Expression Atlas update-an integrated database of gene and protein expression in humans, animals and plants

Expression Atlas (http://www.ebi.ac.uk/gxa) provides information about gene and protein expression in animal and plant samples of different cell types, organism parts, developmental stages, diseases and other conditions. It consists of selected microarray and RNA-sequencing studies from ArrayExpress, which have been manually curated, annotated with ontology terms, checked for high quality and processed using standardised analysis methods. Since the last update, Atlas has grown seven-fold (1572 studies as of August 2015), and incorporates baseline expression profiles of tissues from Human Protein Atlas, GTEx and FANTOM5, and of cancer cell lines from ENCODE, CCLE and Genentech projects. Plant studies constitute a quarter of Atlas data. For genes of interest, the user can view baseline expression in tissues, and differential expression for biologically meaningful pairwise comparisons—estimated using consistent methodology across all of Atlas. Our first proteomics study in human tissues is now displayed alongside transcriptomics data in the same tissues. Novel analyses and visualisations include: ‘enrichment’ in each differential comparison of GO terms, Reactome, Plant Reactome pathways and InterPro domains; hierarchical clustering (by baseline expression) of most variable genes and experimental conditions; and, for a given gene-condition, distribution of baseline expression across biological replicates.© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. The published article is available at: http://nar.oxfordjournals.org/content/44/D1/D74

Insights into Online microRNA Bioinformatics Tools

MicroRNAs (miRNAs) are members of the small non-coding RNA family regulating gene expression at the post-transcriptional level. MiRNAs have been found to have critical roles in various biological and pathological processes. Research in this field has significantly progressed, with increased recognition of the importance of miRNA regulation. As a result of the vast data and information available regarding miRNAs, numerous online tools have emerged to address various biological questions related to their function and influence across essential cellular processes. This review includes a brief introduction to available resources for an investigation covering aspects such as miRNA sequences, target prediction/validation, miRNAs associated with disease, pathway analysis and genetic variants within miRNAs

A Variable Polyglutamine Repeat Affects Subcellular Localization and Regulatory Activity of a Populus ANGUSTIFOLIA Protein.

Polyglutamine (polyQ) stretches have been reported to occur in proteins across many organisms including animals, fungi and plants. Expansion of these repeats has attracted much attention due their associations with numerous human diseases including Huntington's and other neurological maladies. This suggests that the relative length of polyQ stretches is an important modulator of their function. Here, we report the identification of a Populus C-terminus binding protein (CtBP) ANGUSTIFOLIA (PtAN1) which contains a polyQ stretch whose functional relevance had not been established. Analysis of 917 resequenced Populus trichocarpa genotypes revealed three allelic variants at this locus encoding 11-, 13- and 15-glutamine residues. Transient expression assays using Populus leaf mesophyll protoplasts revealed that the 11Q variant exhibited strong nuclear localization whereas the 15Q variant was only found in the cytosol, with the 13Q variant exhibiting localization in both subcellular compartments. We assessed functional implications by evaluating expression changes of putative PtAN1 targets in response to overexpression of the three allelic variants and observed allele-specific differences in expression levels of putative targets. Our results provide evidence that variation in polyQ length modulates PtAN1 function by altering subcellular localization

IMOTA: an interactive multi-omics tissue atlas for the analysis of human miRNA-target interactions

Web repositories for almost all ‘omics’ types have been generated—detailing the repertoire of representatives across different tissues or cell types. A logical next step is the combination of these valuable sources. With IMOTA (interactive multi omics tissue atlas), we developed a database that includes 23 725 relations between miRNAs and 23 tissues, 310 932 relations between mRNAs and the same tissues as well as 63 043 relations between proteins and the 23 tissues in Homo sapiens. IMOTA also contains data on tissue-specific interactions, e.g. information on 331 413 miRNAs and target gene pairs that are jointly expressed in the considered tissues. By using intuitive filter and visualization techniques, it is with minimal effort possible to answer various questions. These include rather general questions but also requests specific for genes, miRNAs or proteins. An example for a general task could be ‘identify all miRNAs, genes and proteins in the lung that are highly expressed and where experimental evidence proves that the miRNAs target the genes’. An example for a specific request for a gene and a miRNA could for example be ‘In which tissues is miR-34c and its target gene BCL2 expressed?’. The IMOTA repository is freely available online at https://ccb-web.cs.uni-saarland.de/imota/

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes

Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.&nbsp

Gramene 2018: unifying comparative genomics and pathway resources for plant research

Gramene (http://www.gramene.org) is a knowledgebase for comparative functional analysis in major crops and model plant species. The current release, #54, includes over 1.7 million genes from 44 reference genomes, most of which were organized into 62,367 gene families through orthologous and paralogous gene classification, whole-genome alignments, and synteny. Additional gene annotations include ontology-based protein structure and function; genetic, epigenetic, and phenotypic diversity; and pathway associations. Gramene's Plant Reactome provides a knowledgebase of cellular-level plant pathway networks. Specifically, it uses curated rice reference pathways to derive pathway projections for an additional 66 species based on gene orthology, and facilitates display of gene expression, gene-gene interactions, and user-defined omics data in the context of these pathways. As a community portal, Gramene integrates best-of-class software and infrastructure components including the Ensembl genome browser, Reactome pathway browser, and Expression Atlas widgets, and undergoes periodic data and software upgrades. Via powerful, intuitive search interfaces, users can easily query across various portals and interactively analyze search results by clicking on diverse features such as genomic context, highly augmented gene trees, gene expression anatomograms, associated pathways, and external informatics resources. All data in Gramene are accessible through both visual and programmatic interfaces

On-the-fly selection of cell-specific enhancers, genes, miRNAs and proteins across the human body using SlideBase

Genomics consortia have produced large datasets profiling the expression of genes, micro-RNAs, enhancers and more across human tissues or cells. There is a need for intuitive tools to select subsets of such data that is the most relevant for specific studies. To this end, we present SlideBase, a web tool which offers a new way of selecting genes, promoters, enhancers and microRNAs that are preferentially expressed/used in a specified set of cells/tissues, based on the use of interactive sliders. With the help of sliders, SlideBase enables users to define custom expression thresholds for individual cell types/tissues, producing sets of genes, enhancers etc. which satisfy these constraints. Changes in slider settings result in simultaneous changes in the selected sets, updated in real time. SlideBase is linked to major databases from genomics consortia, including FANTOM, GTEx, The Human Protein Atlas and BioGPS. Database URL: http://slidebase.binf.ku.d

snoDB: An interconnected online database of human snoRNA

L’ARN est bien plus qu’une molécule transitoire entre l’ADN et les protéines. Au-delà des ARN encodant des protéines, on trouve un vaste éventail d’ARN non-codants qui demeurent encore sous-étudiés. Ces ARN ont été découverts dans les années 1960, mais ce n’est qu’au tournant du siècle que leur incroyable prévalence en cellule a pu être confirmée avec la venue de méthodes de séquençage d’ARN à haut débit. Les expériences à haut débit ont également augmenté de façon exponentielle la quantité de données sur l’ARN créant un besoin pour des outils bio-informatiques permettant leur analyse et leur stockage. Un des premiers, et des plus abondant, type d’ARN non-codant à être découvert sont les petit ARN nucléolaires (snoRNA). Canoniquement caractérisés comme guides de modifications spécifiques dans l’ARN ribosomal, ces petits ARN hautement conservés ont maintenant une liste variée de fonctions non-canoniques, notamment au niveau de l’expression génique, ainsi qu’un nombre croissant d’associations à une panoplie de maladies et de cancer. Considérant la littérature grandissante sur les snoRNA chez l’humain, ainsi que leur connexion maintenant apparente à plusieurs domaines de recherche variés, un regroupement accessible de ce large spectre d’information est maintenant indispensable. Malheureusement, les bases de données en ligne de snoRNA humain, snoRNABase, snOPY, et snoRNA Atlas, ne sont plus à jour ou sont trop pointues au niveau de leurs données. De plus, elles figurent peu ou pas de données d’interactions non-canonique et/ou d’expression. Nous avons donc créé snoDB : une base de données interactive de snoRNA humain qui contient des données sur leurs fonctions non-canoniques, trouvées à travers la littérature, des données d’expression dans une panoplie de tissus, et bien plus. Contrairement à ces prédécesseurs, snoDB offre une visualisions sélectives de son plus large éventail de données, au sein d’une table interactive aux options de recherche abondantes. Les données d’expression peuvent également être visualisées dans la même page, sous forme de carte de chaleur, grâce à l’application sœur de snoDB : snoTHAW. snoDB se démarque aussi par sa connectivité à plus d’une douzaine de ressources incluant le consortium RNAcentral, la plus grande base de données d’ARN non-codant, dont snoDB fais maintenant parti. Les données de ces ressources ont été acquises puis jointe ensemble dans une base de données relationnel postgreSQL. De plus, elles sont toutes en lien dans la table de snoDB afin de facilement pouvoir corroborer l’information visible, ainsi qu’accéder aux fonctionnalités des autres sites. Enfin, snoDB a été construit pour être facile à mettre à jour afin d’assurer ces contributions à la recherche pour de nombreuses années.Abstract: RNA is more than just a transitory molecule between DNA and proteins. Beyond the scope of protein-coding RNAs lies a vast underexplored landscape of non-coding RNAs (ncRNA). These RNAs have been slowly uncovered since the 1960s but it took until the turn of the century, and the advent of high-throughput RNA-Sequencing methodologies, for us to finally see how dominated by ncRNAs the transcriptome really is. High-throughput experiments also exponentially expanded the amount of data on RNA and created a need for bioinformatics tools for their analysis and storage. One of the first, and most abundant, ncRNA types to be discovered was small nucleolar RNAs (snoRNAs). Canonically pegged as guides for the modification of pre-ribosomal RNAs, these highly conserved RNAs now boast a diverse list of crucial non-canonical roles, notably in gene expression, as well as being associated to a myriad of diseases and cancers. Considering the growing body of literature surrounding snoRNAs in humans, and their increasing connections to a broad range of fields of study, having an accessible and comprehensive assessment of these data has become essential. Unfortunately, existing online human snoRNA databases, snoRNABase, snOPY, and snoRNA Atlas, are either outdated or too narrow in scope, focusing almost exclusively on canonical snoRNA interactions and lacking expression data. As such, we have created snoDB: a modern, interactive database of human snoRNAs with curated data on non-canonical snoRNA interactions, expression data in a growing range of tissues and cell lines, and more. Unlike the old snoRNA databases, snoDB features extensive visualisation and filtering capabilities, allowing for its larger array of data to be selectively viewed in an interactive and customizable table. Expression data can be further visualised in interactive heatmaps thanks to snoDB’s sister tool: snoTHAW. snoDB also innovates by being much more interconnected with other resources. Data was gathered, and joined together in a relational postgreSQL database, from over a dozen resources, including the RNAcentral database consortium, the largest database of ncRNA sequences, of which snoDB is now a part of. In addition, all resources are linked to in-table, where data they provided appears, to help corroborate the data shown for transparency, as well as to grant access to interesting features housed on remote sites. Finally, snoDB is built to be easily maintainable, updatable and extensible to keep up with ongoing developments and insure that the information it contains will contribute to snoRNA research for years to come