DNA methylation plays a crucial role in higher organisms. Coupling bisulfite treatment with next generation sequencing enables the interrogation of 5-methylcytosine sites in the genome. However, bisulfite conversion introduces mismatches between the reads and the reference genome, which makes mapping of Illumina and SOLiD reads slow and inaccurate. BatMeth is an algorithm that integrates novel Mismatch Counting, List Filtering, Mismatch Stage Filtering and Fast Mapping onto Two Indexes components to improve unique mapping rate, speed and precision. Experimental results show that BatMeth is faster and more accurate than existing tools. BatMeth is freely available at http://code.google.com/p/batmeth/.

Lumpy skin disease virus (LSDV) is a member of the capripoxvirus (CPPV) genus of the Poxviridae family. LSDV is a rapidly emerging, high-consequence pathogen of cattle, recently spreading from Africa and the Middle East into Europe and Asia. We have sequenced the whole genome of historical LSDV isolates from the Pirbright Institute virus archive, and field isolates from recent disease outbreaks in Sri Lanka, Mongolia, Nigeria and Ethiopia. These genome sequences were compared to published genomes and classified into different subgroups. Two subgroups contained vaccine or vaccine-like samples (“Neethling-like” clade 1.1 and “Kenya-like” subgroup, clade 1.2.2). One subgroup was associated with outbreaks of LSD in the Middle East/Europe (clade 1.2.1) and a previously unreported subgroup originated from cases of LSD in west and central Africa (clade 1.2.3). Isolates were also identified that contained a mix of genes from both wildtype and vaccine samples (vaccine-like recombinants, grouped in clade 2). Whole genome sequencing and analysis of LSDV strains isolated from different regions of Africa, Europe and Asia have provided new knowledge of the drivers of LSDV emergence, and will inform future disease control strategies.

Many viruses that cause serious diseases in humans and animals, including the betacoronaviruses (beta-CoVs), such as SARS-CoV, MERS-CoV, and the recently identified SARS-CoV-2, have natural reservoirs in bats. Because these viruses rely entirely on the host cellular machinery for survival, their evolution is likely to be guided by the link between the codon usage of the virus and that of its host. As a result, specific cellular microenvironments of the diverse hosts and/or host tissues imprint peculiar molecular signatures in virus genomes. Our study is aimed at deciphering some of these signatures. Using a variety of genetic methods we demonstrated that trends in codon usage across chiroptera-hosted CoVs are collaboratively driven by geographically different host-species and temporal-spatial distribution. We not only found that chiroptera-hosted CoVs are the ancestors of SARS-CoV-2, but we also revealed that SARS-CoV-2 has the codon usage characteristics similar to those seen in CoVs infecting the Rhinolophus sp. Surprisingly, the envelope gene of beta-CoVs infecting Rhinolophus sp., including SARS-CoV-2, had extremely high CpG levels, which appears to be an evolutionarily conserved trait. The dissection of the furin cleavage site of various CoVs infecting hosts revealed host-specific preferences for arginine codons; however, arginine is encoded by a wider variety of synonymous codons in the murine CoV (MHV-A59) furin cleavage site. Our findings also highlight the latent diversity of CoVs in mammals that has yet to be fully explored.

Peste des petits ruminants virus (PPRV) causes a highly devastating disease of sheep and goats that threatens food security, small ruminant production and susceptible endangered wild ruminants. With policy directed towards achieving global PPR eradication, the establishment of cost-effective genomic surveillance tools is critical where PPR is endemic. Genomic data can provide sufficient in-depth information to identify the pockets of endemicity responsible for PPRV persistence and viral evolution, and direct an appropriate vaccination response. Yet, access to the required sequencing technology is low in resource-limited settings and is compounded by the difficulty of transporting clinical samples from wildlife across international borders due to the Convention on International Trade in Endangered Species (CITES) of Wild Fauna and Flora, and Nagoya Protocol regulations. Oxford nanopore MinION sequencing technology has recently demonstrated an extraordinary performance in the sequencing of PPRV due to its rapidity, utility in endemic countries and comparatively low cost per sample when compared to other whole-genome (WGS) sequencing platforms. In the present study, Oxford nanopore MinION sequencing was utilised to generate complete genomes of PPRV isolates collected from infected goats in Ngorongoro and Momba districts in the northern and southern highlands of Tanzania during 2016 and 2018, respectively. The tiling multiplex polymerase chain reaction (PCR) was carried out with twenty-five pairs of long-read primers. The resulting PCR amplicons were used for nanopore library preparation and sequencing. The analysis of output data was complete genomes of PPRV, produced within four hours of sequencing (accession numbers: MW960272 and MZ322753). Phylogenetic analysis of the complete genomes revealed a high nucleotide identity, between 96.19 and 99.24% with lineage III PPRV currently circulating in East Africa, indicating a common origin. The Oxford nanopore MinION sequencer can be deployed to overcome diagnostic and surveillance challenges in the PPR Global Control and Eradication program. However, the coverage depth was uneven across the genome and amplicon dropout was observed mainly in the GC-rich region between the matrix (M) and fusion (F) genes of PPRV. Thus, larger field studies are needed to allow the collection of sufficient data to assess the robustness of nanopore sequencing technology.

Current high-throughput sequencing technologies can generate sequence data and provide information on the genetic composition of samples at very high coverage. Deep sequencing approaches enable the detection of rare variants in heterogeneous samples, such as viral quasi-species, but also have the undesired effect of amplifying sequencing errors and artefacts. Distinguishing real variants from such noise is not straightforward. Variant callers that can handle pooled samples can be in trouble at extremely high read depths, while at lower depths sensitivity is often sacrificed to specificity. In this paper, we propose SiNPle (Simplified Inference of Novel Polymorphisms from Large coveragE), a fast and effective software for variant calling. SiNPle is based on a simplified Bayesian approach to compute the posterior probability that a variant is not generated by sequencing errors or PCR artefacts. The Bayesian model takes into consideration individual base qualities as well as their distribution, the baseline error rates during both the sequencing and the PCR stage, the prior distribution of variant frequencies and their strandedness. Our approach leads to an approximate but extremely fast computation of posterior probabilities even for very high coverage data, since the expression for the posterior distribution is a simple analytical formula in terms of summary statistics for the variants appearing at each site in the genome. These statistics can be used to filter out putative SNPs and indels according to the required level of sensitivity. We tested SiNPle on several simulated and real-life viral datasets to show that it is faster and more sensitive than existing methods. The source code for SiNPle is freely available to download and compile, or as a Conda/Bioconda package.

Obtaining a complete good-quality sequence and annotation for the long double-stranded DNA genome of the African swine fever virus (ASFV) from next-generation sequencing (NGS) technology has proven difficult, despite the increasing availability of reference genome sequences and the increasing affordability of NGS. A gap analysis conducted by the global African swine fever research alliance (GARA) partners identified that a standardized, automatic pipeline for NGS analysis was urgently needed, particularly for new outbreak strains. Whilst there are several diagnostic and research labs worldwide that collect isolates of the ASFV from outbreaks, many do not have the capability to analyze, annotate, and format NGS data from outbreaks for submission to NCBI, and some publicly available ASFV genomes have missing or incorrect annotations. We developed an automated, standardized pipeline for the analysis of NGS reads that directly provides users with assemblies and annotations formatted for their submission to NCBI. This pipeline is freely available on GitHub and has been tested through the GARA partners by examining two previously sequenced ASFV genomes; this study also aimed to assess the accuracy and limitations of two strategies present within the pipeline: reference-based (Illumina reads) and de novo assembly (Illumina and Nanopore reads) strategies.