Virus detection in high-throughput sequencing data without a reference genome of the host

AI-enabled pipeline for virus detection, validation, and SNP discovery from next-generation sequencing data

Background and Aims

The rapid and accurate detection of viruses and the discovery of single nucleotide polymorphisms (SNPs) are critical for disease management and understanding viral evolution. This study presents a pipeline for virus detection, validation, and SNP discovery from next-generation sequencing (NGS) data. The pipeline processes raw sequencing data to identify viral sequences with high accuracy and sensitivity by integrating state-of-the-art bioinformatics tools with artificial intelligence.

Methods

Before aligning the reads to the reference genomes, quality control measures, and adapter trimming are performed to ensure the integrity of the data. Unmapped reads are subjected to de novo assembly to reveal novel viral sequences and genetic elements.

Results

The effectiveness of the pipeline is demonstrated by the identification of virus sequences, illustrating its potential for detecting known and emerging pathogens. SNP discovery is performed using a custom Python script that compares the entire population of sequenced viral reads to a reference genome. This approach provides a comprehensive overview of viral genetic diversity and identifies dominant variants and a spectrum of genetic variations.

Conclusion

The robustness of the pipeline is confirmed by the recovery of complete viral sequences, which improves our understanding of viral genomics. This research aims to develop an auto-bioinformatics pipeline for novel viral sequence discovery, in vitro validation, and SNPs using the Python (AI) language to understand viral evolution. This study highlights the synergy between traditional bioinformatics techniques and modern approaches, providing a robust tool for analyzing viral genomes and contributing to the broader field of viral genomics.

Interactive, Browser-Based Graphics to Visualize Complex Data in Education of Biomedical Sciences for Veterinary Students

In veterinary education, data from biomedical or natural sciences are mostly presented in the form of static or animated graphics with no or little amount of interactivity. These kinds of presentations are, however, often not sufficient to depict the complexity of the data or the presented topic. Interactive graphics, which allow to dynamically change data and related graphics, have rarely been considered as teaching tool in higher education of biomedical disciplines for veterinary education so far. In order to study the applicability and the usefulness of interactive graphics in biomedical disciplines for lecturers and students in veterinary education, three different courses from biomedical disciplines were exemplarily implemented as interactive graphics and evaluated in a pilot study by a survey amongst lecturers and students of our university. The interactive graphics were built using the Shiny environment, a web-based application framework for the statistic software R. The survey amongst lecturers and students was based on questionnaires covering questions on the handling and usefulness of the digital teaching tools. In total, n = 327 students and n = 5 lecturers participated in the evaluation study which revealed that the interactive graphics are easy to handle for lecturers and students, and that they can increase the motivation for either teaching or learning. In total, 71% of the students affirmed that interactive graphics led to an increased interest for the presented contents and 76% expressed the wish to get taught more topics with interactive graphics. We also provide a workflow that can be used as a guideline to develop interactive graphics.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40670-022-01613-x.

Clinical Application and Influencing Factor Analysis of Metagenomic Next-Generation Sequencing (mNGS) in ICU Patients With Sepsis

Objective

To analyze the clinical application and related influencing factors of metagenomic next-generation sequencing (mNGS) in patients with sepsis in intensive care unit (ICU).

Methods

The study included 124 patients with severe sepsis admitted to the ICU in the First Affiliated Hospital of Zhengzhou University from June 2020 to September 2021. Two experienced clinicians took blood mNGS and routine blood cultures of patients meeting the sepsis diagnostic criteria within 24 hours after sepsis was considered, and collection the general clinical data.

Results

mNGS positive rate was higher than traditional blood culture (67.74% vs. 19.35%). APACHE II score [odds ratio (OR)=1.096], immune-related diseases (OR=6.544), and hypertension (OR=2.819) were considered as positive independent factors for mNGS or culture-positive. The sequence number of microorganisms and pathogen detection (mNGS) type had no effect on prognosis. Age (OR=1.016), female (OR=5.963), myoglobin (OR=1.005), and positive virus result (OR=8.531) were independent risk factors of sepsis mortality. Adjusting antibiotics according to mNGS results, there was no statistical difference in the prognosis of patients with sepsis.

Conclusion

mNGS has the advantages of rapid and high positive rate in the detection of pathogens in patients with severe sepsis. Patients with high APACHE II score, immune-related diseases, and hypertension are more likely to obtain positive mNGS results. The effect of adjusting antibiotics according to mNGS results on the prognosis of sepsis needs to be further evaluated.

Heat Stress Resistance Mechanisms of Two Cucumber Varieties from Different Regions

High temperatures affect the yield and quality of vegetable crops. Unlike thermosensitive plants, thermotolerant plants have excellent systems for withstanding heat stress. This study evaluated various heat resistance indexes of the thermotolerant cucumber (TT) and thermosensitive cucumber (TS) plants at the seedling stage. The similarities and differences between the regulatory genes were assessed through transcriptome analysis to understand the mechanisms for heat stress resistance in cucumber. The TT plants exhibited enhanced leaf status, photosystem, root viability, and ROS scavenging under high temperature compared to the TS plants. Additionally, transcriptome analysis showed that the genes involved in photosynthesis, the chlorophyll metabolism, and defense responses were upregulated in TT plants but downregulated in TS plants. Zeatin riboside (ZR), brassinosteroid (BR), and jasmonic acid (JA) levels were higher in TT plants than in TS. The heat stress increased gibberellic acid (GA) and indoleacetic acid (IAA) levels in both plant lines; however, the level of GA was higher in TT. Correlation and interaction analyses revealed that heat cucumber heat resistance is regulated by a few transcription factor family genes and metabolic pathways. Our study revealed different phenotypic and physiological mechanisms of the heat response by the thermotolerant and thermosensitive cucumber plants. The plants were also shown to exhibit different expression profiles and metabolic pathways. The heat resistant pathways and genes of two cucumber varieties were also identified. These results enhance our understanding of the molecular mechanisms of cucumber response to high-temperature stress.

Correcting the Estimation of Viral Taxa Distributions in Next-Generation Sequencing Data after Applying Artificial Neural Networks

Estimating the taxonomic composition of viral sequences in a biological samples processed by next-generation sequencing is an important step in comparative metagenomics. Mapping sequencing reads against a database of known viral reference genomes, however, fails to classify reads from novel viruses whose reference sequences are not yet available in public databases. Instead of a mapping approach, and in order to classify sequencing reads at least to a taxonomic level, the performance of artificial neural networks and other machine learning models was studied. Taxonomic and genomic data from the NCBI database were used to sample labelled sequencing reads as training data. The fitted neural network was applied to classify unlabelled reads of simulated and real-world test sets. Additional auxiliary test sets of labelled reads were used to estimate the conditional class probabilities, and to correct the prior estimation of the taxonomic distribution in the actual test set. Among the taxonomic levels, the biological order of viruses provided the most comprehensive data base to generate training data. The prediction accuracy of the artificial neural network to classify test reads to their viral order was considerably higher than that of a random classification. Posterior estimation of taxa frequencies could correct the primary classification results.

A resampling strategy for studying robustness in virus detection pipelines

Next-generation sequencing is regularly used to identify viral sequences in DNA or RNA samples of infected hosts. A major step of most pipelines for virus detection is to map sequence reads against known virus genomes. Due to small differences between the sequences of related viruses, and due to several biological or technical errors, mapping underlies uncertainties. As a consequence, the resulting list of detected viruses can lack robustness. A new approach for generating artificial sequencing reads together with a strategy of resampling from the original findings is proposed that can help to assess the robustness of the originally identified list of viruses. From the original mapping result in form of a SAM file, a set of statistical distributions are derived. These are used in the resampling pipeline to generate new artificial reads which are again mapped versus the reference genomes. By summarizing the resampling procedure, the analyst receives information about whether the presence of a particular virus in the sample gains or losses evidence, and thus about the robustness of the original mapping list but also that of individual viruses in this list. To judge robustness, several indicators are derived from the resampling procedure such as the correlation between original and resampling read counts, or the statistical detection of outliers in the differences of read counts. Additionally, graphical illustrations of read count shifts via Sankey diagrams are provided. To demonstrate the use of the new approach, the resampling approach is applied to three real-world data samples, one of them with laboratory-confirmed Influenza sequences, and to artificially generated data where virus sequences have been spiked into the sequencing data of a host. By applying the resampling pipeline, several viruses drop from the original list while new viruses emerge, showing robustness of those viruses that remain in the list. The evaluation of the new approach shows that the resampling approach is helpful to analyze the viral content of a biological sample, to rate the robustness of original findings and to better show the overall distribution of findings. The method is also applicable to other virus detection pipelines based on read mapping.

Measuring reproducibility of virus metagenomics analyses using bootstrap samples from FASTQ-files

MOTIVATION

High-throughput sequencing data can be affected by different technical errors, e.g. from probe preparation or false base calling. As a consequence, reproducibility of experiments can be weakened. In virus metagenomics, technical errors can result in falsely identified viruses in samples from infected hosts. We present a new resampling approach based on bootstrap sampling of sequencing reads from FASTQ-files in order to generate artificial replicates of sequencing runs which can help to judge the robustness of an analysis. In addition, we evaluate a mixture model on the distribution of read counts per virus to identify potentially false positive findings.

RESULTS

The evaluation of our approach on an artificially generated dataset with known viral sequence content shows in general a high reproducibility of uncovering viruses in sequencing data, i.e. the correlation between original and mean bootstrap read count was highly correlated. However, the bootstrap read counts can also indicate reduced or increased evidence for the presence of a virus in the biological sample. We also found that the mixture-model fits well to the read counts, and furthermore, it provides a higher accuracy on the original or on the bootstrap read counts than on the difference between both. The usefulness of our methods is further demonstrated on two freely available real-world datasets from harbor seals.

AVAILABILITY AND IMPLEMENTATION

We provide a Phyton tool, called RESEQ, available from https://github.com/babaksaremi/RESEQ that allows efficient generation of bootstrap reads from an original FASTQ-file.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An evolutionary divergent pestivirus lacking the Npro gene systemically infects a whale species

Pestiviruses typically infect members of the order Artiodactyla, including ruminants and pigs, although putative rat and bat pestiviruses have also been described. In the present study, we identified and characterized an evolutionary divergent pestivirus in the toothed whale species, harbour porpoise (Phocoena phocoena). We tentatively named the virus Phocoena pestivirus (PhoPeV). PhoPeV displays a typical pestivirus genome organization except for the unique absence of N^pro, an N-terminal autoprotease that targets the innate host immune response. Evolutionary evidence indicates that PhoPeV emerged following an interspecies transmission event from an ancestral pestivirus that expressed N^pro. We show that 9% (n = 10) of stranded porpoises from the Dutch North Sea coast (n = 112) were positive for PhoPeV and they displayed a systemic infection reminiscent of non-cytopathogenic persistent pestivirus infection. The identification of PhoPeV extends the host range of pestiviruses to cetaceans (dolphins, whales, porpoises), which are considered to have evolved from artiodactyls (even-toed ungulates). Elucidation of the pathophysiology of PhoPeV infection and N^pro unique absence will add to our understanding of molecular mechanisms governing pestivirus pathogenesis.

2019 meeting of the global virus network

The Global Virus Network (GVN) was established in 2011 to strengthen research and responses to emerging viral causes of human disease and to prepare against new viral pandemics. There are now 52 GVN Centers of Excellence and 9 Affiliate laboratories in 32 countries. The 11th International GVN meeting was held from June 9-11, 2019 in Barcelona, Spain and was jointly organized with the Spanish Society of Virology. A common theme throughout the meeting was globalization and climate change. This report highlights the recent accomplishments of GVN researchers in several important areas of medical virology, including severe virus epidemics, anticipation and preparedness for changing disease dynamics, host-pathogen interactions, zoonotic virus infections, ethical preparedness for epidemics and pandemics, one health and antivirals.