Gary L. A. Barker

Analysis of the bread wheat genome using whole-genome shotgun sequencing

Bread wheat (Triticum aestivum) is a globally important crop, accounting for 20 per cent of the calories consumed by humans. Major efforts are underway worldwide to increase wheat production by extending genetic diversity and analysing key traits, and genomic resources can accelerate progress. But so far the very large size and polyploid complexity of the bread wheat genome have been substantial barriers to genome analysis. Here we report the sequencing of its large, 17-gigabase-pair, hexaploid genome using 454 pyrosequencing, and comparison of this with the sequences of diploid ancestral and progenitor genomes. We identified between 94,000 and 96,000 genes, and assigned two-thirds to the three component genomes (A, B and D) of hexaploid wheat. High-resolution synteny maps identified many small disruptions to conserved gene order. We show that the hexaploid genome is highly dynamic, with significant loss of gene family members on polyploidization and domestication, and an abundance of gene fragments. Several classes of genes involved in energy harvesting, metabolism and growth are among expanded gene families that could be associated with crop productivity. Our analyses, coupled with the identification of extensive genetic variation, provide a resource for accelerating gene discovery and improving this major crop.

Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array

High-density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker–trait associations in mapping experiments. We developed a genotyping array including about 90 000 gene-associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome-wide distributed SNPs that are represented in populations of diverse geographical origin. We used density-based spatial clustering algorithms to enable high-throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model-free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low-intensity clusters can provide insight into the distribution of presence–absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat.

Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models

The rate at which nonsynonymous single nucleotide polymorphisms (nsSNPs) are being identified in the human genome is increasing dramatically owing to advances in whole‐genome/whole‐exome sequencing technologies. Automated methods capable of accurately and reliably distinguishing between pathogenic and functionally neutral nsSNPs are therefore assuming ever‐increasing importance. Here, we describe the Functional Analysis Through Hidden Markov Models (FATHMM) software and server: a species‐independent method with optional species‐specific weightings for the prediction of the functional effects of protein missense variants. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two separate benchmarks. Furthermore, in one benchmark, we achieve performance accuracies that outperform current state‐of‐the‐art prediction methods (i.e., SNPs&GO and MutPred). We demonstrate that FATHMM can be efficiently applied to high‐throughput/large‐scale human and nonhuman genome sequencing projects with the added benefit of phenotypic outcome associations. To illustrate this, we evaluated nsSNPs in wheat (Triticum spp.) to identify some of the important genetic variants responsible for the phenotypic differences introduced by intense selection during domestication. A Web‐based implementation of FATHMM, including a high‐throughput batch facility and a downloadable standalone package, is available at http://fathmm.biocompute.org.uk.

Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces

The application of DNA barcoding to dietary studies allows prey taxa to be identified in the absence of morphological evidence and permits a greater resolution of prey identity than is possible through direct examination of faecal material. For insectivorous bats, which typically eat a great diversity of prey and which chew and digest their prey thoroughly, DNA‐based approaches to diet analysis may provide the only means of assessing the range and diversity of prey within faeces. Here, we investigated the effectiveness of DNA barcoding in determining the diets of bat species that specialize in eating different taxa of arthropod prey. We designed and tested a novel taxon‐specific primer set and examined the performance of short barcode sequences in resolving prey species. We recovered prey DNA from all faecal samples and subsequent cloning and sequencing of PCR products, followed by a comparison of sequences to a reference database, provided species‐level identifications for 149/207 (72%) clones. We detected a phylogenetically broad range of prey while completely avoiding detection of nontarget groups. In total, 37 unique prey taxa were identified from 15 faecal samples. A comparison of DNA data with parallel morphological analyses revealed a close correlation between the two methods. However, the sensitivity and taxonomic resolution of the DNA method were far superior. The methodology developed here provides new opportunities for the study of bat diets and will be of great benefit to the conservation of these ecologically important predators.

Assessing the use of the mitochondrial cox1 marker for use in DNA barcoding of red algae (Rhodophyta)

The red algae, a remarkably diverse group of organisms, are difficult to identify using morphology alone. Following the proposal to use the mitochondrial cytochrome c oxidase subunit I (cox1) for DNA barcoding animals, we assessed the use of this gene in the identification of red algae using 48 samples plus 31 sequences obtained from GenBank. The data set spanned six orders of red algae: the Bangiales, Ceramiales, Corallinales, Gigartinales, Gracilariales and Rhodymeniales. The results indicated that species could be discriminated. Intraspecific variation was between 0 and 4 bp over 539 bp analyzed except in Mastocarpus stellatus (0-14 bp) and Gracilaria gracilis (0-11 bp). Cryptic diversity was found in Bangia fuscopurpurea, Corallina officinalis, G. gracilis, M. stellatus, Porphyra leucosticta and P. umbilicalis. Interspecific variation across all taxa was between 28 and 148 bp, except for G. gracilis and M. stellatus. A comparison of cox1 with the plastid Rubisco spacer for Porphyra species revealed that it was a more sensitive marker in revealing incipient speciation and cryptic diversity. The cox1 gene has the potential to be used for DNA barcoding of red algae, although a good taxonomic foundation coupled with extensive sampling of taxa is essential for the development of an effective identification system.

Redundancy based detection of sequence polymorphisms in expressed sequence tag data using autoSNP

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.

Development of anonymous cDNA microarrays to study changes to the Senecio floral transcriptome during hybrid speciation

Interspecific hybridization is an important process through which abrupt speciation can occur. In recent years, genetic changes associated with hybrid speciation have been identified through a variety of techniques, including AFLP/SSR mapping, GISH/FISH and cDNA‐AFLP differential display. However, progress in using microarray technology to analyse whole genome/transcriptome changes associated with hybrid speciation has been limited due to the lack of extensive sequence data for many hybrid species and the difficulties in extrapolating results from commercially available microarrays for model species onto nonmodel hybrid taxa. Increasingly therefore researchers studying nonmodel systems are turning to the development of ‘anonymous’ cDNA microarrays, where the time and cost of producing microarrays is reduced by printing unsequenced cDNA clones, and sequencing only those clones that display interesting expression patterns. Here we describe the creation, testing and preliminary use of anonymous cDNA microarrays to study changes in floral transcriptome associated with allopolyploid speciation in the genus Senecio. We report a comparison of gene expression between the allohexaploid hybrid, Senecio cambrensis, its parental taxa Senecio squalidus (diploid) and Senecio vulgaris (tetraploid), and the intermediate triploid (sterile) hybrid Senecio×baxteri. Anonymous microarray analysis revealed dramatic differences in floral gene expression between these four taxa and demonstrates the power of this technique for studies of the genetic impact of hybridization in nonmodel flowering plants.

Genetic characterisation of Emiliania huxleyi (Haptophyta)

Amongst the coccolithophorids, Emiliania huxleyi is the most successful and can form large scale blooms under a variety of environmental conditions. This implies extensive genetic variation within this taxon. Physiological, morphological and antigenic differences between clonal isolates support this suggestion. Our investigations into the level of genetic variation within the morphological species concept of E. huxleyi indicate that it is such a young taxon that sequence comparisons of both coding and non-coding regions cannot resolve the issue of how many separate taxonomic entities are involved. However, PCR-based genetic fingerprinting techniques do reveal extensive genetic diversity, both on a global scale and within major bloom populations in both space and time. Cell DNA content can also separate cells with morphotype A coccoliths from those with morphotype B coccoliths. Taken together with physiological and morphological evidence, these data suggest that the morphotypes of E. huxleyi should be separated at the variety level. We have used both nuclear and plastid rRNA sequence comparisons to confirm the place of E. huxleyi within the Haptophyta.

CerealsDB 2.0: an integrated resource for plant breeders and scientists.

BackgroundFood security is an issue that has come under renewed scrutiny amidst concerns that substantial yield increases in cereal crops are required to feed the world’s booming population. Wheat is of fundamental importance in this regard being one of the three most important crops for both human consumption and livestock feed; however, increase in crop yields have not kept pace with the demands of a growing world population. In order to address this issue, plant breeders require new molecular tools to help them identify genes for important agronomic traits that can be introduced into elite varieties. Studies of the genome using next-generation sequencing enable the identification of molecular markers such as single nucleotide polymorphisms that may be used by breeders to identify and follow genes when breeding new varieties. The development and application of next-generation sequencing technologies has made the characterisation of SNP markers in wheat relatively cheap and straightforward. There is a growing need for the widespread dissemination of this information to plant breeders.DescriptionCerealsDB is an online resource containing a range of genomic datasets for wheat (Triticum aestivum) that will assist plant breeders and scientists to select the most appropriate markers for marker assisted selection. CerealsDB includes a database which currently contains in excess of 100,000 putative varietal SNPs, of which several thousand have been experimentally validated. In addition, CerealsDB contains databases for DArT markers and EST sequences, and links to a draft genome sequence for the wheat variety Chinese Spring.ConclusionCerealsDB is an open access website that is rapidly becoming an invaluable resource within the wheat research and plant breeding communities.

De novo derivation of proteomes from transcriptomes for transcript and protein identification

Identification of proteins by tandem mass spectrometry requires a reference protein database, but these are only available for model species. Here we demonstrate that, for a non-model species, the sequencing of expressed mRNA can generate a protein database for mass spectrometry–based identification. This combination of high-throughput sequencing and protein identification technologies allows detection of genes and proteins. We use human cells infected with human adenovirus as a complex and dynamic model to demonstrate the robustness of this approach. Our proteomics informed by transcriptomics (PIT) technique identifies >99% of over 3,700 distinct proteins identified using traditional analysis that relies on comprehensive human and adenovirus protein lists. We show that this approach can also be used to highlight genes and proteins undergoing dynamic changes in post-transcriptional protein stability.