Locational distribution of gene functional classes in Arabidopsis thaliana

iLoci: robust evaluation of genome content and organization for provisional and mature genome assemblies

We introduce a new framework for genome analyses based on parsing an annotated genome assembly into distinct interval loci (iLoci), available as open-source software as part of the AEGeAn Toolkit (https://github.com/BrendelGroup/AEGeAn). We demonstrate that iLoci provide an alternative coordinate system that is robust to changes in assembly and annotation versions and facilitates granular quality control of genome data. We discuss how statistics computed on iLoci reflect various characteristics of genome content and organization and illustrate how these statistics can be used to establish a baseline for assessment of the completeness and accuracy of the data. We also introduce a well-defined measure of relative genome compactness and compute other iLocus statistics that reveal genome-wide characteristics of gene arrangements in the whole genome context. Given the fast pace of assembly/annotation updates, our AEGeAn Toolkit fills a niche in computational genomics based on deriving persistent and species-specific genome statistics. Gene structure model-centric iLoci provide a precisely defined coordinate system that can be used to store assembly/annotation updates that reflect either stable or changed assessments. Large-scale application of the approach revealed species- and clade-specific genome organization in precisely defined computational terms, promising intriguing forays into the forces of shaping genome structure as more and more genome assemblies are being deposited.

Major Greenwood (1880-1949): a biographical and bibliographical study

Major Greenwood was the foremost medical statistician of the first half of the 20th century in the U.K. Trained in both medicine and statistics, his career extended over 45 years during which he published eight books, 23 extensive reports and over 200 papers. His classical education extended to Latin and Greek, and he was fluent in German and French. We provide an overview of his life including family background, training and his career subdivided according to the places where he worked. We describe in particular the key role he played with others in the development of medical statistics within the Medical Research Council, the General Register Office, the Department of Health and the Universities.

A Factor Graph Approach to Automated GO Annotation

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum.

Integration of a systems biological network analysis and QTL results for biomass heterosis in Arabidopsis thaliana

To contribute to a further insight into heterosis we applied an integrative analysis to a systems biological network approach and a quantitative genetics analysis towards biomass heterosis in early Arabidopsis thaliana development. The study was performed on the parental accessions C24 and Col-0 and the reciprocal crosses. In an over-representation analysis it was tested if the overlap between the resulting gene lists of the two approaches is significantly larger than expected by chance. Top ranked genes in the results list of the systems biological analysis were significantly over-represented in the heterotic QTL candidate regions for either hybrid as well as regarding mid-parent and best-parent heterosis. This suggests that not only a few but rather several genes that influence biomass heterosis are located within each heterotic QTL region. Furthermore, the overlapping resulting genes of the two integrated approaches were particularly enriched in biomass related pathways. A chromosome-wise over-representation analysis gave rise to the hypothesis that chromosomes number 2 and 4 probably carry a majority of the genes involved in biomass heterosis in the early development of Arabidopsis thaliana.

Functional bias in molecular evolution rate of Arabidopsis thaliana

BACKGROUND

Characteristics derived from mutation and other mechanisms that are advantageous for survival are often preserved during evolution by natural selection. Some genes are conserved in many organisms because they are responsible for fundamental biological function, others are conserved for their unique functional characteristics. Therefore one would expect the rate of molecular evolution for individual genes to be dependent on their biological function. Whether this expectation holds for genes duplicated by whole genome duplication is not known.

RESULTS

We empirically demonstrate here, using duplicated genes generated from the Arabidopsis thaliana alpha-duplication event, that the rate of molecular evolution of genes duplicated in this event depend on biological function. Using functional clustering based on gene ontology annotation of gene pairs, we show that some duplicated genes, such as defense response genes, are under weaker purifying selection or under stronger diversifying selection than other duplicated genes, such as protein translation genes, as measured by the ratio of nonsynonymous to synonymous divergence (dN/dS).

CONCLUSIONS

These results provide empirical evidence indicating that molecular evolution rate for genes duplicated in whole genome duplication, as measured by dN/dS, may depend on biological function, which we characterize using gene ontology annotation. Furthermore, the general approach used here provides a framework for comparative analysis of molecular evolution rate for genes based on their biological function.

GO-At: in silico prediction of gene function in Arabidopsis thaliana by combining heterogeneous data

Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO-At (gene ontology prediction in A. thaliana), a method that combines five data types (co-expression, sequence, phylogenetic profile, interaction and gene neighbourhood) to predict gene function in Arabidopsis. Using a simple, yet powerful two-step approach, GO-At first generates a list of genes ranked in descending order of probability of functional association with the query gene. Next, a prediction score is automatically assigned to each function in this list based on the assumption that functions appearing most frequently at the top of the list are most likely to represent the function of the query gene. In this way, the second step provides an effective alternative to simply taking the 'best hit' from the first list, and achieves success rates of up to 79%. GO-At is applicable across all three GO categories: molecular function, biological process and cellular component, and can assign functions at multiple levels of annotation detail. Furthermore, we demonstrate GO-At's ability to predict functions of uncharacterized genes by identifying ten putative golgins/Golgi-associated proteins amongst 8219 genes of previously unknown cellular component and present independent evidence to support our predictions. A web-based implementation of GO-At (http://www.bioinformatics.leeds.ac.uk/goat) is available, providing a unique resource for plant researchers to make predictions for uncharacterized genes and predict novel functions in Arabidopsis.