Adaptation of perennial flowering phenology across the European range of .

Flowering phenology is important in the adaptation of many plants to their local environment, but its adaptive value has not been extensively studied in herbaceous perennials. We used as a model system to determine the importance of flowering phenology to fitness of a herbaceous perennial with a wide geographical range. Individual plants representative of local genetic diversity (accessions) were collected across Europe, including in Spain, the Alps and Scandinavia.

Demography and mating system shape the genome-wide impact of purifying selection in .

Plant mating systems have profound effects on levels and structuring of genetic variation and can affect the impact of natural selection. Although theory predicts that intermediate outcrossing rates may allow plants to prevent accumulation of deleterious alleles, few studies have empirically tested this prediction using genomic data. Here, we study the effect of mating system on purifying selection by conducting population-genomic analyses on whole-genome resequencing data from 38 European individuals of the arctic-alpine crucifer We find that outcrossing and mixed-mating populations maintain genetic diversity at similar levels, whereas highly self-fertilizing Scandinavian show a strong reduction in genetic diversity, most likely as a result of a postglacial colonization bottleneck.

MitoRS, a method for high throughput, sensitive, and accurate detection of mitochondrial DNA heteroplasmy.

Background: Mitochondrial dysfunction is linked to numerous pathological states, in particular related to metabolism, brain health and ageing. Nuclear encoded gene polymorphisms implicated in mitochondrial functions can be analyzed in the context of classical genome wide association studies. By contrast, mitochondrial DNA (mtDNA) variants are more challenging to identify and analyze for several reasons.

Gene Ontology consistent protein function prediction: the FALCON algorithm applied to six eukaryotic genomes.

: Gene Ontology (GO) is a hierarchical vocabulary for the description of biological functions and locations, often employed by computational methods for protein function prediction. Due to the structure of GO, function predictions can be self- contradictory. For example, a protein may be predicted to belong to a detailed functional class, but not in a broader class that, due to the vocabulary structure, includes the predicted one.

A large-scale evaluation of computational protein function prediction.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high.

In silico characterization and molecular evolutionary analysis of a novel superfamily of fungal effector proteins.

Most fungal plant pathogens secrete effector proteins during pathogenesis to manipulate their host's defense and promote disease. These are so highly diverse in sequence and distribution, they are essentially considered as species-specific. However, we have recently shown the presence of homologous effectors in fungal species of the Dothideomycetes class.

The turbulent life of Sirevirus retrotransposons and the evolution of the maize genome: more than ten thousand elements tell the story.

Sireviruses are one of the three genera of Copia long terminal repeat (LTR) retrotransposons, exclusive to and highly abundant in plants, and with a unique, among retrotransposons, genome structure. Yet, perhaps due to the few references to the Sirevirus origin of some families, compounded by the difficulty in correctly assigning retrotransposon families into genera, Sireviruses have hardly featured in recent research. As a result, analysis at this key level of classification and details of their colonization and impact on plant genomes are currently lacking.

Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.

A major challenge in the field of systems biology consists of predicting gene regulatory networks based on different training data. Within the DREAM4 initiative, we took part in the multifactorial sub-challenge that aimed to predict gene regulatory networks of size 100 from training data consisting of steady-state levels obtained after applying multifactorial perturbations to the original in silico network. Due to the static character of the challenge data, we tackled the problem via a sparse Gaussian Markov Random Field, which relates network topology with the covariance inverse generated by the gene measurements.

Genome-wide computational function prediction of Arabidopsis proteins by integration of multiple data sources.

Although Arabidopsis (Arabidopsis thaliana) is the best studied plant species, the biological role of one-third of its proteins is still unknown. We developed a probabilistic protein function prediction method that integrates information from sequences, protein-protein interactions, and gene expression. The method was applied to proteins from Arabidopsis.

Bayesian Markov Random Field analysis for protein function prediction based on network data.

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act.

The use of multiple hierarchically independent gene ontology terms in gene function prediction and genome annotation.

The Gene Ontology (GO) is a widely used controlled vocabulary for the description of gene function. In this study we quantify the usage of multiple and hierarchically independent GO terms in the curated genome annotations of seven well-studied species. In most genomes, significant proportions (6-60%) of genes have been annotated with multiple and hierarchically independent terms.