Detecting virus integration sites based on multiple related sequencing data by VirTect

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression

There are an estimated 300,000 mammalian viruses from which infectious diseases in humans may arise. They inhabit human tissues such as the lungs, blood, and brain and often remain undetected. Efficient and accurate detection of viral infection is vital to understanding its impact on human health and to make accurate predictions to limit adverse effects, such as future epidemics. The increasing use of high-throughput sequencing methods in research, agriculture, and healthcare provides an opportunity for the cost-effective surveillance of viral diversity and investigation of virus-disease correlation. However, existing methods for identifying viruses in sequencing data rely on and are limited to reference genomes or cannot retain single-cell resolution through cell barcode tracking. We introduce a method that accurately and rapidly detects viral sequences in bulk and single-cell transcriptomics data based on highly conserved amino acid domains, which enables the detection of RNA viruses covering up to 1012 virus species. The analysis of viral presence and host gene expression in parallel at single-cell resolution allows for the characterization of host viromes and the identification of viral tropism and host responses. We applied our method to identify putative novel viruses in rhesus macaque PBMC data that display cell type specificity and whose presence correlates with altered host gene expression.

Detection of viral infection in cell lines using ViralCellDetector

Cell lines are commonly used in research to study biology, including gene expression regulation, cancer progression, and drug responses. However, cross-contaminations with bacteria, mycoplasma, and viruses are common issues in cell line experiments. Detection of bacteria and mycoplasma infections in cell lines is relatively easy but identifying viral infections in cell lines is difficult. Currently, there are no established methods or tools available for detecting viral infections in cell lines. To address this challenge, we developed a tool called ViralCellDetector that detects viruses through mapping RNA-seq data to a library of virus genome. Using this tool, we observed that around 10% of experiments with the MCF7 cell line were likely infected with viruses. Furthermore, to facilitate the detection of samples with unknown sources of viral infection, we identified the differentially expressed genes involved in viral infection from two different cell lines and used these genes in a machine learning approach to classify infected samples based on the host response gene expression biomarkers. Our model reclassifies the infected and non-infected samples with an AUC of 0.91 and an accuracy of 0.93. Overall, our mapping- and marker-based approaches can detect viral infections in any cell line simply based on readily accessible RNA-seq data, allowing researchers to avoid the use of unintentionally infected cell lines in their studies.

VIS Atlas: A Database of Virus Integration Sites in Human Genome from NGS Data to Explore Integration Patterns

Integration of oncogenic DNA viruses into the human genome is a key step in most virus-induced carcinogenesis. Here, we constructed a virus integration site (VIS) Atlas database, an extensive collection of integration breakpoints for three most prevalent oncoviruses, human papillomavirus, hepatitis B virus, and Epstein-Barr virus based on the next-generation sequencing (NGS) data, literature, and experimental data. There are 63,179 breakpoints and 47,411 junctional sequences with full annotations deposited in the VIS Atlas database, comprising 47 virus genotypes and 17 disease types. The VIS Atlas database provides (1) a genome browser for NGS breakpoint quality check, visualization of VISs, and the local genomic context; (2) a novel platform to discover integration patterns; and (3) a statistics interface for a comprehensive investigation of genotype-specific integration features. Data collected in the VIS Atlas aid to provide insights into virus pathogenic mechanisms and the development of novel antitumor drugs. The VIS Atlas database is available at https://www.vis-atlas.tech/.

Virus finding tools: current solutions and limitations

MOTIVATION

The study of the Human Virome remains challenging nowadays. Viral metagenomics, through high-throughput sequencing data, is the best choice for virus discovery. The metagenomics approach is culture-independent and sequence-independent, helping search for either known or novel viruses. Though it is estimated that more than 40% of the viruses found in metagenomics analysis are not recognizable, we decided to analyze several tools to identify and discover viruses in RNA-seq samples.

RESULTS

We have analyzed eight Virus Tools for the identification of viruses in RNA-seq data. These tools were compared using a synthetic dataset of 30 viruses and a real one. Our analysis shows that no tool succeeds in recognizing all the viruses in the datasets. So we can conclude that each of these tools has pros and cons, and their choice depends on the application domain.

AVAILABILITY

Synthetic data used through the review and raw results of their analysis can be found at https://zenodo.org/record/6426147. FASTQ files of real data can be found in GEO (https://www.ncbi.nlm.nih.gov/gds) or ENA (https://www.ebi.ac.uk/ena/browser/home). Raw results of their analysis can be downloaded from https://zenodo.org/record/6425917.

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.

Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data

The integration of viruses into the human genome is known to be associated with tumorigenesis in many cancers, but the accurate detection of integration breakpoints from short read sequencing data is made difficult by human-viral homologies, viral genome heterogeneity, coverage limitations, and other factors. To address this, we present Exogene, a sensitive and efficient workflow for detecting viral integrations from paired-end next generation sequencing data. Exogene's read filtering and breakpoint detection strategies yield integration coordinates that are highly concordant with long read validation. We demonstrate this concordance across 6 TCGA Hepatocellular carcinoma (HCC) tumor samples, identifying integrations of hepatitis B virus that are also supported by long reads. Additionally, we applied Exogene to targeted capture data from 426 previously studied HCC samples, achieving 98.9% concordance with existing methods and identifying 238 high-confidence integrations that were not previously reported. Exogene is applicable to multiple types of paired-end sequence data, including genome, exome, RNA-Seq and targeted capture.

VIRUSBreakend: Viral Integration Recognition Using Single Breakends

Motivation

Integration of viruses into infected host cell DNA can cause DNA damage and disrupt genes. Recent cost reductions and growth of whole genome sequencing has produced a wealth of data in which viral presence and integration detection is possible. While key research and clinically relevant insights can be uncovered, existing software has not achieved widespread adoption, limited in part due to high computational costs, the inability to detect a wide range of viruses, as well as precision and sensitivity.

Results

Here, we describe VIRUSBreakend, a high-speed tool that identifies viral DNA presence and genomic integration. It utilizes single breakends, breakpoints in which only one side can be unambiguously placed, in a novel virus-centric variant calling and assembly approach to identify viral integrations with high sensitivity and a near-zero false discovery rate. VIRUSBreakend detects viral integrations anywhere in the host genome including regions such as centromeres and telomeres unable to be called by existing tools. Applying VIRUSBreakend to a large metastatic cancer cohort, we demonstrate that it can reliably detect clinically relevant viral presence and integration including HPV, HBV, MCPyV, EBV and HHV-8.

Availability and implementation

VIRUSBreakend is part of the Genomic Rearrangement IDentification Software Suite (GRIDSS). It is available under a GPLv3 license from https://github.com/PapenfussLab/VIRUSBreakend.

Supplementary information

Supplementary data are available at Bioinformatics online.

"Protein aggregates" contain RNA and DNA, entrapped by misfolded proteins but largely rescued by slowing translational elongation

All neurodegenerative diseases feature aggregates, which usually contain disease-specific diagnostic proteins; non-protein constituents, however, have rarely been explored. Aggregates from SY5Y-APP_Sw neuroblastoma, a cell model of familial Alzheimer's disease, were crosslinked and sequences of linked peptides identified. We constructed a normalized "contactome" comprising 11 subnetworks, centered on 24 high-connectivity hubs. Remarkably, all 24 are nucleic acid-binding proteins. This led us to isolate and sequence RNA and DNA from Alzheimer's and control aggregates. RNA fragments were mapped to the human genome by RNA-seq and DNA by ChIP-seq. Nearly all aggregate RNA sequences mapped to specific genes, whereas DNA fragments were predominantly intergenic. These nucleic acid mappings are all significantly nonrandom, making an artifactual origin extremely unlikely. RNA (mostly cytoplasmic) exceeded DNA (chiefly nuclear) by twofold to fivefold. RNA fragments recovered from AD tissue were ~1.5-to 2.5-fold more abundant than those recovered from control tissue, similar to the increase in protein. Aggregate abundances of specific RNA sequences were strikingly differential between cultured SY5Y-APP_Sw glioblastoma cells expressing APOE3 vs. APOE4, consistent with APOE4 competition for E-box/CLEAR motifs. We identified many G-quadruplex and viral sequences within RNA and DNA of aggregates, suggesting that sequestration of viral genomes may have driven the evolution of disordered nucleic acid-binding proteins. After RNA-interference knockdown of the translational-procession factor EEF2 to suppress translation in SY5Y-APP_Sw cells, the RNA content of aggregates declined by >90%, while reducing protein content by only 30% and altering DNA content by ≤10%. This implies that cotranslational misfolding of nascent proteins may ensnare polysomes into aggregates, accounting for most of their RNA content.

Demonstrating the utility of flexible sequence queries against indexed short reads with FlexTyper

Across the life sciences, processing next generation sequencing data commonly relies upon a computationally expensive process where reads are mapped onto a reference sequence. Prior to such processing, however, there is a vast amount of information that can be ascertained from the reads, potentially obviating the need for processing, or allowing optimized mapping approaches to be deployed. Here, we present a method termed FlexTyper which facilitates a “reverse mapping” approach in which high throughput sequence queries, in the form of k-mer searches, are run against indexed short-read datasets in order to extract useful information. This reverse mapping approach enables the rapid counting of target sequences of interest. We demonstrate FlexTyper’s utility for recovering depth of coverage, and accurate genotyping of SNP sites across the human genome. We show that genotyping unmapped reads can correctly inform a sample’s population, sex, and relatedness in a family setting. Detection of pathogen sequences within RNA-seq data was sensitive and accurate, performing comparably to existing methods, but with increased flexibility. We present two examples of ways in which this flexibility allows the analysis of genome features not well-represented in a linear reference. First, we analyze contigs from African genome sequencing studies, showing how they distribute across families from three distinct populations. Second, we show how gene-marking k-mers for the killer immune receptor locus allow allele detection in a region that is challenging for standard read mapping pipelines. The future adoption of the reverse mapping approach represented by FlexTyper will be enabled by more efficient methods for FM-index generation and biology-informed collections of reference queries. In the long-term, selection of population-specific references or weighting of edges in pan-population reference genome graphs will be possible using the FlexTyper approach. FlexTyper is available at https://github.com/wassermanlab/OpenFlexTyper.

In the past 15 years, next generation sequencing technology has revolutionized our capacity to process and analyze DNA sequencing data. From agriculture to medicine, this technology is enabling a deeper understanding of the blueprint of life. Next generation sequencing data is composed of short sequences of DNA, referred to as “reads”, which are often shorter than 200 base pairs making them many orders of magnitude smaller than the entirety of a human genome. Gaining insights from this data has typically leveraged a reference-guided mapping approach, where the reads are aligned to a reference genome and then post-processed to gain actionable information such as presence or absence of genomic sequence, or variation between the reference genome and the sequenced sample. Many experts in the field of genomics have concluded that selecting a single, linear reference genome for mapping reads against is limiting, and several current research endeavors are focused on exploring options for improved analysis methods to unlock the full utility of sequencing data. Among these improvements are the usage of sex-matched genomes, population-specific reference genomes, and emergent graph-based reference pan-genomes. However, advanced methods that use raw DNA sequencing data to inform the choice of reference genome and guide the alignment of reads to enriched reference genomes are needed. Here we develop a method termed FlexTyper, which creates a searchable index of the short read data and enables flexible, user-guided queries to provide valuable insights without the need for reference-guided mapping. We demonstrate the utility of our method by identifying sample ancestry and sex in human whole genome sequencing data, detecting viral pathogen reads in RNA-seq data, African-enriched genome regions absent from the global reference, and killer-cell immune receptor alleles that are complex to discern using standard read mapping. We anticipate early adoption of FlexTyper within analysis pipelines as a pre-mapping component, and further envision the bioinformatics and genomics community will leverage the tool for creative uses of sequence queries from unmapped data.

ViR: a tool to solve intrasample variability in the prediction of viral integration sites using whole genome sequencing data

Background

Several bioinformatics pipelines have been developed to detect sequences from viruses that integrate into the human genome because of the health relevance of these integrations, such as in the persistence of viral infection and/or in generating genotoxic effects, often progressing into cancer. Recent genomics and metagenomics analyses have shown that viruses also integrate into the genome of non-model organisms (i.e., arthropods, fish, plants, vertebrates). However, rarely studies of endogenous viral elements (EVEs) in non-model organisms have gone beyond their characterization from reference genome assemblies. In non-model organisms, we lack a thorough understanding of the widespread occurrence of EVEs and their biological relevance, apart from sporadic cases which nevertheless point to significant roles of EVEs in immunity and regulation of expression. The concomitance of repetitive DNA, duplications and/or assembly fragmentations in a genome sequence and intrasample variability in whole-genome sequencing (WGS) data could determine misalignments when mapping data to a genome assembly. This phenomenon hinders our ability to properly identify integration sites.

Results

To fill this gap, we developed ViR, a pipeline which solves the dispersion of reads due to intrasample variability in sequencing data from both single and pooled DNA samples thus ameliorating the detection of integration sites. We tested ViR to work with both in silico and real sequencing data from a non-model organism, the arboviral vector Aedes albopictus. Potential viral integrations predicted by ViR were molecularly validated supporting the accuracy of ViR results.

Conclusion

ViR will open new venues to explore the biology of EVEs, especially in non-model organisms. Importantly, while we generated ViR with the identification of EVEs in mind, its application can be extended to detect any lateral transfer event providing an ad-hoc sequence to interrogate.

HIVID2: an accurate tool to detect virus integrations in the host genome

MOTIVATION

Virus integration in the host genome is frequently reported to be closely associated with many human diseases, and the detection of virus integration is a critically challenging task. However, most existing tools show limited specificity and sensitivity. Therefore, the objective of this study is to develop a method for accurate detection of virus integration into host genomes.

RESULTS

Herein, we report a novel method termed HIVID2 that is a significant upgrade of HIVID. HIVID2 performs a paired-end combination (PE-combination) for potentially integrated reads. The resulting sequences are then remapped onto the reference genomes, and both split and discordant chimeric reads are used to identify accurate integration breakpoints with high confidence. HIVID2 represents a great improvement in specificity and sensitivity, and predicts breakpoints closer to the real integrations, compared with existing methods. The advantage of our method was demonstrated using both simulated and real data sets. HIVID2 uncovered novel integration breakpoints in well-known cervical cancer-related genes, including FHIT and LRP1B, which was verified using protein expression data. In addition, HIVID2 allows the user to decide whether to automatically perform advanced analysis using the identified virus integrations. By analyzing the simulated data and real data tests, we demonstrated that HIVID2 is not only more accurate than HIVID but also better than other existing programs with respect to both sensitivity and specificity. We believe that HIVID2 will help in enhancing future research associated with virus integration.

AVAILABILITY

HIVID2 can be accessed at https://github.com/zengxi-hada/HIVID2/.

CONTACT

Xi Zeng (zengxi@mail.hzau.edu.cn), Linghao Zhao (michael_yifan@126.com).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A First NGS Investigation Suggests No Association Between Viruses and Canine Cancers

Approximately 10–15% of worldwide human cancers are attributable to viral infection. When operating as carcinogenic elements, viruses may act with various mechanisms, but the most important is represented by viral integration into the host genome, causing chromosome instability, genomic mutations, and aberrations. In canine species, few reports have described an association between viral integration and canine cancers, but more comprehensive studies are needed. The advancement of next-generation sequencing and the cost reduction have resulted in a progressive increasing of sequencing data in veterinary oncology offering an opportunity to study virome in canine cancers. In this study, we have performed viral detection and integration analyses using VirusFinder2 software tool on available whole-genome and whole-exome sequencing data of different canine cancers. Several viral sequences were detected in lymphomas, hemangiosarcomas, melanomas, and osteosarcomas, but no reliable integration sites were identified. Even if with some limitations such as the depth and type of sequencing, a restricted number of available nonhuman genomes software, and a limited knowledge on endogenous retroviruses in the canine genome, results are compelling. However, further experiments are needed, and similarly to feline species, dedicated analysis tools for the identification of viral integration sites in canine cancers are required.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics meets medicine

During June 10–12, 2018, the International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held in Los Angeles, California, USA. The conference included 11 scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics ranging from 3D genome structure analysis and visualization, next generation sequencing analysis, computational drug discovery, medical informatics, cancer genomics to systems biology. While medical genomics has always been a main theme in ICIBM, this year we for the first time organized the BMC Medical Genomics Supplement for ICIBM. Here, we describe 15 ICIBM papers selected for publishing in BMC Medical Genomics.