ONTdeCIPHER: An amplicon-based nanopore sequencing pipeline for tracking pathogen variants.

MOTIVATION: Amplicon-based nanopore sequencing is increasingly used for molecular surveillance during epidemics (e.g. ZIKA, EBOLA) or pandemics (e.g. SARS-CoV-2). However, there is still a lack of versatile and easy-to-use tools that allow users with minimal bioinformatics skills to perform the main steps of downstream analysis, from quality testing to SNPs effect to phylogenetic analysis. RESULTS: Here, we present ONTdeCIPHER, an amplicon-based Oxford Nanopore Technology (ONT) sequencing pipeline to analyze the genetic diversity of SARS-CoV-2 and other pathogenes. Our pipeline integrates 13 bioinformatics tools. With a single command line and a simple configuration file, users can pre-process their data and obtain the sequencing statistics, reconstruct the consensus genome, identify variants and their effects for each viral isolate, infer lineage and, finally perform multi-sequence alignments and phylogenetic analyses. AVAILABILITY: ONTdeCIPHER is available at https://github.com/emiracherif/ONTdeCIPHER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Human-Altered Landscapes and Climate to Predict Human Infectious Disease Hotspots.

BACKGROUND: Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on the following three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses. METHODS: We modelled presence-absence data in spatially explicit binomial and zero-inflation binomial logistic regressions with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of the EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models. RESULTS: For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of the disease reservoirs and hosts of the three viral groups. Common influencing factors of disease emergence were climatic covariates (minimum temperature and rainfall) and human-induced land modifications. CONCLUSIONS: Using topographical, climatic, and previous disease outbreak reports, we can identify and predict future high-risk areas for disease emergence and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales.