3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists.

RNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on specialized bioinformatics skills. We have developed the '3D RNA-seq' App, an R shiny App and web-based pipeline for the comprehensive analysis of RNA-seq data from any organism. It represents an easy-to-use, flexible and powerful tool for analysis of both gene and transcript-level gene expression to identify differential gene/transcript expression, differential alternative splicing and differential transcript usage (3D) as well as isoform switching from RNA-seq data. 3D RNA-seq integrates state-of-the-art differential expression analysis tools and adopts best practice for RNA-seq analysis. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to analyse their RNA-seq data. It achieves this by operating through a user-friendly graphical interface which automates the data flow through the programs in the pipeline. The comprehensive analysis performed by 3D RNA-seq is extremely rapid and accurate, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of data from a time-series of cold-treated Arabidopsis plants and from dexamethasone-treated male and female mouse cortex and hypothalamus data identifying dexamethasone-induced sex- and brain region-specific differential gene expression and alternative splicing.

Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints.

Undomesticated wild species, crop wild relatives, and landraces represent sources of variation for wheat improvement to address challenges from climate change and the growing human population. Here, we study 56,342 domesticated hexaploid, 18,946 domesticated tetraploid and 3,903 crop wild relatives in a massive-scale genotyping and diversity analysis. Using DArTseqTM technology, we identify more than 300,000 high-quality SNPs and SilicoDArT markers and align them to three reference maps: the IWGSC RefSeq v1.0 genome assembly, the durum wheat genome assembly (cv. Svevo), and the DArT genetic map. On average, 72% of the markers are uniquely placed on these maps and 50% are linked to genes. The analysis reveals landraces with unexplored diversity and genetic footprints defined by regions under selection. This provides fertile ground to develop wheat varieties of the future by exploring specific gene or chromosome regions and identifying germplasm conserving allelic diversity missing in current breeding programs.

Three poisonous plants (Oenanthe, Cicuta and Anamirta) that antagonise the effect of γ-aminobutyric acid in human brain.

Although we are familiar with common British plants that are poisonous, such as Atropa belladonna (deadly nightshade) and Aconitum napellus (monkshood), the two most poisonous plants in the British Flora are Oenanthe crocata (dead man's fingers) and Cicuta virosa (cowbane). In recent years their poisons have been shown to be polyacetylenes (n-C2H2). The plants closely resemble two of the most common plants in the family Apiaceae (Umbelliferae), celery and parsley. Unwittingly, they are ingested by naive foragers and death occurs very rapidly. The third plant Anamirta derives from South-East Asia and contains a powerful convulsant, picrotoxin, which has been used from time immemorial to catch fish, and more recently to poison Birds of Paradise. All three poisons have been shown to block the γ-aminobutyric acid (GABA) system in the human brain that normally has a powerful inhibitory neuronal action. It has also been established that two groups of sedative drugs, barbiturates and benzodiazepines, exert their inhibitory action by stimulating the GABA system. These drugs are the treatments of choice for poisoning by the three vicious plants.

BrAPI-an application programming interface for plant breeding applications.

MOTIVATION: Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. RESULTS: To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases. AVAILABILITY AND IMPLEMENTATION: More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

Exome sequencing of geographically diverse barley landraces and wild relatives gives insights into environmental adaptation.

After domestication, during a process of widespread range extension, barley adapted to a broad spectrum of agricultural environments. To explore how the barley genome responded to the environmental challenges it encountered, we sequenced the exomes of a collection of 267 georeferenced landraces and wild accessions. A combination of genome-wide analyses showed that patterns of variation have been strongly shaped by geography and that variant-by-environment associations for individual genes are prominent in our data set. We observed significant correlations of days to heading (flowering) and height with seasonal temperature and dryness variables in common garden experiments, suggesting that these traits were major drivers of environmental adaptation in the sampled germplasm. A detailed analysis of known flowering-associated genes showed that many contain extensive sequence variation and that patterns of single- and multiple-gene haplotypes exhibit strong geographical structuring. This variation appears to have substantially contributed to range-wide ecogeographical adaptation, but many factors key to regional success remain unidentified.

Tablet: Visualizing Next-Generation Sequence Assemblies and Mappings.

This chapter is designed to be a practical guide to using Tablet for the visualization of next/second-generation (NGS) sequencing data. NGS data is being produced more frequently and in greater data volumes every year. As such, it is increasingly important to have tools which enable biologists and bioinformaticians to understand and gain key insights into their data. Visualization can play a key role in the exploration of such data as well as aid in the visual validation of sequence assemblies and features such as single nucleotide polymorphisms (SNPs). We aim to show several use cases which demonstrate Tablet's ability to visually highlight various situations of interest which can arise in NGS data.

An investigation of causes of false positive single nucleotide polymorphisms using simulated reads from a small eukaryote genome.

BACKGROUND: Single Nucleotide Polymorphisms (SNPs) are widely used molecular markers, and their use has increased massively since the inception of Next Generation Sequencing (NGS) technologies, which allow detection of large numbers of SNPs at low cost. However, both NGS data and their analysis are error-prone, which can lead to the generation of false positive (FP) SNPs. We explored the relationship between FP SNPs and seven factors involved in mapping-based variant calling - quality of the reference sequence, read length, choice of mapper and variant caller, mapping stringency and filtering of SNPs by read mapping quality and read depth. This resulted in 576 possible factor level combinations. We used error- and variant-free simulated reads to ensure that every SNP found was indeed a false positive. RESULTS: The variation in the number of FP SNPs generated ranged from 0 to 36,621 for the 120 million base pairs (Mbp) genome. All of the experimental factors tested had statistically significant effects on the number of FP SNPs generated and there was a considerable amount of interaction between the different factors. Using a fragmented reference sequence led to a dramatic increase in the number of FP SNPs generated, as did relaxed read mapping and a lack of SNP filtering. The choice of reference assembler, mapper and variant caller also significantly affected the outcome. The effect of read length was more complex and suggests a possible interaction between mapping specificity and the potential for contributing more false positives as read length increases. CONCLUSIONS: The choice of tools and parameters involved in variant calling can have a dramatic effect on the number of FP SNPs produced, with particularly poor combinations of software and/or parameter settings yielding tens of thousands in this experiment. Between-factor interactions make simple recommendations difficult for a SNP discovery pipeline but the quality of the reference sequence is clearly of paramount importance. Our findings are also a stark reminder that it can be unwise to use the relaxed mismatch settings provided as defaults by some read mappers when reads are being mapped to a relatively unfinished reference sequence from e.g. a non-model organism in its early stages of genomic exploration.

Chromatin state analysis of the barley epigenome reveals a higher-order structure defined by H3K27me1 and H3K27me3 abundance.

Combinations of histones carrying different covalent modifications are a major component of epigenetic variation. We have mapped nine modified histones in the barley seedling epigenome by chromatin immunoprecipitation next-generation sequencing (ChIP-seq). The chromosomal distributions of the modifications group them into four different classes, and members of a given class also tend to coincide at the local DNA level, suggesting that global distribution patterns reflect local epigenetic environments. We used this peak sharing to define 10 chromatin states representing local epigenetic environments in the barley genome. Five states map mainly to genes and five to intergenic regions. Two genic states involving H3K36me3 are preferentially associated with constitutive gene expression, while an H3K27me3-containing genic state is associated with differentially expressed genes. The 10 states display striking distribution patterns that divide barley chromosomes into three distinct global environments. First, telomere-proximal regions contain high densities of H3K27me3 covering both genes and intergenic DNA, together with very low levels of the repressive H3K27me1 modification. Flanking these are gene-rich interior regions that are rich in active chromatin states and have greatly decreased levels of H3K27me3 and increasing amounts of H3K27me1 and H3K9me2. Lastly, H3K27me3-depleted pericentromeric regions contain gene islands with active chromatin states separated by extensive retrotransposon-rich regions that are associated with abundant H3K27me1 and H3K9me2 modifications. We propose an epigenomic framework for barley whereby intergenic H3K27me3 specifies facultative heterochromatin in the telomere-proximal regions and H3K27me1 is diagnostic for constitutive heterochromatin elsewhere in the barley genome.

Helium: visualization of large scale plant pedigrees.

BACKGROUND: Plant breeders use an increasingly diverse range of data types to identify lines with desirable characteristics suitable to be taken forward in plant breeding programmes. There are a number of key morphological and physiological traits, such as disease resistance and yield that need to be maintained and improved upon if a commercial variety is to be successful. Computational tools that provide the ability to integrate and visualize this data with pedigree structure, will enable breeders to make better decisions on the lines that are used in crossings to meet both the demands for increased yield/production and adaptation to climate change. RESULTS: We have used a large and unique set of experimental barley (H. vulgare) data to develop a prototype pedigree visualization system. We then used this prototype to perform a subjective user evaluation with domain experts to guide and direct the development of an interactive pedigree visualization tool called Helium. CONCLUSIONS: We show that Helium allows users to easily integrate a number of data types along with large plant pedigrees to offer an integrated environment in which they can explore pedigree data. We have also verified that users were happy with the abstract representation of pedigrees that we have used in our visualization tool.

tropiTree: an NGS-based EST-SSR resource for 24 tropical tree species.

The development of genetic tools for non-model organisms has been hampered by cost, but advances in next-generation sequencing (NGS) have created new opportunities. In ecological research, this raises the prospect for developing molecular markers to simultaneously study important genetic processes such as gene flow in multiple non-model plant species within complex natural and anthropogenic landscapes. Here, we report the use of bar-coded multiplexed paired-end Illumina NGS for the de novo development of expressed sequence tag-derived simple sequence repeat (EST-SSR) markers at low cost for a range of 24 tree species. Each chosen tree species is important in complex tropical agroforestry systems where little is currently known about many genetic processes. An average of more than 5,000 EST-SSRs was identified for each of the 24 sequenced species, whereas prior to analysis 20 of the species had fewer than 100 nucleotide sequence citations. To make results available to potential users in a suitable format, we have developed an open-access, interactive online database, tropiTree (http://bioinf.hutton.ac.uk/tropiTree), which has a range of visualisation and search facilities, and which is a model for the efficient presentation and application of NGS data.

The low-recombining pericentromeric region of barley restricts gene diversity and evolution but not gene expression.

The low-recombining pericentromeric region of the barley genome contains roughly a quarter of the genes of the species, embedded in low-recombining DNA that is rich in repeats and repressive chromatin signatures. We have investigated the effects of pericentromeric region residency upon the expression, diversity and evolution of these genes. We observe no significant difference in average transcript level or developmental RNA specificity between the barley pericentromeric region and the rest of the genome. In contrast, all of the evolutionary parameters studied here show evidence of compromised gene evolution in this region. First, genes within the pericentromeric region of wild barley show reduced diversity and significantly weakened purifying selection compared with the rest of the genome. Second, gene duplicates (ohnolog pairs) derived from the cereal whole-genome duplication event ca. 60MYa have been completely eliminated from the barley pericentromeric region. Third, local gene duplication in the pericentromeric region is reduced by 29% relative to the rest of the genome. Thus, the pericentromeric region of barley is a permissive environment for gene expression but has restricted gene evolution in a sizeable fraction of barley's genes.

Using Tablet for visual exploration of second-generation sequencing data.

The advent of second-generation sequencing (2GS) has provided a range of significant new challenges for the visualization of sequence assemblies. These include the large volume of data being generated, short-read lengths and different data types and data formats associated with the diversity of new sequencing technologies. This article illustrates how Tablet-a high-performance graphical viewer for visualization of 2GS assemblies and read mappings-plays an important role in the analysis of these data. We present Tablet, and through a selection of use cases, demonstrate its value in quality assurance and scientific discovery, through features such as whole-reference coverage overviews, variant highlighting, paired-end read mark-up, GFF3-based feature tracks and protein translations. We discuss the computing and visualization techniques utilized to provide a rich and responsive graphical environment that enables users to view a range of file formats with ease. Tablet installers can be freely downloaded from http://bioinf.hutton.ac.uk/tablet in 32 or 64-bit versions for Windows, OS X, Linux or Solaris. For further details on the Tablet, contact tablet@hutton.ac.uk.

Comparative visualization of genetic and physical maps with Strudel.

UNLABELLED: Data visualization can play a key role in comparative genomics, for example, underpinning the investigation of conserved synteny patterns. Strudel is a desktop application that allows users to easily compare both genetic and physical maps interactively and efficiently. It can handle large datasets from several genomes simultaneously, and allows all-by-all comparisons between these. AVAILABILITY AND IMPLEMENTATION: Installers for Strudel are available for Windows, Linux, Solaris and Mac OS X at http://bioinf.scri.ac.uk/strudel/.