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The full genome sequences of two isolates of bluetongue virus (BTV) from a commercial sheeppox vaccine were determined. Strain SPvvvv/02 shows low sequence identity to its closest relative, strain BTV-26 KUW2010/02, indicating the probable detection of a novel BTV genotype, whereas strain SPvvvv/03 shows high sequence identity to strain BTV-28/1537/14.

Background Hepatocellular carcinoma is the second most deadly cancer with late presentation and limited treatment options, highlighting an urgent need to better understand HCC to facilitate the identification of early-stage biomarkers and uncover therapeutic targets for the development of novel therapies for HCC. Methods Deep transcriptome sequencing of tumor and paired non-tumor liver tissues was performed to comprehensively evaluate the profiles of both the host and HBV transcripts in HCC patients. Differential gene expression patterns and the dys-regulated genes associated with clinical outcomes were analyzed. Somatic mutations were identified from the sequencing data and the deleterious mutations were predicted. Lastly, human-HBV chimeric transcripts were identified, and their distribution, potential function and expression association were analyzed. Results Expression profiling identified the significantly upregulated TP73 as a nodal molecule modulating expression of apoptotic genes. Approximately 2.5% of dysregulated genes significantly correlated with HCC clinical characteristics. Of the 110 identified genes, those involved in post-translational modification, cell division and/or transcriptional regulation were upregulated, while those involved in redox reactions were downregulated in tumors of patients with poor prognosis. Mutation signature analysis identified that somatic mutations in HCC tumors were mainly non-synonymous, frequently affecting genes in the micro-environment and cancer pathways. Recurrent mutations occur mainly in ribosomal genes. The most frequently mutated genes were generally associated with a poorer clinical prognosis. Lastly, transcriptome sequencing suggest that HBV replication in the tumors of HCC patients is rare. HBV-human fusion transcripts are a common observation, with favored HBV and host insertion sites being the HBx C-terminus and gene introns (in tumors) and introns/intergenic-regions (in non-tumors), respectively. HBV-fused genes in tumors were mainly involved in RNA binding while those in non-tumors tissues varied widely. These observations suggest that while HBV may integrate randomly during chronic infection, selective expression of functional chimeric transcripts may occur during tumorigenesis. Conclusions Transcriptome sequencing of HCC patients reveals key cancer molecules and clinically relevant pathways deregulated/mutated in HCC patients and suggests that while HBV may integrate randomly during chronic infection, selective expression of functional chimeric transcripts likely occur during the process of tumorigenesis.

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.

Epizootic hemorrhagic disease virus (EHDV) is a Culicoides-transmitted orbivirus that infects domestic and wild ruminants in many parts of the world. Of the eight proposed serotypes, only EHDV-1, 2 and 6 have been reported to be present in the Americas. Following the identification of a virulent EHD-6 reasssortant virus in the USA in 2007 (EHDV-6 Indiana), with outer coat protein segments derived from an Australian strain of EHDV and all remaining segments derived from a locally circulating EHDV-2 strain, questions have remained about the origin of the Australian parent strain and how it may have arrived in the USA. When EHDV-6 was identified in asymptomatic cattle imported into the Caribbean island of Trinidad in 2013, full genome sequencing was carried out to further characterise the virus. The EHDV-6 Trinidad was a reassortant virus, with 8 of its 10 segments, being derived from the same exotic Australian EHDV-6 strain as the VP2 and VP5 present in the EHDV-6 Indiana strain from the USA. Analyses of the two remaining segments revealed that segment 8 showed the highest nucleotide identity (90.4%) with a USA New Jersey strain of EHDV-1, whereas segment 4 had the highest nucleotide identity (96.5%) with an Australian EHDV-2 strain. This data strongly suggests that the Trinidad EHDV-6 has an Australian origin, receiving its segment 4 from a reassortment event with an EHDV-2 also from Australia. This reassortant virus likely came to the Americas, where it received its segment 8 from a locally-circulating (as yet unknown) EHDV strain. This virus then may have gained entry into the USA, where it further reassorted with a known locally-circulating EHDV-2, the resulting strain being EHDV-6 Indiana. This study therefore identifies, for the first time, the likely minor parent virus of the EHDV-6 currently circulating in the USA., Highlights • A unique reassortant EHD-6 was circulating asymptomatically in Trinidad in 2013 • Nine of the ten segments making up the EHD-6 Trinidad virus had a likely Australian origin • The EHDV-6 Trinidad is likely to be the minor parent virus of the EHDV-6 Indiana strain currently circulating in the USA.

Background There are a large number of biological databases publicly available for scientists in the web. Also, there are many private databases generated in the course of research projects. These databases are in a wide variety of formats. Web standards have evolved in the recent times and semantic web technologies are now available to interconnect diverse and heterogeneous sources of data. Therefore, integration and querying of biological databases can be facilitated by techniques used in semantic web. Heterogeneous databases can be converted into Resource Description Format (RDF) and queried using SPARQL language. Searching for exact queries in these databases is trivial. However, exploratory searches need customized solutions, especially when multiple databases are involved. This process is cumbersome and time consuming for those without a sufficient background in computer science. In this context, a search engine facilitating exploratory searches of databases would be of great help to the scientific community. Results We present BioCarian, an efficient and user-friendly search engine for performing exploratory searches on biological databases. The search engine is an interface for SPARQL queries over RDF databases. We note that many of the databases can be converted to tabular form. We first convert the tabular databases to RDF. The search engine provides a graphical interface based on facets to explore the converted databases. The facet interface is more advanced than conventional facets. It allows complex queries to be constructed, and have additional features like ranking of facet values based on several criteria, visually indicating the relevance of a facet value and presenting the most important facet values when a large number of choices are available. For the advanced users, SPARQL queries can be run directly on the databases. Using this feature, users will be able to incorporate federated searches of SPARQL endpoints. We used the search engine to do an exploratory search on previously published viral integration data and were able to deduce the main conclusions of the original publication. BioCarian is accessible via http://www.biocarian.com. Conclusions We have developed a search engine to explore RDF databases that can be used by both novice and advanced users. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1840-4) contains supplementary material, which is available to authorized users.

The number of biological databases available both in the public domain and in private keep on increasing every day. Scientists and researchers need to analyze and make use of the data stored in different databases. One limitation is that these databases are stored in diverse formats. However, semantic web methods have introduced the Resource description format (RDF) to unify heterogeneous databases. In this paper we illustrate how to construct a knowledge graph out of biological RDF databases by connecting possibly related data. We also show how the resulting knowledge graph can be made compact. We, then show how free-text search can be implemented to access nodes in the knowledge graph. Finally, we introduce a way to display and navigate the created knowledge graph.

The study of virus integrations in human genome is important since virus integrations were shown to be associated with diseases. In the literature, few methods have been proposed that predict virus integrations using next generation sequencing datasets. Although they work, they are slow and are not very sensitive. This paper introduces a new method BatVI to predict viral integrations. Our method uses a fast screening method to filter out chimeric reads containing possible viral integrations. Next, sensitive alignments of these candidate chimeric reads are called by BLAST. Chimeric reads that are co-localized in the human genome are clustered. Finally, by assembling the chimeric reads in each cluster, high confident virus integration sites are extracted. We compared the performance of BatVI with existing methods VirusFinder and VirusSeq using both simulated and real-life datasets of liver cancer patients. BatVI ran an order of magnitude faster and was able to predict almost twice the number of true positives compared to other methods while maintaining a false positive rate less than 1%. For the liver cancer datasets, BatVI uncovered novel integrations to two important genes TERT and MLL4, which were missed by previous studies. Through gene expression data, we verified the correctness of these additional integrations. BatVI can be downloaded from http://biogpu.ddns.comp.nus.edu.sg/~ksung/batvi/index.html .

There are over 1600 publicly available biological databases, and this number is growing at a steady rate. These databases are not in a uniform format, however, web-based technologies can be employed to integrate them. One of the key steps in enabling semantic web techniques is to convert these databases into a uniform format, for example, that of the Resource Description Framework (RDF). There are already several biological databases available in RDF format. However, some of the major databases are only found in custom formats. In this paper, we review the methods that are available to explore biological databases in RDF format. We also review current projects that facilitate the conversion of biological data into RDF format. We will identify the strengths and weaknesses of the current exploratory methods and suggest improvements that will enable both novice and expert users to search biological databases more effectively.