Automatic Gene Function Prediction in the 2020's.

The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved.

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function.

Motivation: Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task.

Metric learning on expression data for gene function prediction.

Motivation: Co-expression of two genes across different conditions is indicative of their involvement in the same biological process. However, when using RNA-Seq datasets with many experimental conditions from diverse sources, only a subset of the experimental conditions is expected to be relevant for finding genes related to a particular Gene Ontology (GO) term. Therefore, we hypothesize that when the purpose is to find similarly functioning genes, the co-expression of genes should not be determined on all samples but only on those samples informative for the GO term of interest.

Improving protein function prediction using protein sequence and GO-term similarities.

Motivation: Most automatic functional annotation methods assign Gene Ontology (GO) terms to proteins based on annotations of highly similar proteins. We advocate that proteins that are less similar are still informative. Also, despite their simplicity and structure, GO terms seem to be hard for computers to learn, in particular the Biological Process ontology, which has the most terms (>29 000).

Homologues of potato chromosome 5 show variable collinearity in the euchromatin, but dramatic absence of sequence similarity in the pericentromeric heterochromatin.

Background: In flowering plants it has been shown that de novo genome assemblies of different species and genera show a significant drop in the proportion of alignable sequence. Within a plant species, however, it is assumed that different haplotypes of the same chromosome align well. In this paper we have compared three de novo assemblies of potato chromosome 5 and report on the sequence variation and the proportion of sequence that can be aligned.

A quantitative and dynamic model of the Arabidopsis flowering time gene regulatory network.

Various environmental signals integrate into a network of floral regulatory genes leading to the final decision on when to flower. Although a wealth of qualitative knowledge is available on how flowering time genes regulate each other, only a few studies incorporated this knowledge into predictive models. Such models are invaluable as they enable to investigate how various types of inputs are combined to give a quantitative readout.

The genome of the stress-tolerant wild tomato species Solanum pennellii.

Solanum pennellii is a wild tomato species endemic to Andean regions in South America, where it has evolved to thrive in arid habitats. Because of its extreme stress tolerance and unusual morphology, it is an important donor of germplasm for the cultivated tomato Solanum lycopersicum. Introgression lines (ILs) in which large genomic regions of S.

Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing.

We explored genetic variation by sequencing a selection of 84 tomato accessions and related wild species representative of the Lycopersicon, Arcanum, Eriopersicon and Neolycopersicon groups, which has yielded a huge amount of precious data on sequence diversity in the tomato clade. Three new reference genomes were reconstructed to support our comparative genome analyses. Comparative sequence alignment revealed group-, species- and accession-specific polymorphisms, explaining characteristic fruit traits and growth habits in the various cultivars.

The genomes of the fungal plant pathogens Cladosporium fulvum and Dothistroma septosporum reveal adaptation to different hosts and lifestyles but also signatures of common ancestry.

We sequenced and compared the genomes of the Dothideomycete fungal plant pathogens Cladosporium fulvum (Cfu) (syn. Passalora fulva) and Dothistroma septosporum (Dse) that are closely related phylogenetically, but have different lifestyles and hosts. Although both fungi grow extracellularly in close contact with host mesophyll cells, Cfu is a biotroph infecting tomato, while Dse is a hemibiotroph infecting pine.

Mutational robustness of gene regulatory networks.

Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it.

Predicting the impact of alternative splicing on plant MADS domain protein function.

Several genome-wide studies demonstrated that alternative splicing (AS) significantly increases the transcriptome complexity in plants. However, the impact of AS on the functional diversity of proteins is difficult to assess using genome-wide approaches. The availability of detailed sequence annotations for specific genes and gene families allows for a more detailed assessment of the potential effect of AS on their function.

Correlated mutations via regularized multinomial regression.

Background: In addition to sequence conservation, protein multiple sequence alignments contain evolutionary signal in the form of correlated variation among amino acid positions. This signal indicates positions in the sequence that influence each other, and can be applied for the prediction of intra- or intermolecular contacts. Although various approaches exist for the detection of such correlated mutations, in general these methods utilize only pairwise correlations.

Genome sequence and analysis of the tuber crop potato.

Potato (Solanum tuberosum L.) is the world's most important non-grain food crop and is central to global food security. It is clonally propagated, highly heterozygous, autotetraploid, and suffers acute inbreeding depression.

Finished genome of the fungal wheat pathogen Mycosphaerella graminicola reveals dispensome structure, chromosome plasticity, and stealth pathogenesis.

The plant-pathogenic fungus Mycosphaerella graminicola (asexual stage: Septoria tritici) causes septoria tritici blotch, a disease that greatly reduces the yield and quality of wheat. This disease is economically important in most wheat-growing areas worldwide and threatens global food production. Control of the disease has been hampered by a limited understanding of the genetic and biochemical bases of pathogenicity, including mechanisms of infection and of resistance in the host.

PRI-CAT: a web-tool for the analysis, storage and visualization of plant ChIP-seq experiments.

Although several tools for the analysis of ChIP-seq data have been published recently, there is a growing demand, in particular in the plant research community, for computational resources with which such data can be processed, analyzed, stored, visualized and integrated within a single, user-friendly environment. To accommodate this demand, we have developed PRI-CAT (Plant Research International ChIP-seq analysis tool), a web-based workflow tool for the management and analysis of ChIP-seq experiments. PRI-CAT is currently focused on Arabidopsis, but will be extended with other plant species in the near future.

Assessing the contribution of alternative splicing to proteome diversity in Arabidopsis thaliana using proteomics data.

Background: Large-scale analyses of genomics and transcriptomics data have revealed that alternative splicing (AS) substantially increases the complexity of the transcriptome in higher eukaryotes. However, the extent to which this complexity is reflected at the level of the proteome remains unclear. On the basis of a lack of conservation of AS between species, we previously concluded that AS does not frequently serve as a mechanism that enables the production of multiple functional proteins from a single gene.

SLIDER: a generic metaheuristic for the discovery of correlated motifs in protein-protein interaction networks.

Correlated motif mining (cmm) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for cmm thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network.

Sequence motifs in MADS transcription factors responsible for specificity and diversification of protein-protein interaction.

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana.

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.

Conserved and variable correlated mutations in the plant MADS protein network.

Background: Plant MADS domain proteins are involved in a variety of developmental processes for which their ability to form various interactions is a key requisite. However, not much is known about the structure of these proteins or their complexes, whereas such knowledge would be valuable for a better understanding of their function. Here, we analyze those proteins and the complexes they form using a correlated mutation approach in combination with available structural, bioinformatics and experimental data.

Continuous-time modeling of cell fate determination in Arabidopsis flowers.

Background: The genetic control of floral organ specification is currently being investigated by various approaches, both experimentally and through modeling. Models and simulations have mostly involved boolean or related methods, and so far a quantitative, continuous-time approach has not been explored.

Results: We propose an ordinary differential equation (ODE) model that describes the gene expression dynamics of a gene regulatory network that controls floral organ formation in the model plant Arabidopsis thaliana.

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.

In silico miRNA prediction in metazoan genomes: balancing between sensitivity and specificity.

Background: MicroRNAs (miRNAs), short approximately 21-nucleotide RNA molecules, play an important role in post-transcriptional regulation of gene expression. The number of known miRNA hairpins registered in the miRBase database is rapidly increasing, but recent reports suggest that many miRNAs with restricted temporal or tissue-specific expression remain undiscovered. Various strategies for in silico miRNA identification have been proposed to facilitate miRNA discovery.

Comparative analysis indicates that alternative splicing in plants has a limited role in functional expansion of the proteome.

Background: Alternative splicing (AS) is a widespread phenomenon in higher eukaryotes but the extent to which it leads to functional protein isoforms and to proteome expansion at large is still a matter of debate. In contrast to animal species, for which AS has been studied extensively at the protein and functional level, protein-centered studies of AS in plant species are scarce. Here we investigate the functional impact of AS in dicot and monocot plant species using a comparative approach.