Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE). A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions. The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks.

Sequencing of multiple related species followed by comparative genomics analysis constitutes a powerful approach for the systematic understanding of any genome. Here, we use the genomes of 12 Drosophila species for the de novo discovery of functional elements in the fly. Each type of functional element shows characteristic patterns of change, or ‘evolutionary signatures’, dictated by its precise selective constraints. Such signatures enable recognition of new protein-coding genes and exons, spurious and incorrect gene annotations, and numerous unusual gene structures, including abundant stop-codon readthrough. Similarly, we predict non-protein-coding RNA genes and structures, and new microRNA (miRNA) genes. We provide evidence of miRNA processing and functionality from both hairpin arms and both DNA strands. We identify several classes of pre- and post-transcriptional regulatory motifs, and predict individual motif instances with high confidence. We also study how discovery power scales with the divergence and number of species compared, and we provide general guidelines for comparative studies.

The web resource Regulatory Sequence Analysis Tools (RSAT) (http://rsat.ulb.ac.be/rsat) offers a collection of software tools dedicated to the prediction of regulatory sites in non-coding DNA sequences. These tools include sequence retrieval, pattern discovery, pattern matching, genome-scale pattern matching, feature-map drawing, random sequence generation and other utilities. Alternative formats are supported for the representation of regulatory motifs (strings or position-specific scoring matrices) and several algorithms are proposed for pattern discovery. RSAT currently holds >100 fully sequenced genomes and these data are regularly updated from GenBank.

A novel coronavirus, named SARS-CoV-2, emerged in 2019 in China and rapidly spread worldwide. As no approved therapeutics exists to treat COVID-19, the disease associated to SARS-Cov-2, there is an urgent need to propose molecules that could quickly enter into clinics. Repurposing of approved drugs is a strategy that can bypass the time-consuming stages of drug development. In this study, we screened the PRESTWICK CHEMICAL LIBRARY composed of 1,520 approved drugs in an infected cell-based assay. The robustness of the screen was assessed by the identification of drugs that already demonstrated in vitro antiviral effect against SARS-CoV-2. Thereby, 90 compounds were identified as positive hits from the screen and were grouped according to their chemical composition and their known therapeutic effect. Then EC50 and CC50 were determined for a subset of 15 compounds from a panel of 23 selected drugs covering the different groups. Eleven compounds such as macrolides antibiotics, proton pump inhibitors, antiarrhythmic agents or CNS drugs emerged showing antiviral potency with 2 < EC50 ≤ 20 µM. By providing new information on molecules inhibiting SARS-CoV-2 replication in vitro, this study provides information for the selection of drugs to be further validated in vivo. Disclaimer: This study corresponds to the early stages of antiviral development and the results do not support by themselves the use of the selected drugs to treat SARS-CoV-2 infection.