Gordon Stephen

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.

Flapjack--graphical genotype visualization.

SUMMARY New software tools for graphical genotyping are required that can routinely handle the large data volumes generated by the high-throughput single-nucleotide polymorphism (SNP) platforms, genotyping-by-sequencing and other comparable genotyping technologies. Flapjack has been developed to facilitate analysis of these data, providing real time rendering with rapid navigation and comparisons between lines, markers and chromosomes, with visualization, sorting and querying based on associated data, such as phenotypes, quantitative trait loci or other mappable features. AVAILABILITY Flapjack is freely available for Microsoft Windows, Mac OS X, Linux and Solaris, and can be downloaded from http://bioinf.scri.ac.uk/flapjack .

Comparative visualization of genetic and physical maps with Strudel

Summary: 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/. Contact: strudel@scri.ac.uk; micha.bayer@scri.ac.uk

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 barleys genes.

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

BackgroundSingle 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.ResultsThe 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.ConclusionsThe 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.

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 Tablets ability to visually highlight various situations of interest which can arise in NGS data.

BLASTmap: A Shiny-Based Application to Visualize BLAST Results as Interactive Heat Maps and a Tool to Design Gene-Specific Baits for Bespoke Target Enrichment Sequencing

Numerous genes that determine the outcome of plant-pathogen interactions are currently being discovered and include, for example, immune receptors, susceptibility factors and pathogen effectors and their host targets. Target enrichment sequencing provides a means to preferentially resequence these genes of interest without the need to first generate a genotype-specific genome assembly. The Basic Local Alignment Search Tool (BLAST), in combination with the here developed BLASTmap, can be used to design probes that specifically target such gene(s), either by using the target species or the closest related genus as a reference. BLAST is a ubiquitous tool in biological sequence analysis and a multitude of programs are available for the visualization of BLAST alignments. However, there are currently no dedicated programs for visual comparison of large-scale BLAST output attributes such as bit score. The need to quickly and efficiently compare many thousands of BLAST results led to the development of BLASTmap, an interactive web application created using the Shiny R package, customized for clustering and viewing BLAST results as an interactive heat map. Here we show an example of how BLASTmap was successfully applied to analyze custom DNA/RNA probe sequences and to visually determine that four probes are sufficient for the specific yet inclusive enrichment of the potato R2 disease resistance gene family.