conda, or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/ . Video Abstract.

Standards recommendations for the Earth BioGenome Project.

A global international initiative, such as the Earth BioGenome Project (EBP), requires both agreement and coordination on standards to ensure that the collective effort generates rapid progress toward its goals. To this end, the EBP initiated five technical standards committees comprising volunteer members from the global genomics scientific community: Sample Collection and Processing, Sequencing and Assembly, Annotation, Analysis, and IT and Informatics. The current versions of the resulting standards documents are available on the EBP website, with the recognition that opportunities, technologies, and challenges may improve or change in the future, requiring flexibility for the EBP to meet its goals. Here, we describe some highlights from the proposed standards, and areas where additional challenges will need to be met.

ISA API: An open platform for interoperable life science experimental metadata.

BACKGROUND: The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open source community specifications and software tools for enabling discovery, exchange, and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab-a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, the JSON serialization ISA-JSON was developed. RESULTS: In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters, and its growing user community. CONCLUSIONS: The ISA API provides users with rich programmatic metadata-handling functionality to support automation, a common interface, and an interoperable medium between the 2 ISA formats, as well as with other life science data formats required for depositing data in public databases.

Knowledge and Attitudes Among Life Scientists Toward Reproducibility Within Journal Articles: A Research Survey.

We constructed a survey to understand how authors and scientists view the issues around reproducibility, focusing on interactive elements such as interactive figures embedded within online publications, as a solution for enabling the reproducibility of experiments. We report the views of 251 researchers, comprising authors who have published in eLIFE Sciences, and those who work at the Norwich Biosciences Institutes (NBI). The survey also outlines to what extent researchers are occupied with reproducing experiments themselves. Currently, there is an increasing range of tools that attempt to address the production of reproducible research by making code, data, and analyses available to the community for reuse. We wanted to collect information about attitudes around the consumer end of the spectrum, where life scientists interact with research outputs to interpret scientific results. Static plots and figures within articles are a central part of this interpretation, and therefore we asked respondents to consider various features for an interactive figure within a research article that would allow them to better understand and reproduce a published analysis. The majority (91%) of respondents reported that when authors describe their research methodology (methods and analyses) in detail, published research can become more reproducible. The respondents believe that having interactive figures in published papers is a beneficial element to themselves, the papers they read as well as to their readers. Whilst interactive figures are one potential solution for consuming the results of research more effectively to enable reproducibility, we also review the equally pressing technical and cultural demands on researchers that need to be addressed to achieve greater success in reproducibility in the life sciences.

Recommendations for connecting molecular sequence and biodiversity research infrastructures through ELIXIR.

Threats to global biodiversity are increasingly recognised by scientists and the public as a critical challenge. Molecular sequencing technologies offer means to catalogue, explore, and monitor the richness and biogeography of life on Earth. However, exploiting their full potential requires tools that connect biodiversity infrastructures and resources. As a research infrastructure developing services and technical solutions that help integrate and coordinate life science resources across Europe, ELIXIR is a key player. To identify opportunities, highlight priorities, and aid strategic thinking, here we survey approaches by which molecular technologies help inform understanding of biodiversity. We detail example use cases to highlight how DNA sequencing is: resolving taxonomic issues; Increasing knowledge of marine biodiversity; helping understand how agriculture and biodiversity are critically linked; and playing an essential role in ecological studies. Together with examples of national biodiversity programmes, the use cases show where progress is being made but also highlight common challenges and opportunities for future enhancement of underlying technologies and services that connect molecular and wider biodiversity domains. Based on emerging themes, we propose key recommendations to guide future funding for biodiversity research: biodiversity and bioinformatic infrastructures need to collaborate closely and strategically; taxonomic efforts need to be aligned and harmonised across domains; metadata needs to be standardised and common data management approaches widely adopted; current approaches need to be scaled up dramatically to address the anticipated explosion of molecular data; bioinformatics support for biodiversity research needs to be enabled and sustained; training for end users of biodiversity research infrastructures needs to be prioritised; and community initiatives need to be proactive and focused on enabling solutions. For sequencing data to deliver their full potential they must be connected to knowledge: together, molecular sequence data collection initiatives and biodiversity research infrastructures can advance global efforts to prevent further decline of Earth's biodiversity.

Data management challenges for artificial intelligence in plant and agricultural research.

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

CerealsDB-new tools for the analysis of the wheat genome: update 2020.

CerealsDB (www.cerealsdb.uk.net) is an online repository of mainly hexaploid wheat (Triticum aestivum) single nucleotide polymorphisms (SNPs) and genotyping data. The CerealsDB website has been designed to enable wheat breeders and scientists to select the appropriate markers for research breeding tasks, such as marker-assisted selection. We report a large update of genotyping information for over 6000 wheat accessions and describe new webtools for exploring and visualizing the data. We also describe a new database of quantitative trait loci that links phenotypic traits to CerealsDB SNP markers and allelic scores for each of those markers. CerealsDB is an open-access website that hosts information on wheat SNPs considered useful for both plant breeders and research scientists. The latest CerealsDB database is available at https://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/indexNEW.php.

A Galaxy-based training resource for single-cell RNA-sequencing quality control and analyses.

BACKGROUND: It is not a trivial step to move from single-cell RNA-sequencing (scRNA-seq) data production to data analysis. There is a lack of intuitive training materials and easy-to-use analysis tools, and researchers can find it difficult to master the basics of scRNA-seq quality control and the later analysis. RESULTS: We have developed a range of practical scripts, together with their corresponding Galaxy wrappers, that make scRNA-seq training and quality control accessible to researchers previously daunted by the prospect of scRNA-seq analysis. We implement a "visualize-filter-visualize" paradigm through simple command line tools that use the Loom format to exchange data between the tools. The point-and-click nature of Galaxy makes it easy to assess, visualize, and filter scRNA-seq data from short-read sequencing data. CONCLUSION: We have developed a suite of scRNA-seq tools that can be used for both training and more in-depth analyses.

Aequatus: an open-source homology browser.

Background: Phylogenetic information inferred from the study of homologous genes helps us to understand the evolution of genes and gene families, including the identification of ancestral gene duplication events as well as regions under positive or purifying selection within lineages. Gene family and orthogroup characterization enables the identification of syntenic blocks, which can then be visualized with various tools. Unfortunately, currently available tools display only an overview of syntenic regions as a whole, limited to the gene level, and none provide further details about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. Findings: We present Aequatus, an open-source web-based tool that provides an in-depth view of gene structure across gene families, with various options to render and filter visualizations. It relies on precalculated alignment and gene feature information typically held in, but not limited to, the Ensembl Compara and Core databases. We also offer Aequatus.js, a reusable JavaScript module that fulfills the visualization aspects of Aequatus, available within the Galaxy web platform as a visualization plug-in, which can be used to visualize gene trees generated by the GeneSeqToFamily workflow.

GeneSeqToFamily: a Galaxy workflow to find gene families based on the Ensembl Compara GeneTrees pipeline.

Background: Gene duplication is a major factor contributing to evolutionary novelty, and the contraction or expansion of gene families has often been associated with morphological, physiological, and environmental adaptations. The study of homologous genes helps us to understand the evolution of gene families. It plays a vital role in finding ancestral gene duplication events as well as identifying genes that have diverged from a common ancestor under positive selection. There are various tools available, such as MSOAR, OrthoMCL, and HomoloGene, to identify gene families and visualize syntenic information between species, providing an overview of syntenic regions evolution at the family level. Unfortunately, none of them provide information about structural changes within genes, such as the conservation of ancestral exon boundaries among multiple genomes. The Ensembl GeneTrees computational pipeline generates gene trees based on coding sequences, provides details about exon conservation, and is used in the Ensembl Compara project to discover gene families. Findings: A certain amount of expertise is required to configure and run the Ensembl Compara GeneTrees pipeline via command line. Therefore, we converted this pipeline into a Galaxy workflow, called GeneSeqToFamily, and provided additional functionality. This workflow uses existing tools from the Galaxy ToolShed, as well as providing additional wrappers and tools that are required to run the workflow. Conclusions: GeneSeqToFamily represents the Ensembl GeneTrees pipeline as a set of interconnected Galaxy tools, so they can be run interactively within the Galaxy's user-friendly workflow environment while still providing the flexibility to tailor the analysis by changing configurations and tools if necessary. Additional tools allow users to subsequently visualize the gene families produced by the workflow, using the Aequatus.js interactive tool, which has been developed as part of the Aequatus software project.

Leaf LIMS: A Flexible Laboratory Information Management System with a Synthetic Biology Focus.

This paper presents Leaf LIMS, a flexible laboratory information management system (LIMS) designed to address the complexity of synthetic biology workflows. At the project's inception there was a lack of a LIMS designed specifically to address synthetic biology processes, with most systems focused on either next generation sequencing or biobanks and clinical sample handling. Leaf LIMS implements integrated project, item, and laboratory stock tracking, offering complete sample and construct genealogy, materials and lot tracking, and modular assay data capture. Hence, it enables highly configurable task-based workflows and supports data capture from project inception to completion. As such, in addition to it supporting synthetic biology it is ideal for many laboratory environments with multiple projects and users. The system is deployed as a web application through Docker and is provided under a permissive MIT license. It is freely available for download at https://leaflims.github.io .

An improved assembly and annotation of the allohexaploid wheat genome identifies complete families of agronomic genes and provides genomic evidence for chromosomal translocations.

Advances in genome sequencing and assembly technologies are generating many high-quality genome sequences, but assemblies of large, repeat-rich polyploid genomes, such as that of bread wheat, remain fragmented and incomplete. We have generated a new wheat whole-genome shotgun sequence assembly using a combination of optimized data types and an assembly algorithm designed to deal with large and complex genomes. The new assembly represents >78% of the genome with a scaffold N50 of 88.8 kb that has a high fidelity to the input data. Our new annotation combines strand-specific Illumina RNA-seq and Pacific Biosciences (PacBio) full-length cDNAs to identify 104,091 high-confidence protein-coding genes and 10,156 noncoding RNA genes. We confirmed three known and identified one novel genome rearrangements. Our approach enables the rapid and scalable assembly of wheat genomes, the identification of structural variants, and the definition of complete gene models, all powerful resources for trait analysis and breeding of this key global crop.

Developing data interoperability using standards: A wheat community use case.

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations. To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach's potential to be generalizable to other (agricultural) domains.

Prevalence and Dynamics of Ribosomal DNA Micro-heterogeneity Are Linked to Population History in Two Contrasting Yeast Species.

Despite the considerable number and taxonomic breadth of past and current genome sequencing projects, many of which necessarily encompass the ribosomal DNA, detailed information on the prevalence and evolutionary significance of sequence variation in this ubiquitous genomic region are severely lacking. Here, we attempt to address this issue in two closely related yet contrasting yeast species, the baker's yeast Saccharomyces cerevisiae and the wild yeast Saccharomyces paradoxus. By drawing on existing datasets from the Saccharomyces Genome Resequencing Project, we identify a rich seam of ribosomal DNA sequence variation, characterising 1,068 and 970 polymorphisms in 34 S. cerevisiae and 26 S. paradoxus strains respectively. We discover the two species sets exhibit distinct mutational profiles. Furthermore, we show for the first time that unresolved rDNA sequence variation resulting from imperfect concerted evolution of the ribosomal DNA region follows a U-shaped allele frequency distribution in each species, similar to loci that evolve under non-concerted mechanisms but arising through rather different evolutionary processes. Finally, we link differences between the shapes of these allele frequency distributions to the two species' contrasting population histories.

CerealsDB 3.0: expansion of resources and data integration.

BACKGROUND: The increase in human populations around the world has put pressure on resources, and as a consequence food security has become an important challenge for the 21st century. Wheat (Triticum aestivum) is one of the most important crops in human and livestock diets, and the development of wheat varieties that produce higher yields, combined with increased resistance to pests and resilience to changes in climate, has meant that wheat breeding has become an important focus of scientific research. In an attempt to facilitate these improvements in wheat, plant breeders have employed molecular tools to help them identify genes for important agronomic traits that can be bred into new varieties. Modern molecular techniques have ensured that the rapid and inexpensive characterisation of SNP markers and their validation with modern genotyping methods has produced a valuable resource that can be used in marker assisted selection. CerealsDB was created as a means of quickly disseminating this information to breeders and researchers around the globe. DESCRIPTION: CerealsDB version 3.0 is an online resource that contains a wide range of genomic datasets for wheat that will assist plant breeders and scientists to select the most appropriate markers for use in marker assisted selection. CerealsDB includes a database which currently contains in excess of a million putative varietal SNPs, of which several hundreds of thousands have been experimentally validated. In addition, CerealsDB also contains new data on functional SNPs predicted to have a major effect on protein function and we have constructed a web service to encourage data integration and high-throughput programmatic access. CONCLUSION: CerealsDB is an open access website that hosts information on SNPs that are considered useful for both plant breeders and research scientists. The recent inclusion of web services designed to federate genomic data resources allows the information on CerealsDB to be more fully integrated with the WheatIS network and other biological databases.

NanoOK: multi-reference alignment analysis of nanopore sequencing data, quality and error profiles.

MOTIVATION: The Oxford Nanopore MinION sequencer, currently in pre-release testing through the MinION Access Programme (MAP), promises long reads in real-time from an inexpensive, compact, USB device. Tools have been released to extract FASTA/Q from the MinION base calling output and to provide basic yield statistics. However, no single tool yet exists to provide comprehensive alignment-based quality control and error profile analysis--something that is extremely important given the speed with which the platform is evolving. RESULTS: NanoOK generates detailed tabular and graphical output plus an in-depth multi-page PDF report including error profile, quality and yield data. NanoOK is multi-reference, enabling detailed analysis of metagenomic or multiplexed samples. Four popular Nanopore aligners are supported and it is easily extensible to include others. AVAILABILITY AND IMPLEMENTATION: NanoOK is an open-source software, implemented in Java with supporting R scripts. It has been tested on Linux and Mac OS X and can be downloaded from https://github.com/TGAC/NanoOK. A VirtualBox VM containing all dependencies and the DH10B read set used in this article is available from http://opendata.tgac.ac.uk/nanook/. A Docker image is also available from Docker Hub--see program documentation https://documentation.tgac.ac.uk/display/NANOOK. CONTACT: richard.leggett@tgac.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ribosomal DNA sequence heterogeneity reflects intraspecies phylogenies and predicts genome structure in two contrasting yeast species.

The ribosomal RNA encapsulates a wealth of evolutionary information, including genetic variation that can be used to discriminate between organisms at a wide range of taxonomic levels. For example, the prokaryotic 16S rDNA sequence is very widely used both in phylogenetic studies and as a marker in metagenomic surveys and the internal transcribed spacer region, frequently used in plant phylogenetics, is now recognized as a fungal DNA barcode. However, this widespread use does not escape criticism, principally due to issues such as difficulties in classification of paralogous versus orthologous rDNA units and intragenomic variation, both of which may be significant barriers to accurate phylogenetic inference. We recently analyzed data sets from the Saccharomyces Genome Resequencing Project, characterizing rDNA sequence variation within multiple strains of the baker's yeast Saccharomyces cerevisiae and its nearest wild relative Saccharomyces paradoxus in unprecedented detail. Notably, both species possess single locus rDNA systems. Here, we use these new variation datasets to assess whether a more detailed characterization of the rDNA locus can alleviate the second of these phylogenetic issues, sequence heterogeneity, while controlling for the first. We demonstrate that a strong phylogenetic signal exists within both datasets and illustrate how they can be used, with existing methodology, to estimate intraspecies phylogenies of yeast strains consistent with those derived from whole-genome approaches. We also describe the use of partial Single Nucleotide Polymorphisms, a type of sequence variation found only in repetitive genomic regions, in identifying key evolutionary features such as genome hybridization events and show their consistency with whole-genome Structure analyses. We conclude that our approach can transform rDNA sequence heterogeneity from a problem to a useful source of evolutionary information, enabling the estimation of highly accurate phylogenies of closely related organisms, and discuss how it could be extended to future studies of multilocus rDNA systems. [concerted evolution; genome hydridisation; phylogenetic analysis; ribosomal DNA; whole genome sequencing; yeast].

wigExplorer, a BioJS component to visualise wig data.

wigExplorer is a BioJS component whose main purpose is to provide a platform for visualisation of wig-formatted data. Wig files are extensively used by genome browsers such as the UCSC Genome Browser. wigExplorer follows the BioJS standard specification, requiring a simple configuration and installation. wigExplorer provides an easy way to navigate the visible region of the canvas and allows interaction with other components via predefined events. Availability: http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7721.

Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics.

The processes of quality assessment and control are an active area of research at The Genome Analysis Centre (TGAC). Unlike other sequencing centers that often concentrate on a certain species or technology, TGAC applies expertise in genomics and bioinformatics to a wide range of projects, often requiring bespoke wet lab and in silico workflows. TGAC is fortunate to have access to a diverse range of sequencing and analysis platforms, and we are at the forefront of investigations into library quality and sequence data assessment. We have developed and implemented a number of algorithms, tools, pipelines and packages to ascertain, store, and expose quality metrics across a number of next-generation sequencing platforms, allowing rapid and in-depth cross-platform Quality Control (QC) bioinformatics. In this review, we describe these tools as a vehicle for data-driven informatics, offering the potential to provide richer context for downstream analysis and to inform experimental design.