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RNA viruses are a major threat to animals and plants. RNA interference (RNAi) and the interferon response provide innate antiviral defense against RNA viruses. Here, we performed a large-scale screen using Caenorhabditis elegans and its natural pathogen the Orsay virus (OrV), and we identified cde-1 as important for antiviral defense. CDE-1 is a homolog of the mammalian TUT4 and TUT7 terminal uridylyltransferases (collectively called TUT4(7)); its catalytic activity is required for its antiviral function. CDE-1 uridylates the 3ʹ end of the OrV RNA genome and promotes its degradation in a manner independent of the RNAi pathway. Likewise, TUT4(7) enzymes uridylate influenza A virus (IAV) mRNAs in mammalian cells. Deletion of TUT4(7) leads to increased IAV mRNA and protein levels. Collectively, these data implicate 3ʹ-terminal uridylation of viral RNAs as a conserved antiviral defense mechanism.

We present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu_cnio/beyondcell .

Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter‐ or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next‐generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi‐omics strategies. We also describe intratumour dissection through clonal inference and single‐cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice. Intertumour heterogeneity and intratumour heterogeneity have been revealed as key causes of treatment failure and relapse. This review aims to provide a bioinformatics roadmap and general guidelines to propose anticancer data‐driven treatment strategies for bulk and single‐cell omics data in order to target tumour heterogeneity. Moreover, we discuss how these workflows could be integrated into the clinical practice.

Increasingly large numbers of proteins require methods for functional annotation. This is typically based on pairwise inference from the homology of either protein sequence or structure. Recently, similarity networks have been presented to leverage both the ability to visualize relationships between proteins and assess the transferability of functional inference. Here we present PANADA, a novel toolkit for the visualization and analysis of protein similarity networks in Cytoscape. Networks can be constructed based on pairwise sequence or structural alignments either on a set of proteins or, alternatively, by database search from a single sequence. The Panada web server, executable for download and examples and extensive help files are available at URL: http://protein.bio.unipd.it/panada/.

  There are a small number of articles that have been brought up to the highest levels of quality (Featured Article and Good Article) such as Multiple Sequence Alignment, Genome Wide Association Study, and Folding@home. About ISCB The International Society for Computational Biology (ISCB) - www.iscb.org - is the first, and continues to be the only, society representing computational biology and bioinformatics on a worldwide scale.

Abstract Alternative splicing of messenger RNA can generate an array of mature transcripts, but it is not clear how many go on to produce functionally relevant protein isoforms. There is only limited evidence for alternative proteins in proteomics analyses and data from population genetic variation studies indicate that most alternative exons are evolving neutrally. Determining which transcripts produce biologically important isoforms is key to understanding isoform function and to interpreting the real impact of somatic mutations and germline variations. Here we have developed a method, TRIFID, to classify the functional importance of splice isoforms. TRIFID was trained on isoforms detected in large-scale proteomics analyses and distinguishes these biologically important splice isoforms with high confidence. Isoforms predicted as functionally important by the algorithm had measurable cross species conservation and significantly fewer broken functional domains. Additionally, exons that code for these functionally important protein isoforms are under purifying selection, while exons from low scoring transcripts largely appear to be evolving neutrally. TRIFID has been developed for the human genome, but it could in principle be applied to other well-annotated species. We believe that this method will generate valuable insights into the cellular importance of alternative splicing.

Post-translational modification of protein lysines was recently shown to be a common feature of eukaryotic organisms. The ubiquitin modification is regarded as a versatile regulatory mechanism with many important cellular roles. Large-scale datasets are becoming available for H. sapiens ubiquitination. However, using current experimental techniques the vast majority of their sites remain unidentified and in silico tools may offer an alternative. Here, we introduce Rapid UBIquitination (RUBI) a sequence-based ubiquitination predictor designed for rapid application on a genome scale. RUBI was constructed using an iterative approach. At each iteration, important factors which influenced performance and its usability were investigated. The final RUBI model has an AUC of 0.868 on a large cross-validation set and is shown to outperform other available methods on independent sets. Predicted intrinsic disorder is shown to be weakly anti-correlated to ubiquitination for the H. sapiens dataset and improves performance slightly. RUBI predicts the number of ubiquitination sites correctly within three sites for ca. 80 % of the tested proteins. The average potentially ubiquitinated proteome fraction is predicted to be at least 25 % across a variety of model organisms, including several thousand possible H. sapiens proteins awaiting experimental characterization. RUBI can accurately predict ubiquitination on unseen examples and has a signal across different eukaryotic organisms. The factors which influenced the construction of RUBI could also be tested in other post-translational modification predictors. One of the more interesting factors is the influence of intrinsic protein disorder on ubiquitinated lysines where residues with low disorder probability are preferred.

Background Intrinsic protein disorder is becoming an increasingly important topic in protein science. During the last few years, intrinsically disordered proteins (IDPs) have been shown to play a role in many important biological processes, e.g. protein signalling and regulation. This has sparked a need to better understand and characterize different types of IDPs, their functions and roles. Our recently published database, MobiDB, provides a centralized resource for accessing and analysing intrinsic protein disorder annotations. Results Here, we present a thorough description and analysis of the data made available by MobiDB, providing descriptive statistics on the various available annotation sources. Version 1.2.1 of the database contains annotations for ca. 4,500,000 UniProt sequences, covering all eukaryotic proteomes. In addition, we describe a novel consensus annotation calculation and its related weighting scheme. The comparison between disorder information sources highlights how the MobiDB consensus captures the main features of intrinsic disorder and correlates well with manually curated datasets. Finally, we demonstrate the annotation of 13 eukaryotic model organisms through MobiDB's datasets, and of an example protein through the interactive user interface. Conclusions MobiDB is a central resource for intrinsic disorder research, containing both experimental data and predictions. In the future it will be expanded to include additional information for all known proteins.

This report summarizes the scientific content and activities of the annual symposium organized by the Student Council of the International Society for Computational Biology (ISCB), held in conjunction with the Intelligent Systems for Molecular Biology (ISMB) conference in Boston, USA, on July 11 th , 2014.

This report summarizes the scientific content and activities of the annual symposium organized by the Student Council of the International Society for Computational Biology (ISCB), held in conjunction with the Intelligent Systems for Molecular Biology (ISMB) / European Conference on Computational Biology (ECCB) conference in Berlin, Germany, on July 19, 2013.