Caledonian hot zone magmatism in the “Newer Granites”: insight from the Cluanie and Clunes plutons, Northern Scottish Highlands

Scottish “Newer” Granites record the evolution of the Caledonides resulting from Iapetus subduction and slab breakoff during the Silurian-Devonian Scandian Orogeny, but relationships between geodynamics, petrogenesis and emplacement are incomplete. Laser ablation U-Pb results from magmatic zircons at the Cluanie Pluton (Northern Highlands) identify clusters of concordant Silurian data points. A cluster with a weighted mean 206Pb/238U age of 431.6 ± 1.3 Ma (2 confidence interval, n = 6) records emplacement whilst older points (clustered at 441.8 ± 2.3 Ma, n = 9) record deep crustal hot zone magmatism prior to ascent. The Cluanie Pluton, and its neighbour the ∼428 Ma Clunes tonalite, have adakite-like high Na, Sr/Y, La/Yb and low Mg, Ni and Cr characteristics, and lack mafic facies common in other “Newer Granites”. These geochemical signatures indicate the tapping of batches of homogenised, evolved magma from the deeper crust. The emplacement age of the Cluanie Pluton confirms volumetrically modest subduction-related magmatism occurred beneath the Northern Highlands before slab breakoff, probably as a result of crustal thickening during the ∼450 Ma Grampian 2 event. Extensive new in-situ geochemical-geochronological studies for this terrane may further substantiate the deep crustal hot zone model and the association between Caledonian magmatism and potentially metallogenesis. The term “Newer Granites” is outdated as it ignores the demonstrated relationships between magmatism, Scandian orogenesis and slab breakoff. Hence, “Caledonian intrusions” would be a more appropriate generic term to cover those bodies related to either Iapetus subduction or to slab breakoff

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

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

Helium: visualization of large scale plant pedigrees.

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. 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. 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

TetraploidSNPMap: Software for Linkage Analysis and QTL Mapping in Autotetraploid Populations Using SNP Dosage Data

An earlier software application of ours, TetraploidMap for Windows, enabled linkage analysis and quantitative trait locus interval mapping to be carried out in an experimental cross of an autotetraploid species, using both dominant markers such as amplified fragment length polymorphisms and codominant markers such as simple sequence repeats. The size was limited to 800 markers, and quantitative trait locus mapping was conducted for each parent separately due to the difficulties in obtaining a reliable consensus map for the 2 parents. Modern genotyping technologies now give rise to datasets of thousands of single nucleotide polymorphisms, and these can be scored in autotetraploid species as single nucleotide polymorphism dosages, distinguishing among the heterozygotes AAAB, AABB, and ABBB, rather than simply using the presence or absence of an allele. The dosage data is more informative about recombination and leads to higher density linkage maps. The current program, TetraploidSNPMap, makes full use of the dosage data, and has new facilities for displaying the clustering of single nucleotide polymorphisms, rapid ordering of large numbers of single nucleotide polymorphisms using a multidimensional scaling analysis, and phase calling. It also has new routines for quantitative trait locus mapping based on a hidden Markov model, which use the dosage data to model the effects of alleles from both parents simultaneously. A Windows-based interface facilitates data entry and exploration. It is distributed with a detailed user guide. TetraploidSNPMap is freely available from our GitHub repository

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

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.</p