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Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emissions and loss of biodiversity1. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value2. Faba bean (Vicia fabaL.) has a high yield potential and is well suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has expanded to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, although with substantial copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association analysis to dissect the genetic basis of seed size and hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate the improvement of sustainable protein production across the Mediterranean, subtropical and northern temperate agroecological zones.

A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

We have investigated gene expression changes produced by acute and chronic daily treatment with a prototypical antidepressant, imipramine, using DNA microarrays. The analysis of similarities in gene expression patterns among functionally related genes revealed four expression profile cluster areas that showed a highly significant overrepresentation of several functional classes. Genes encoding for proteins involved in cAMP metabolism, postsynaptic membrane proteins, and proto-oncogenes were overrepresented in different cluster areas. Furthermore, we found that serine proteases as a group were similarly regulated by chronic antidepressant treatment. Our data suggest that cAMP metabolism, synaptic function, and protein processing by serine proteases may be important targets of antidepressant treatment and potential objects for antidepressant drug development.

Agents acting as noncompetitive N-methyl-d-aspartate (NMDA)/glutamate receptor antagonists induce the expression of several genes in limbic cortical regions, such as the cingulate, retrosplenial, and entorhinal cortices. These include important regulatory genes such as the neurotrophin brain–derived neurotrophic factor (BDNF), its receptor trkB, and c-fos. We applied expression profiling methods to find genes coregulated with BDNF following treatment with the prototypical NMDA/glutamate receptor antagonist MK-801. Expression profiling provides a useful technique for describing the molecular and transcriptional level events that follow various processes. We illustrate the utility of microarrays to find novel ESTs regulated by MK-801. We also used expression profiling with microarrays to characterize the levels of transcription factor cAMP response element modulator (CREM) and inducible cAMP early repressor (ICER) isoforms that are induced by MK-801. These factors may act as the eventual repressors for BDNF expression via competition and heterodimerization with phosphorylated CREB, a transcription factor important for BDNF expression. Finally, we find and confirm the regulation of Erp29, RTN1, and an ABC transporter by antagonism of NMDA/glutamate receptors as potential stress related molecules in brain. The emerging picture generated by using these expression profiling approaches, identifies several of what likely will be many molecules that take part in the complex events that occur during BDNF signaling mediated by blockade of NMDA/glutamate receptors.

DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self‐organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organization of large data files. We have here applied the SOM algorithm to analyze published data of yeast gene expression and show that SOM is an excellent tool for the analysis and visualization of gene expression profiles.

Tegmental nuclei in the ventral midbrain and anterior hindbrain control motivated behavior, mood, memory, and movement. These nuclei contain inhibitory GABAergic and excitatory glutamatergic neurons, whose molecular diversity and development remain largely unraveled. Many tegmental neurons originate in the embryonic ventral rhombomere 1 (r1), where GABAergic fate is regulated by the transcription factor (TF) Tal1. We used single-cell mRNA sequencing of the mouse ventral r1 to characterize the Tal1-dependent and independent neuronal precursors. We describe gene expression dynamics during bifurcation of the GABAergic and glutamatergic lineages and show how active Notch signaling promotes GABAergic fate selection in post-mitotic precursors. We identify GABAergic precursor subtypes that give rise to distinct tegmental nuclei and demonstrate that Sox14 and Zfpm2, two TFs downstream of Tal1, are necessary for the differentiation of specific tegmental GABAergic neurons. Our results provide a framework for understanding the development of cellular diversity in the tegmental nuclei.

Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicansand Pseudomonas aureginosagenomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicansand D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Motivation: The rapid increase in the amount of protein sequence data has created a need for an automated identification of evolutionarily related subgroups from large datasets. The existing methods typically require a priori specification of the number of putative groups, which defines the resolution of the classification solution. Results: We introduce a Bayesian model-based approach to simultaneous identification of evolutionary groups and conserved parts of the protein sequences. The model-based approach provides an intuitive and efficient way of determining the number of groups from the sequence data, in contrast to the ad hoc methods often exploited for similar purposes. Our model recognizes the areas in the sequences that are relevant for the clustering and regards other areas as noise. We have implemented the method using a fast stochastic optimization algorithm which yields a clustering associated with the estimated maximum posterior probability. The method has been shown to have high specificity and sensitivity in simulated and real clustering tasks. With real datasets the method also highlights the residues close to the active site. Availability: Software ‘kPax’ is available at http://www.rni.helsinki.fi/jic/softa.html Contact: pekka.marttinen@helsinki.fi Supplementary information: http://www.rni.helsinki.fi/~jic/softa.html