Since its first release in 2007, GeneCodis has become a valuable tool to functionally interpret results from experimental techniques in genomics. This web-based application integrates different sources of information to finding groups of genes with similar biological meaning. This process, known as enrichment analysis, is essential in the interpretation of high-throughput experiments. The frequent feedbacks and the natural evolution of genomics and bioinformatics have allowed the growth of the tool and the development of this third release. In this version, a special effort has been made to remove noisy and redundant output from the enrichment results with the inclusion of a recently reported algorithm that summarizes significantly enriched terms and generates functionally coherent modules of genes and terms. A new comparative analysis has been added to allow the differential analysis of gene sets. To expand the scope of the application, new sources of biological information have been included, such as genetic diseases, drugs–genes interactions and Pubmed information among others. Finally, the graphic section has been renewed with the inclusion of new interactive graphics and filtering options. The application is freely available at http://genecodis.cnb.csic.es.

The CRISPR/Cas technology is enabling targeted genome editing in multiple organisms with unprecedented accuracy and specificity by using RNA-guided nucleases. A critical point when planning a CRISPR/Cas experiment is the design of the guide RNA (gRNA), which directs the nuclease and associated machinery to the desired genomic location. This gRNA has to fulfil the requirements of the nuclease and lack homology with other genome sites that could lead to off-target effects. Here we introduce the Breaking-Cas system for the design of gRNAs for CRISPR/Cas experiments, including those based in the Cas9 nuclease as well as others recently introduced. The server has unique features not available in other tools, including the possibility of using all eukaryotic genomes available in ENSEMBL (currently around 700), placing variable PAM sequences at 5′ or 3′ and setting the guide RNA length and the scores per nucleotides. It can be freely accessed at: http://bioinfogp.cnb.csic.es/tools/breakingcas, and the code is available upon request.

Current global change is fueling an interest to understand the genetic and molecular mechanisms of plant adaptation to climate. In particular, altered flowering time is a common strategy for escape from unfavourable climate temperature. In order to determine the genomic bases underlying flowering time adaptation to this climatic factor, we have systematically analysed a collection of 174 highly diverse Arabidopsis thaliana accessions from the Iberian Peninsula. Analyses of 1.88 million single nucleotide polymorphisms provide evidence for a spatially heterogeneous contribution of demographic and adaptive processes to geographic patterns of genetic variation. Mountains appear to be allele dispersal barriers, whereas the relationship between flowering time and temperature depended on the precise temperature range. Environmental genome-wide associations supported an overall genome adaptation to temperature, with 9.4% of the genes showing significant associations. Furthermore, phenotypic genome-wide associations provided a catalogue of candidate genes underlying flowering time variation. Finally, comparison of environmental and phenotypic genome-wide associations identified known (Twin Sister of FT, FRIGIDA-like 1, and Casein Kinase II Beta chain 1) and new (Epithiospecifer Modifier 1 and Voltage-Dependent Anion Channel 5) genes as candidates for adaptation to climate temperature by altered flowering time. Thus, this regional collection provides an excellent resource to address the spatial complexity of climate adaptation in annual plants.

BackgroundMicroRNAs are short RNA molecules that post-transcriptionally regulate gene expression. Today, microRNA target prediction remains challenging since very few have been experimentally validated and sequence-based predictions have large numbers of false positives. Furthermore, due to the different measuring rules used in each database of predicted interactions, the selection of the most reliable ones requires extensive knowledge about each algorithm.ResultsHere we propose two methods to measure the confidence of predicted interactions based on experimentally validated information. The output of the methods is a combined database where new scores and statistical confidences are re-assigned to each predicted interaction. The new scores allow the robust combination of several databases without the effect of low-performing algorithms dragging down good-performing ones. The combined databases obtained using both algorithms described in this paper outperform each of the existing predictive algorithms that were considered for the combination.ConclusionsOur approaches are a useful way to integrate predicted interactions from different databases. They reduce the selection of interactions to a unique database based on an intuitive score and allow comparing databases between them.