MOTIVATION: The circulating recombinant form of HIV-1 CRF02-AG is the most frequent non-B subtype in Europe. Anti-HIV therapy and pathophysiological studies on the impact of HIV-1 tropism require genotypic determination of HIV-1 tropism for non-B subtypes. But genotypic approaches based on analysis of the V3 envelope region perform poorly when used to determine the tropism of CRF02-AG. We, therefore, designed an algorithm based on information from the gp120 and gp41 ectodomain that better predicts the tropism of HIV-1 subtype CRF02-AG. RESULTS: We used a bio-statistical method to identify the genotypic determinants of CRF02-AG coreceptor use. Toulouse HIV Extended Tropism Algorithm (THETA), based on a Least Absolute Shrinkage and Selection Operator method, uses HIV envelope sequence from phenotypically characterized clones. Prediction of R5X4/X4 viruses was 86% sensitive and that of R5 viruses was 89% specific with our model. The overall accuracy of THETA was 88%, making it sufficiently reliable for predicting the tropism of subtype CRF02-AG sequences. AVAILABILITY AND IMPLEMENTATION: Binaries are freely available for download at https://github.com/viro-tls/THETA. It was implemented in Matlab and supported on MS Windows platform. The sequence data used in this work are available from GenBank under the accession numbers MK618182-MK618417.

MOTIVATION: Allelic imbalance (AI), i.e. the unequal expression of the alleles of the same gene in a single cell, affects a subset of genes in diploid organisms. One prominent example of AI is parental genomic imprinting, which results in parent-of-origin-dependent, mono-allelic expression of a limited number of genes in metatherian and eutherian mammals and in angiosperms. Currently available methods for identifying AI rely on data modeling and come with the associated limitations. RESULTS: We have designed ISoLDE (Integrative Statistics of alleLe Dependent Expression), a novel nonparametric statistical method that takes into account both AI and the characteristics of RNA-seq data to infer allelic expression bias when at least two biological replicates are available for reciprocal crosses. ISoLDE learns the distribution of a specific test statistic from the data and calls genes 'allelically imbalanced', 'bi-allelically expressed' or 'undetermined'. Depending on the number of replicates, predefined thresholds or permutations are used to make calls. We benchmarked ISoLDE against published methods, and showed that ISoLDE compared favorably with respect to sensitivity, specificity and robustness to the number of replicates. Using ISoLDE on different RNA-seq datasets generated from hybrid mouse tissues, we did not discover novel imprinted genes (IGs), confirming the most conservative estimations of IG number. AVAILABILITY AND IMPLEMENTATION: ISoLDE has been implemented as a Bioconductor package available at http://bioconductor.org/packages/ISoLDE/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com.