The European Genome-phenome Archive in 2021.

The European Genome-phenome Archive (EGA - https://ega-archive.org/) is a resource for long term secure archiving of all types of potentially identifiable genetic, phenotypic, and clinical data resulting from biomedical research projects. Its mission is to foster hosted data reuse, enable reproducibility, and accelerate biomedical and translational research in line with the FAIR principles. Launched in 2008, the EGA has grown quickly, currently archiving over 4,500 studies from nearly one thousand institutions. The EGA operates a distributed data access model in which requests are made to the data controller, not to the EGA, therefore, the submitter keeps control on who has access to the data and under which conditions. Given the size and value of data hosted, the EGA is constantly improving its value chain, that is, how the EGA can contribute to enhancing the value of human health data by facilitating its submission, discovery, access, and distribution, as well as leading the design and implementation of standards and methods necessary to deliver the value chain. The EGA has become a key GA4GH Driver Project, leading multiple development efforts and implementing new standards and tools, and has been appointed as an ELIXIR Core Data Resource.

Predicting novel metabolic pathways through subgraph mining.

MOTIVATION: The ability to predict pathways for biosynthesis of metabolites is very important in metabolic engineering. It is possible to mine the repertoire of biochemical transformations from reaction databases, and apply the knowledge to predict reactions to synthesize new molecules. However, this usually involves a careful understanding of the mechanism and the knowledge of the exact bonds being created and broken. There is a need for a method to rapidly predict reactions for synthesizing new molecules, which relies only on the structures of the molecules, without demanding additional information such as thermodynamics or hand-curated reactant mapping, which are often hard to obtain accurately. RESULTS: We here describe a robust method based on subgraph mining, to predict a series of biochemical transformations, which can convert between two (even previously unseen) molecules. We first describe a reliable method based on subgraph edit distance to map reactants and products, using only their chemical structures. Having mapped reactants and products, we identify the reaction centre and its neighbourhood, the reaction signature, and store this in a reaction rule network. This novel representation enables us to rapidly predict pathways, even between previously unseen molecules. We demonstrate this ability by predicting pathways to molecules not present in the KEGG database. We also propose a heuristic that predominantly recovers natural biosynthetic pathways from amongst hundreds of possible alternatives, through a directed search of the reaction rule network, enabling us to provide a reliable ranking of the different pathways. Our approach scales well, even to databases with >100 000 reactions. AVAILABILITY AND IMPLEMENTATION: A Java-based implementation of our algorithms is available at https://github.com/RamanLab/ReactionMiner. CONTACT: sayanranu@cse.iitd.ac.in or kraman@iitm.ac.in. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rare Variant, Gene-Based Association Study of Hereditary Melanoma Using Whole-Exome Sequencing.

Background: Extraordinary progress has been made in our understanding of common variants in many diseases, including melanoma. Because the contribution of rare coding variants is not as well characterized, we performed an exome-wide, gene-based association study of familial cutaneous melanoma (CM) and ocular melanoma (OM). Methods: Using 11 990 jointly processed individual DNA samples, whole-exome sequencing was performed, followed by large-scale joint variant calling using GATK (Genome Analysis ToolKit). PLINK/SEQ was used for statistical analysis of genetic variation. Four models were used to estimate the association among different types of variants. In vitro functional validation was performed using three human melanoma cell lines in 2D and 3D proliferation assays. In vivo tumor growth was assessed using xenografts of human melanoma A375 melanoma cells in nude mice (eight mice per group). All statistical tests were two-sided. Results: Strong signals were detected for CDKN2A (Pmin = 6.16 × 10-8) in the CM cohort (n = 273) and BAP1 (Pmin = 3.83 × 10-6) in the OM (n = 99) cohort. Eleven genes that exhibited borderline association (P < 10-4) were independently validated using The Cancer Genome Atlas melanoma cohort (379 CM, 47 OM) and a matched set of 3563 European controls with CDKN2A (P = .009), BAP1 (P = .03), and EBF3 (P = 4.75 × 10-4), a candidate risk locus, all showing evidence of replication. EBF3 was then evaluated using germline data from a set of 132 familial melanoma cases and 4769 controls of UK origin (joint P = 1.37 × 10-5). Somatically, loss of EBF3 expression correlated with progression, poorer outcome, and high MITF tumors. Functionally, induction of EBF3 in melanoma cells reduced cell growth in vitro, retarded tumor formation in vivo, and reduced MITF levels. Conclusions: The results of this large rare variant germline association study further define the mutational landscape of hereditary melanoma and implicate EBF3 as a possible CM predisposition gene.

Bayesian identification of bacterial strains from sequencing data.

Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an environmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at https://github.com/PROBIC/BIB.