Chris Duran

Geneious Basic

Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data. Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl. Contact: peter@biomatters.com

Discovering genetic polymorphisms in next-generation sequencing data.

The ongoing revolution in DNA sequencing technology now enables the reading of thousands of millions of nucleotide bases in a single instrument run. However, this data quantity is often compromised by poor confidence in the read quality. The identification of genetic polymorphisms from this data is therefore problematic and, combined with the vast quantity of data, poses a major bioinformatics challenge. However, once these difficulties have been addressed, next-generation sequencing will offer a means to identify and characterize the wealth of genetic polymorphisms underlying the vast phenotypic variation in biological systems. We describe the recent advances in next-generation sequencing technology, together with preliminary approaches that can be applied for single nucleotide polymorphism discovery in plant species.

Molecular Genetic Markers: Discovery, Applications, Data Storage and Visualisation

Molecular genetic markers represent one of the most powerful tools for the analysis of genomes and enable the association of heritable traits with underlying genomic variation. Molecular marker technology has developed rapidly over the last decade and two forms of sequence based marker, Simple Sequence Repeats (SSRs), also known as microsatellites, and Single Nucleotide Polymorphisms (SNPs) now predominate applications in modern genetic analysis. The reducing cost of DNA sequencing has led to the availability of large sequence data sets derived from whole genome sequencing and large scale Expressed Sequence Tag (EST) discovery that enable the mining of SSRs and SNPs, which may then be applied to diversity analysis, genetic trait mapping, association studies, and marker assisted selection. These markers are inexpensive, require minimal labour to produce and can frequently be associated with annotated genes. Here we review automated methods for the discovery of SSRs and SNPs and provide an overview of the diverse applications of these markers.

Single nucleotide polymorphism discovery from wheat next-generation sequence data.

Single nucleotide polymorphisms (SNPs) are the most abundant type of molecular genetic marker and can be used for producing high-resolution genetic maps, marker-trait association studies and marker-assisted breeding. Large polyploid genomes such as wheat present a challenge for SNP discovery because of the potential presence of multiple homoeologs for each gene. AutoSNPdb has been successfully applied to identify SNPs from Sanger sequence data for several species, including barley, rice and Brassica, but the volume of data required to accurately call SNPs in the complex genome of wheat has prevented its application to this important crop. DNA sequencing technology has been revolutionized by the introduction of next-generation sequencing, and it is now possible to generate several million sequence reads in a timely and cost-effective manner. We have produced wheat transcriptome sequence data using 454 sequencing technology and applied this for SNP discovery using a modified autoSNPdb method, which integrates SNP and gene annotation information with a graphical viewer. A total of 4,694,141 sequence reads from three bread wheat varieties were assembled to identify a total of 38 928 candidate SNPs. Each SNP is within an assembly complete with annotation, enabling the selection of polymorphism within genes of interest.

Future tools for association mapping in crop plants.

Association mapping currently relies on the identification of genetic markers. Several technologies have been adopted for genetic marker analysis, with single nucleotide polymorphisms (SNPs) being the most popular where a reasonable quantity of genome sequence data are available. We describe several tools we have developed for the discovery, annotation, and visualization of molecular markers for association mapping. These include autoSNPdb for SNP discovery from assembled sequence data; TAGdb for the identification of gene specific paired read Illumina GAII data; CMap3D for the comparison of mapped genetic and physical markers; and BAC and Gene Annotator for the online annotation of genes and genomic sequences.

Targeted identification of genomic regions using TAGdb

BackgroundThe introduction of second generation sequencing technology has enabled the cost effective sequencing of genomes and the identification of large numbers of genes and gene promoters. However, the assembly of DNA sequences to create a representation of the complete genome sequence remains costly, especially for the larger and more complex plant genomes.ResultsWe have developed an online database, TAGdb, that enables researchers to identify paired read sequences that share identity with a submitted query sequence. These tags can be used to design oligonucleotide primers for the PCR amplification of the region in the target genome.ConclusionsThe ability to produce large numbers of paired read genome tags using second generation sequencing provides a cost effective method for the identification of genes and promoters in large, complex or orphan species without the need for whole genome assembly.

Genetic Maps and the Use of Synteny

Genetic linkage maps represent the order of known molecular genetic markers along a given chromosome for a given species. This provides an insight into the organisation of a plant genome. In comparative genomics, synteny is the preserved order of genes on chromosomes of related species which results from descent from a common ancestor. Comparative mapping is a valuable technique to identify similarities and differences between species and enables the transfer of information from one map to another and assists in the reconstruction of ancestral genomes. This chapter demonstrates the application of online resources to identify candidate genes underlying a QTL, conduct genome comparisons, identify syntenic regions and view comparative genetic maps in grass and Brassica species.

CMap3D: a 3D visualization tool for comparative genetic maps

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/.

Molecular Marker Discovery and Genetic Map Visualisation

The bulk of variation at the nucleotide level is often not visible at the phenotypic level. However, this variation can be exploited using molecular genetic marker systems. Molecular genetic markers represent one of the most powerful tools for genome analysis and permit the association of heritable traits with underlying genomic variation. Molecular marker technology has developed rapidly over the last decade, with the development of high-throughput genotyping methods and the availability of large amounts of sequence data for automated marker discovery. Two forms of sequence based marker, Simple Sequence Repeats (SSRs), also known as microsatellites, and Single Nucleotide Polymorphisms (SNPs) are the principal markers currently applied in modern genetic analysis. This are supplemented with anonymous marker systems such as Amplified Fragment Length Polymorphisms (AFLPs; Vos et al. 1995), and Diversity Array Technology (DArT; Jaccoud et al. 2001). The reducing cost of DNA sequencing has led to the availability of large sequence data sets that enable the mining of sequence based markers, such as SSRs and SNPs, which may then be applied to diversity analysis, genetic trait mapping, association studies, and marker assisted selection.

Predicting polymorphic EST-SSRs in silico

The public availability of large quantities of gene sequence data provides a valuable resource of the mining of Simple Sequence Repeat (SSR) molecular genetic markers for genetic analysis. These markers are inexpensive, require minimal labour to produce and can frequently be associated with functionally annotated genes. This study presents the characterization of barley EST‐SSRs and the identification of putative polymorphic SSRs from EST data. Polymorphic SSRs are distinguished from monomorphic SSRs by the representation of varying motif lengths within an alignment of sequence reads. Two measures of confidence are calculated, redundancy of a polymorphism and co‐segregation with accessions. The utility of this method is demonstrated through the discovery of 597 candidate polymorphic SSRs, from a total of 452 642 consensus expressed sequences. PCR amplification primers were designed for the identified SSRs. Ten primer pairs were validated for polymorphism in barley and for transferability across species. Analysis of the polymorphisms in relation to SSR motif, length, position and annotation is discussed.