SUMMARY: Marker assisted breeding (MAB) supports breeding by identifying individuals or molecular markers associated with important traits. MAB methods include genome-wide association studies (GWAS) and genomic selection (GS). Local haplotypes are regions of DNA that are inherited together due to high levels of linkage disequilibrium. Local haplotypes can improve the prediction accuracy and power of GS and GWAS. Currently available local haplotyping tools improve GWAS power through fine-mapping of candidate regions or through haplotype-based GWAS. However, no local haplotyping tools utilize the benefits of haplotypes for GS. Here we present HaploVar, a local haplotyping tool designed to improve both GWAS and GS pipelines by identifying local haplotypes and formatting the output to be compatible with all major GWAS and GS tools. HaploVar can be used in any haplotype-based MAB study. AVAILABILITY AND IMPLEMENTATION: HaploVar can be downloaded from CRAN with R > 4.0.0 (DOI: 10.32614/CRAN.package.HaploVar). HaploVar and a tutorial vignette is available on GitHub (https://github.com/TessaMacNish/HaploVar). HaploVar is available under an MIT license. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

SUMMARY: Genome-wide association studies (GWAS) excels at harnessing dense genomic variant datasets to identify candidate regions responsible for producing a given phenotype. However, GWAS and traditional fine-mapping methods do not provide insight into the complex local landscape of linkage that contains and has been shaped by the causal variant(s). Here, we present crosshap, an R package that performs robust density-based clustering of variants based on their linkage profiles to capture haplotype structures in a local genomic region of interest. Following this, crosshap is equipped with visualization tools for choosing optimal clustering parameters (ɛ) before producing an intuitive figure that provides an overview of the complex relationships between linked variants, haplotype combinations, phenotype, and metadata traits. AVAILABILITY AND IMPLEMENTATION: The crosshap package is freely available under the MIT license and can be downloaded directly from CRAN with R >4.0.0. The development version is available on GitHub alongside issue support (https://github.com/jacobimarsh/crosshap). Tutorial vignettes and documentation are available (https://jacobimarsh.github.io/crosshap/).

Improving productivity of the staple crops wheat and rice is essential to feed the growing global population, particularly in the context of a changing climate. However, current rates of yield gain are insufficient to support the predicted population growth. New approaches are required to accelerate the breeding process, and many of these are driven by the application of large-scale crop data. To leverage the substantial volumes and types of data that can be applied for precision breeding, the wheat and rice research communities are working towards the development of integrated systems to access and standardize the dispersed, heterogeneous available data. Here, we outline the initiatives of the International Wheat Information System (WheatIS) and the International Rice Informatics Consortium (IRIC) to establish Web-based single-access systems and data mining tools to make the available resources more accessible, drive discovery and accelerate the production of new crop varieties. We discuss the progress of WheatIS and IRIC towards unifying specialized wheat and rice databases and building custom software platforms to manage and interrogate these data. Single-access crop information systems will strengthen scientific collaboration, optimize the use of public research funds and help achieve the required yield gains in the two most important global food crops.

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.

SUMMARY: We developed runBNG, an open-source software package which wraps BioNano genomic analysis tools into a single script that can be run on the command line. runBNG can complete analyses, including quality control of single molecule maps, optical map de novo assembly, comparisons between different optical maps, super-scaffolding and structural variation detection. Compared to existing software BioNano IrysView and the KSU scripts, the major advantages of runBNG are that the whole pipeline runs on one single platform and it has a high customizability. AVAILABILITY AND IMPLEMENTATION: runBNG is written in bash, with the requirement of BioNano IrysSolve packages, GCC, Perl and Python software. It is freely available at https://github.com/appliedbioinformatics/runBNG. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Reference genome assemblies are valuable, as they provide insights into gene content, genetic evolution and domestication. The higher the quality of a reference genome assembly the more accurate the downstream analysis will be. During the last few years, major efforts have been made towards improving the quality of genome assemblies. However, erroneous and incomplete assemblies are still common. Complementary to DNA sequencing technologies, optical mapping has advanced genomic studies by facilitating the production of genome scaffolds and assessing structural variation. However, there are few tools available to comprehensively examine misassemblies in reference genome sequences using optical map data. RESULTS: We present BioNanoAnalyst, a software package to examine genome assemblies based on restriction endonuclease cut sites and optical map data. A graphical user interface (GUI) allows users to assess reference genome sequences on different computer platforms without the requirement of programming knowledge. The zoom function makes visualisation convenient, while a GFF3 format output file gives an option to directly visualise questionable assembly regions by location and nucleotides following import into a local genome browser. CONCLUSIONS: BioNanoAnalyst is a tool to identify misassemblies in a reference genome sequence using optical map data. With the reported information, users can rapidly identify assembly errors and correct them using other software tools, which could facilitate an accurate downstream analysis.

Many computing students do not receive adequate training in software quality management. Some students do not have the opportunity to practice software process improvement activities even if they do see the topics covered in their course lectures and textbooks. Serious games are gaining popularity as a means of instruction in higher education. Some excellent prescriptive software process simulation games have been created, as well as a few software engineering drill and practice games. In general, these games do not allow students to create agile process models or experiment with process improvement strategies. We are creating a serious game that will serve as a virtual learning environment to allow students to explore agile process improvement practices. Our game is designed as a single-player game where the player takes the role of software team leader and plays against an AI (artificial intelligence) opponent representing the customer's interests and needs. Players are rewarded for developing project strategies that allow for completion of projects on time, within budget, and meet the necessary software quality requirements. It is our intention to create a game with sufficiently detailed instructions to allow instructors to introduce hands on practice with agile process improvement activities without requiring additional class time.

BACKGROUND: Measuring the extent of shared ancestry between individuals or organisms is important in many fields, including forensic science, conservation genetics and animal breeding. The traditional approach is to calculate the expected degree of relatedness between individuals in a pedigree. This assumes that the founders of the pedigree are non-inbred and unrelated to each other, which is rarely the case. In contrast, molecular data allow measurement of actual relatedness without knowledge of a pedigree. Methods to do this have been proposed, but generally do not take the lengths of the genomic regions shared between individuals into account. RESULTS: Two measures based on the extent of haplotype sharing between genomes are proposed. The intercept measure B estimates the fraction of shared genome between individuals, and the product measure C is closely related to the numerator relationship matrix A. Both are based on a model for the joint distribution of markers at the haplotype level. The two measures are compared to the pedigree-based measure A and to vanRaden's G, a frequently used molecular measure, using a set of data comprising 5037 dairy cattle. The comparison criteria include the ability to capture genealogical relatedness and the prediction accuracy obtained when used in genomic prediction. Both B and C explain around 95 % of the variation in A, whereas G explains around 6 %. G captures genealogical relatedness poorly, particularly for distantly related individuals (second cousins or farther). Both B and C tend to be larger than A but this can be ascribed to the assumption of non-inbred unrelated founders. Using C in linear mixed models results in slightly higher prediction accuracy than G, and using B results in slightly lower prediction accuracy. CONCLUSIONS: The two proposed measures of relatedness capture genealogical relatedness well, outperforming vanRaden's G in this respect. When used in genomic prediction models, the product measure leads to slightly improved prediction accuracy.

BACKGROUND: Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. RESULTS: An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. CONCLUSIONS: The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis.

The objective of this study was to assess the monotonic mental workload under changing conditions of operator fatigue during a night time driver simulation study. Several cardiovascular measures were used in order to differentiate between driving and a continuous tracking task. From all of the standard cardiovascular measures, heart rate in beats per minute emerged as the most sensitive for workload discrimination. Heart rate was higher during driving than during the tracking task, pointing to a slightly higher demanding workload for the driving task. This result was stable over the course of the night and showed only a minimal fatigue influence. Heart rate variability in milliseconds, on the other hand, was on average higher for the continuous tracking task in comparison to the driving. This was especially the case for the sessions with high subjective sleepiness. It can thus be concluded that the fatigue state of the operator was more impaired during the tracking task than during driving.

BACKGROUND: Although genome-scale expression experiments are performed routinely in biomedical research, methods of analysis remain simplistic and their interpretation challenging. The conventional approach is to compare the expression of each gene, one at a time, between treatment groups. This implicitly treats the gene expression levels as independent, but they are in fact highly interdependent, and exploiting this enables substantial power gains to be realized. RESULTS: We assume that information on the dependence structure between the expression levels of a set of genes is available in the form of a Bayesian network (directed acyclic graph), derived from external resources. We show how to analyze gene expression data conditional on this network. Genes whose expression is directly affected by treatment may be identified using tests for the independence of each gene and treatment, conditional on the parents of the gene in the network. We apply this approach to two datasets: one from a hepatotoxicity study in rats using a PPAR pathway, and the other from a study of the effects of smoking on the epithelial transcriptome, using a global transcription factor network. CONCLUSIONS: The proposed method is straightforward, simple to implement, gives rise to substantial power gains, and may assist in relating the experimental results to the underlying biology.

UNLABELLED: Genetic linkage mapping enables the study of genome organization and the association of heritable traits with regions of sequenced genomes. Comparative genetic mapping is particularly powerful as it allows translation of information between related genomes and gives an insight into genome evolution. A common tool for the storage, comparison and visualization of genetic maps is CMap. However, current visualization in CMap is limited to the comparison of adjacent aligned maps. To overcome this limitation, we have developed CMap3D, a tool to compare multiple genetic maps in three-dimensional space. CMap3D is based on a client/server model ensuring operability with current CMap data repositories. This tool can be applied to any species where genetic map information is available and enables rapid, direct comparison between multiple aligned maps. AVAILABILITY AND IMPLEMENTATION: The software is a stand-alone application written in Processing and Java. Binaries are available for Windows, OSX and Linux, and require Sun Microsystems Java Runtime Environment 1.6 or later. The software is freely available for non-commercial use from http://flora.acpfg.com.au/.

BACKGROUND: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example Kruskal's algorithm. The efficiency of the algorithm makes it tractable for high-dimensional problems. RESULTS: We extend Chow and Liu's approach in two ways: first, to find the forest optimizing a penalized likelihood criterion, for example AIC or BIC, and second, to handle data with both discrete and Gaussian variables. We apply the approach to three datasets: two from gene expression studies and the third from a genetics of gene expression study. The minimal BIC forest supplements a conventional analysis of differential expression by providing a tentative network for the differentially expressed genes. In the genetics of gene expression context the method identifies a network approximating the joint distribution of the DNA markers and the gene expression levels. CONCLUSIONS: The approach is generally useful as a preliminary step towards understanding the overall dependence structure of high-dimensional discrete and/or continuous data. Trees and forests are unrealistically simple models for biological systems, but can provide useful insights. Uses include the following: identification of distinct connected components, which can be analysed separately (dimension reduction); identification of neighbourhoods for more detailed analyses; as initial models for search algorithms with a larger search space, for example decomposable models or Bayesian networks; and identification of interesting features, such as hub nodes.

The ability to sequence the DNA of an organism has become one of the most important tools in modern biological research. Until recently, the sequencing of even small model genomes required substantial funds and international collaboration. The development of 'second-generation' sequencing technology has increased the throughput and reduced the cost of sequence generation by several orders of magnitude. These new methods produce vast numbers of relatively short reads, usually at the expense of read accuracy. Since the first commercial second-generation sequencing system was produced by 454 Technologies and commercialised by Roche, several other companies including Illumina, Applied Biosystems, Helicos Biosciences and Pacific Biosciences have joined the competition. Because of the relatively high error rate and lack of assembly tools, short-read sequence technology has mainly been applied to the re-sequencing of genomes. However, some recent applications have focused on the de novo assembly of these data. De novo assembly remains the greatest challenge for DNA sequencing and there are specific problems for second generation sequencing which produces short reads with a high error rate. However, a number of different approaches for short-read assembly have been proposed and some have been implemented in working software. In this review, we compare the current approaches for second-generation genome sequencing, explore the future direction of this technology and the implications for plant genome research.

Much progress has been made toward interactive ray tracing, but most research has focused specifically on ray casting. A common approach is to use "packets" of rays to amortize cost across sets of rays. Whether "packets" can be used to speed up the cost of reflection and refraction rays is unclear. The issue is complicated since such rays do not share common origins and often have less directional coherence than viewing and shadow rays. Since the primary advantage of ray tracing over rasterization is the computation of global effects, such as accurate reflection and refraction, this lack of knowledge should be corrected. We are also interested in exploring whether distribution ray tracing, due to its stochastic properties, further erodes the effectiveness of techniques used to accelerate ray casting. This paper addresses the question of whether packet-based ray tracing algorithms can be effectively used for more than visibility computation. We show that by choosing an appropriate data structure and a suitable packet assembly algorithm we can extend the idea of "packets" from ray casting to Whitted-style and distribution ray tracing, while maintaining efficiency.

We present a mathematical framework for enforcing energy conservation in a bidirectional reflectance distribution function (BRDF) by specifying halfway vector distributions in simple two-dimensional domains. Energy-conserving BRDFs can produce plausible rendered images with accurate reflectance behavior, especially near grazing angles. Using our framework, we create an empirical BRDF that allows easy specification of diffuse, specular, and retroreflective materials. We also present a second BRDF model that is useful for data fitting; although it does not preserve energy, it uses the same halfway vector domain as the first model. We show that this data-fitting BRDF can be used to match measured data extremely well using only a small set of parameters. We believe that this is an improvement over table-based lookups and factored versions of BRDF data.

UNLABELLED: AutoSNP is a program to detect single nucleotide polymorphisms (SNPs) and insertion/deletion polymorphisms (indels) in expressed sequence tag (EST) data. The program uses d2cluster and cap3 to cluster and align EST sequences, and uses redundancy to differentiate between candidate SNPs and sequence errors. Candidate polymorphisms are identified as occurring in multiple reads within an alignment. For each candidate SNP, two measures of confidence are calculated, the redundancy of the polymorphism at a SNP locus and the co segregation of the candidate SNP with other SNPs in the alignment. AVAILABILITY: The program was written in PERL and is freely available to non-commercial users by request from the authors.

MOTIVATION: Data from one-channel cDNA microarray studies may exhibit poor reproducibility due to spatial heterogeneity, non-linear array-to-array variation and problems in correcting for background. Uncorrected, these phenomena can give rise to misleading conclusions. RESULTS: Spatial heterogeneity may be corrected using two-dimensional loess smoothing (Colantuoni et al., 2002). Non-linear between-array variation may be corrected using an iterative application of one-dimensional loess smoothing. A method for background correction using a smoothing function rather than simple subtraction is described. These techniques promote within-array spatial uniformity and between-array reproducibility. Their application is illustrated using data from a study of the effects of an insulin sensitizer, rosiglitazone, on gene expression in white adipose tissue in diabetic db/db mice. They may also be useful with data from two-channel cDNA microarrays and from oligonucleotide arrays. AVAILABILITY: R functions for the methods described are available on request from the author.