Radiation Hybrid Map of Barley Chromosome 3H.

Assembly of the barley (Hordeum vulgare L.) genome is complicated by its large size (5.1 Gb) and proportion of repetitive elements (84%). This process is facilitated by high resolution maps for aligning bacterial artificial chromosome (BAC) contigs along chromosomes. Available genetic maps, however, do not provide accurate information on the physical position of a large portion of the genome located in recombination-poor regions. Radiation hybrid (RH) mapping is an alternative approach, which is based on radiation-induced deletions along the length of chromosomes. In this study, the first RH map for barley chromosome 3H was developed. In total, 373 in vivo RH lines were generated by irradiating wheat (Triticum aestivum L.)-barley chromosome 3H addition lines and crossing them to a normal wheat cultivar. Each RH informative line (containing deletions) had, on average, three deletions. The induced deletion size varied from 36.58 Kb to 576.00 Mb, with an average length of 52.42 Mb. This initial chromosome 3H radiation hybrid (3H-RH) map had a 9.53× higher resolution than an analogous genetic map, reaching a maximum of >262.40× resolution in regions around the centromere. The final RH map was 3066.1 cR in length, with a 0.76 Mb resolution. It was estimated that the map resolution can be improved to an average of 30.34 Kb by saturating the 3H-RH map with molecular markers. The generated RH panel enabled alignment of BAC and sequenced contigs as small as 1.50 Kb in size. The high resolution and the coverage of poor-recombination regions make RH maps an ideal resource for barley genome assembly, as well as other genetic studies.

Reliable Radiation Hybrid Maps: An Efficient Scalable Clustering-Based Approach.

The process of mapping markers from radiation hybrid mapping (RHM) experiments is equivalent to the traveling salesman problem and, thereby, has combinatorial complexity. As an additional problem, experiments typically result in some unreliable markers that reduce the overall quality of the map. We propose a clustering approach for addressing both problems efficiently by eliminating unreliable markers without the need for mapping the complete set of markers. Traditional approaches for eliminating markers use resampling of the full data set, which has an even higher computational complexity than the original mapping problem. In contrast, the proposed approach uses a divide-and-conquer strategy to construct framework maps based on clusters that exclude unreliable markers. Clusters are ordered using parallel processing and are then combined to form the complete map. We present three algorithms that explore the trade-off between the number of markers included in the map and placement accuracy. Using an RHM data set of the human genome, we compare the framework maps from our proposed approaches with published physical maps and with the results of using the Carthagene tool. Overall, our approaches have a very low computational complexity and produce solid framework maps with good chromosome coverage and high agreement with the physical map marker order.

A radiation hybrid map of chromosome 1D reveals synteny conservation at a wheat speciation locus.

The species cytoplasm specific (scs) genes affect nuclear-cytoplasmic interactions in interspecific hybrids. A radiation hybrid (RH) mapping population of 188 individuals was employed to refine the location of the scs (ae) locus on Triticum aestivum chromosome 1D. "Wheat Zapper," a comparative genomics tool, was used to predict synteny between wheat chromosome 1D, Oryza sativa, Brachypodium distachyon, and Sorghum bicolor. A total of 57 markers were developed based on synteny or literature and genotyped to produce a RH map spanning 205.2 cR. A test-cross methodology was devised for phenotyping of RH progenies, and through forward genetic, the scs (ae) locus was pinpointed to a 1.1 Mb-segment containing eight genes. Further, the high resolution provided by RH mapping, combined with chromosome-wise synteny analysis, located the ancestral point of fusion between the telomeric and centromeric repeats of two paleochromosomes that originated chromosome 1D. Also, it indicated that the centromere of this chromosome is likely the result of a neocentromerization event, rather than the conservation of an ancestral centromere as previously believed. Interestingly, location of scs locus in the vicinity of paleofusion is not associated with the expected disruption of synteny, but rather with a good degree of conservation across grass species. Indeed, these observations advocate the evolutionary importance of this locus as suggested by "Maan's scs hypothesis."

Wheat Zapper: a flexible online tool for colinearity studies in grass genomes.

In the course of evolution, the genomes of grasses have maintained an observable degree of gene order conservation. The information available for already sequenced genomes can be used to predict the gene order of nonsequenced species by means of comparative colinearity studies. The "Wheat Zapper" application presented here performs on-demand colinearity analysis between wheat, rice, Sorghum, and Brachypodium in a simple, time efficient, and flexible manner. This application was specifically designed to provide plant scientists with a set of tools, comprising not only synteny inference, but also automated primer design, intron/exon boundaries prediction, visual representation using the graphic tool Circos 0.53, and the possibility of downloading FASTA sequences for downstream applications. Quality of the "Wheat Zapper" prediction was confirmed against the genome of maize, with good correlation (r > 0.83) observed between the gene order predicted on the basis of synteny and their actual position on the genome. Further, the accuracy of "Wheat Zapper" was calculated at 0.65 considering the "Genome Zipper" application as the "gold" standard. The differences between these two tools are amply discussed, making the point that "Wheat Zapper" is an accurate and reliable on-demand tool that is sure to benefit the cereal scientific community. The Wheat Zapper is available at http://wge.ndsu.nodak.edu/wheatzapper/ .

DNA repair and crossing over favor similar chromosome regions as discovered in radiation hybrid of Triticum.

BACKGROUND: The uneven distribution of recombination across the length of chromosomes results in inaccurate estimates of genetic to physical distances. In wheat (Triticum aestivum L.) chromosome 3B, it has been estimated that 90% of the cross over events occur in distal sub-telomeric regions representing 40% of the chromosome. Radiation hybrid (RH) mapping which does not rely on recombination is a strategy to map genomes and has been widely employed in animal species and more recently in some plants. RH maps have been proposed to provide i) higher and ii) more uniform resolution than genetic maps, and iii) to be independent of the distribution patterns observed for meiotic recombination. An in vivo RH panel was generated for mapping chromosome 3B of wheat in an attempt to provide a complete scaffold for this ~1 Gb segment of the genome and compare the resolution to previous genetic maps. RESULTS: A high density RH map with 541 marker loci anchored to chromosome 3B spanning a total distance of 1871.9 cR was generated. Detailed comparisons with a genetic map of similar quality confirmed that i) the overall resolution of the RH map was 10.5 fold higher and ii) six fold more uniform. A significant interaction (r = 0.879 at p = 0.01) was observed between the DNA repair mechanism and the distribution of crossing-over events. This observation could be explained by accepting the possibility that the DNA repair mechanism in somatic cells is affected by the chromatin state in a way similar to the effect that chromatin state has on recombination frequencies in gametic cells. CONCLUSIONS: The RH data presented here support for the first time in vivo the hypothesis of non-casual interaction between recombination hot-spots and DNA repair. Further, two major hypotheses are presented on how chromatin compactness could affect the DNA repair mechanism. Since the initial RH application 37 years ago, we were able to show for the first time that the iii) third hypothesis of RH mapping might not be entirely correct.

A resource for the in silico identification of fungal polyketide synthases from predicted fungal proteomes.

The goal of this study was to develop a tool specifically designed to identify iterative polyketide synthases (iPKSs) from predicted fungal proteomes. A fungi-based PKS prediction model, specifically for fungal iPKSs, was developed using profile hidden Markov models (pHMMs) based on two essential iPKS domains, the ß-ketoacyl synthase (KS) domain and acyltransferase (AT) domain, derived from fungal iPKSs. This fungi-based PKS prediction model was initially tested on the well-annotated proteome of Fusarium graminearum, identifying 15 iPKSs that matched previous predictions and gene disruption studies. These fungi-based pHMMs were subsequently applied to the predicted fungal proteomes of Alternaria brassicicola, Fusarium oxysporum f.sp. lycopersici, Verticillium albo-atrum and Verticillium dahliae. The iPKSs predicted were compared against those predicted by the currently available mixed-kingdom PKS models that include both bacterial and fungal sequences. These mixed-kingdom models have been proven previously by others to be better in predicting true iPKSs from non-iPKSs compared with other available models (e.g. Pfam and TIGRFAM). The fungi-based model was found to perform significantly better on fungal proteomes than the mixed-kingdom PKS model in accuracy, sensitivity, specificity and precision. In addition, the model was capable of predicting the reducing nature of fungal iPKSs by comparison of the bit scores obtained from two separate reducing and nonreducing pHMMs for each domain, which was confirmed by phylogenetic analysis of the KS domain. Biological confirmation of the predictions was obtained by polymerase chain reaction (PCR) amplification of the KS and AT domains of predicted iPKSs from V. dahliae using domain-specific primers and genomic DNA, followed by sequencing of the PCR products. It is expected that the fungi-based PKS model will prove to be a useful tool for the identification and annotation of fungal PKSs from predicted proteomes.

Generalised sequence signatures through symbolic clustering.

Traditionally sequence motifs and domains are defined such that insertions, deletions and mismatched regions are small compared with matched regions. We introduce an algorithm for the identification of Generalised Sequence Signatures (GSS) that can be composed of windows distributed throughout the sequence. Our approach is based on clustering analysis of recurring subsequences of a predefined length, to which we refer as symbols. Sequences are grouped so as to maximise the number of shared symbols among them. We show that the utilisation of GSS for deriving sequence annotations yields higher confidence values than the usage of other signature recognition approaches.

Clustering sequences by overlap.

A clustering algorithm is introduced that combines the strengths of clustering and motif finding techniques. Clusters are identified based on unambiguously defined sequence sections as in motif finding algorithms. The definition of similarity within clusters allows transitive matches and, thereby, enables the discovery of remote homologies that cannot be found through motif-finding algorithms. Directed Acyclic Graph (DAG) structures are constructed that link short clusters to the longer ones. We compare the clustering results to the corresponding domains in the InterPro database. A second comparison shows that annotations based on our domains are inherently more consistent than those based on InterPro domains.

Relating gene expression data on two-component systems to functional annotations in Escherichia coli.

BACKGROUND: Obtaining physiological insights from microarray experiments requires computational techniques that relate gene expression data to functional information. Traditionally, this has been done in two consecutive steps. The first step identifies important genes through clustering or statistical techniques, while the second step assigns biological functions to the identified groups. Recently, techniques have been developed that identify such relationships in a single step. RESULTS: We have developed an algorithm that relates patterns of gene expression in a set of microarray experiments to functional groups in one step. Our only assumption is that patterns co-occur frequently. The effectiveness of the algorithm is demonstrated as part of a study of regulation by two-component systems in Escherichia coli. The significance of the relationships between expression data and functional annotations is evaluated based on density histograms that are constructed using product similarity among expression vectors. We present a biological analysis of three of the resulting functional groups of proteins, develop hypotheses for further biological studies, and test one of these hypotheses experimentally. A comparison with other algorithms and a different data set is presented. CONCLUSION: Our new algorithm is able to find interesting and biologically meaningful relationships, not found by other algorithms, in previously analyzed data sets. Scaling of the algorithm to large data sets can be achieved based on a theoretical model.