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Although standards and guidelines for the interpretation of variants identified in genes that cause Mendelian disorders have been developed, this is not the case for more complex genetic models including variant combinations in multiple genes. During a large curation process conducted on 318 research articles presenting oligogenic variant combinations, we encountered several recurring issues concerning their proper reporting and pathogenicity assessment. These mainly concern the absence of strong evidence that refutes a monogenic model and the lack of a proper genetic and functional assessment of the joint effect of the involved variants. With the increasing accumulation of such cases, it has become essential to develop standards and guidelines on how these oligogenic/multilocus variant combinations should be interpreted, validated, and reported in order to provide high-quality data and supporting evidence to the scientific community.

The emergence of statistical and predictive methods able to analyse genomic data has revolutionised the field of medical genetics, allowing the identification of disease-causing gene variants (i.e. mutations) for several human genetic diseases. Although these approaches have greatly improved our understanding of Mendelian «one gene – one phenotype» genetic models, studying diseases related to more intricate models that involve causative variants in several genes (i.e. oligogenic diseases) still remains a challenge, either due to the lack of sufficient methodologies and disease-specific cohorts to study or the phenotypic complexity associated with such diseases. This situation makes it difficult to not only understand the genetic mechanisms of the disease, but to also offer proper counseling and support to the patient. Until recently, no specialized predictive methods existed to directly predict causative variant combinations for oligogenic diseases. However, with the advent of data on variant combinations in gene pairs (i.e. bilocus variant combinations) leading to disease, collected at the Digenic Diseases Database (DIDA), we hypothesized that the transition from single to variant combination pathogenicity predictors is now possible.To investigate this hypothesis, we organised our research on two main routes. At first, we developed an interpretable variant combination pathogenicity predictor, called VarCoPP, for gene pairs. For this goal, we trained multiple Random Forest algorithms on pathogenic bilocus variant combinations from DIDA against neutral data from the 1000 Genomes Project and investigated the contribution of the incorporated variant, gene and gene pair features to the prediction outcome. In the second part, we explored the usefulness of different gene pair burden scores based on this novel predictive method, in discovering oligogenic signatures in neurodevelopmental diseases, which involve a spectrum of monogenic to polygenic cases. We performed a preliminary analysis on the Deciphering Developmental Diseases (DDD) project containing exome data of 4195 families and assessed the capability of our scores in supporting already diagnosed monogenic cases, discovering significant pairs compared to control cases and linking patients in communities based on the genetic burden they share, using the Leiden community detection algorithm.The performance of VarCoPP shows that it is possible to predict disease-causing bilocus variant combinations with good accuracy both during cross-validation and when testing on new cases. We also show its relevance for disease-related gene panels, and enhanced its clinical applicability by defining confidence zones that guarantee with 95\% or 99\% probability that a prediction is indeed a true positive, guiding clinical researchers towards the most relevant results. This method and additional biological annotations are incorporated in an online platform called ORVAL that allows the prediction and exploration of candidate disease-causing oligogenic variant combinations with predicted gene networks, based on patient variant data. Our preliminary analysis on the DDD cohort shows that - although all bi-locus burden scores show advantages, disadvantages and certain types of biases - taking the maximum pathogenicity score present inside a gene pair seems to provide, at the moment, the most unbiased results. We also show that our predictive methods enable us to detect patient communities inside DDD, based exclusively on the shared pathogenic bi-locus burden between patients, with more than half of these communities containing enriched phenotypic and molecular pathway information. Our predictive method is also able to bring to the surface genes not officially known to be involved in disease, but nevertheless, with a biological relevance, as well as a few examples of potential oligogenicity inside the network, paving the way for further exploration of oligogenic signatures for neurodevelopmental diseases., Doctorat en Sciences, info:eu-repo/semantics/nonPublished

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”. Los Grupos de Estudiantes Regionales (RSG) del Consejo de Estudiantes de la Sociedad Internacional de Biología Computacional (ISCB-SC) han sido fundamentales para conectar a los biólogos computacionales a nivel mundial y crear más conciencia sobre la educación bioinformática. Este artículo destaca las iniciativas llevadas a cabo por los RSG tanto a nivel nacional como internacional para fortalecer el presente y el futuro de la comunidad bioinformática. Además, discutimos las direcciones futuras que tomará la organización y los desafíos para avanzar más en la misión principal de ISCB-SC: “Nutrir a la nueva generación de biólogos computacionales”. Iowa State University, Estados Unidos European Molecular Biology Laboratory, Alemania Uppsala University, Suecia University of Padova, Italia Institute of Tropical Medicine, Bélgica University of Antwerp, Bélgica University of KwaZulu Natal, Sudáfrica Universidade Federal de Minas Gerais, Brazil Universitat Autònoma de Barcelona, España European Bioinformatics Institute, Reino Unido Universidad de Antioquía, Colombia Universidad CES, Colombia University of South Carolina, Estados Unidos Mohammed V University in Rabat, Marruecos University of Cape Town, Sudáfrica Jawaharlal Nehru University, India Universidad Andres, Chile Universidad de Talca, Chile Université libre de Bruxelles-Vrije Universiteit Brusse, Bélgica Technical University of Denmark, Dinamarca University of Copenhagen, Dinamarca University of Luxembourg, Luxemburgo University of Lausanne, Suiza APIN Public Health Initiatives, Nigeria Institute of Ecology, Argentina Lund University, Suecia Universidad Nacional, Costa Rica Max Planck Institute for Biophysical Chemistry, Alemania Rhodes University, Sudáfrica Dokuz Eylül University, Turquía Hacettepe University, Turquía University Medical Center Rostock, Alemania University Heidelberg, Alemania La Jolla Institute for Immunology, Estados Unidos Katholieke Universiteit Leuven, Belgica Roche, Alemania Carnegie Mellon University, Estados Unidos University of South Wales, Reino Unido Canterbury Christ Church University, Reino Unido Escuela de Ciencias Biológicas

Au shell – (Au-Cu) core coatings on carbon microdisc electrodes (30 m diameter) have been prepared by a two-step technique whereby Cu particles electrodeposited onto carbon supports(first step) had their surface layers replaced by Au (second step). The latter has been achieved by means of spontaneous partial replacement of the non-precious metal deposits of Cu by Au upon their immersion in the chlorolaurate-based solution: 3 Cu/C + 2 AuCl4 2 Au (Cu)/C + 3 Cu2+ + 8 Cl . The Au-Cu coated microelectrodes, u(Cu)/C, were subsequently used for voltammetric determination of cystine and their performance compared to a bulk Au RDE. The voltammetric picture was similar at both electrodes. A peak corresponding to adsorbed cystine oxidation at ca. +1.2 V vs. Ag/AgCl and a plateau at ca. +1.6 V corresponding to cystine oxidation from the bulk solution under mass transfer control, were recorded. The latter permitted an estimate of the apparent number of electrons associated with cystine oxidation which were calculated to be around 5 in both cases. However, the peak current was found to be much higher at the Au-Cu microelectrodes, even after correction for increased roughness was taken into account. This increase is attributed to an electronic effect of Cu on Au which decreases the affinity of the latter for excessive oxide formation and/or poisonous carbonaceous intermediates of cystine oxidation. Both the peak and plateau currents varied linearly with the concentration range of 1 10-5 – 1 10-4 M cystine and, in the former case, with a detection limit (3 ) estimated to be about 2 10-6 M.

We report on the development of a simple amperometric method for monitoring of dissolved methanol in 0.5 M – 5 M solutions in sulfuric acid (relevant to methanol levels in the feed of direct methanol fuel cells) using bare Pt microdisc electrodes. The high mass transport rates of electroactive species to the microdisc from the solution phase results in a shift of the dissolved oxygen reduction wave to more negative potentials so that it does not interfere with the main methanol oxidation peak at sufficiently positive potentials (+0.5 - +0.7 V vs. Ag/AgCl). For continuous monitoring of methanol a potential sequence consisting of +1.6 V for 10s (cleaning pulse) / 0 V for 120 s (equilibration pulse) / +0.6 V for 180 s (measurement pulse) is applied to the indicator electrode, resulting in current-time transients that are reproducible upon periodic application for at least 8 h. There is a square root correlation between the oxidation current at +0.6 V and methanol concentration for the dissolved methanol sensor in the 0.5–3 M range, in accordance with theoretical predictions, thus excluding electrode poisoning/current saturation up to 3 M . The detection limit was estimated as 0.19 M making this simple method applicable for rough estimates and a crude monitoring of methanol levels.