Centromere Satellite Repeats Have Undergone Rapid Changes in Polyploid Wheat Subgenomes.

Centromeres mediate the pairing of homologous chromosomes during meiosis; this pairing is particularly challenging for polyploid plants such as hexaploid bread wheat (Triticum aestivum), as their meiotic machinery must differentiate homologs from similar homoeologs. However, the sequence compositions (especially functional centromeric satellites) and evolutionary history of wheat centromeres are largely unknown. Here, we mapped T. aestivum centromeres by chromatin immunoprecipitation sequencing using antibodies to the centromeric-specific histone H3 variant (CENH3); this identified two types of functional centromeric satellites that are abundant in two of the three subgenomes. These centromeric satellites had unit sizes greater than 500 bp and contained specific sites with highly phased binding to CENH3 nucleosomes. Phylogenetic analysis revealed that the satellites have diverged in the three T. aestivum subgenomes, and the more homogeneous satellite arrays are associated with CENH3. Satellite signals decreased and the degree of satellites variation increased from diploid to hexaploid wheat. Moreover, several T. aestivum centromeres lack satellite repeats. Rearrangements, including local expansion and satellite variations, inversions, and changes in gene expression, occurred during the evolution from diploid to tetraploid and hexaploid wheat. These results reveal the asymmetry in centromere organization among the wheat subgenomes, which may play a role in proper homolog pairing during meiosis.

An integrative approach to understanding the combinatorial histone code at functional elements.

SUMMARY: The rapid advancement of genomic technology has revealed the enormous complexity and combinatorial nature of chromatin modifications. To facilitate interpretation of the combinatorial nature of chromatin, we have developed a novel method to integrate all chromatin datasets into distinct nucleosome types (nucleosome alphabet). We have applied this approach to Saccharomyces cerevisiae, generating a nucleosome alphabet, which forms chromatin motifs when mapped back to the genome. By applying novel chromatin alignment and global word search approaches, we have defined distinctive chromatin motifs for introns, origins of replication, tRNAs, antisense transcripts, double-strand-break hotspots and DNase hypersensitive sites, and can distinguish genes by expression level. We have also uncovered strong associations between transcription factor binding and specific types of nucleosomes. Our results demonstrate the uses and functionality of defining a chromatin alphabet and provide a unique and novel framework for exploring chromatin architecture. CONTACT: mjbuck@buffalo.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sequence-based classification using discriminatory motif feature selection.

Most existing methods for sequence-based classification use exhaustive feature generation, employing, for example, all k-mer patterns. The motivation behind such (enumerative) approaches is to minimize the potential for overlooking important features. However, there are shortcomings to this strategy. First, practical constraints limit the scope of exhaustive feature generation to patterns of length ≤ k, such that potentially important, longer (> k) predictors are not considered. Second, features so generated exhibit strong dependencies, which can complicate understanding of derived classification rules. Third, and most importantly, numerous irrelevant features are created. These concerns can compromise prediction and interpretation. While remedies have been proposed, they tend to be problem-specific and not broadly applicable. Here, we develop a generally applicable methodology, and an attendant software pipeline, that is predicated on discriminatory motif finding. In addition to the traditional training and validation partitions, our framework entails a third level of data partitioning, a discovery partition. A discriminatory motif finder is used on sequences and associated class labels in the discovery partition to yield a (small) set of features. These features are then used as inputs to a classifier in the training partition. Finally, performance assessment occurs on the validation partition. Important attributes of our approach are its modularity (any discriminatory motif finder and any classifier can be deployed) and its universality (all data, including sequences that are unaligned and/or of unequal length, can be accommodated). We illustrate our approach on two nucleosome occupancy datasets and a protein solubility dataset, previously analyzed using enumerative feature generation. Our method achieves excellent performance results, with and without optimization of classifier tuning parameters. A Python pipeline implementing the approach is available at http://www.epibiostat.ucsf.edu/biostat/sen/dmfs/.

Contribution of histone sequence preferences to nucleosome organization: proposed definitions and methodology.

We propose definitions and procedures for comparing nucleosome maps and discuss current agreement and disagreement on the effect of histone sequence preferences on nucleosome organization in vivo.

A multi-layer method to study genome-scale positions of nucleosomes.

The basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 bp of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and experimental nucleosome position data and found that besides a high nucleosome recognition and a strong agreement with standard statistical methods, the MLM can identify distinct classes of nucleosomes, making it an important tool for the genome wide analysis of nucleosome position and function. In conclusion, the MLM allows a better representation of nucleosome position data and a significant reduction in computational time.