ViralFusionSeq: accurately discover viral integration events and reconstruct fusion transcripts at single-base resolution

LINE(1)s of evidence in HBV-driven liver cancer

Hepatitis B virus (HBV) integration in hepatocellular carcinoma (HCC) is a poorly understood event. In a recent Cancer Cell paper, Lau et al. (2014) describe a HBV-human fusion transcript (HBx-LINE1) that functions as a lncRNA, influences the epithelial-mesenchymal transition, and correlates with reduced patient survival and tumor formation in mice.

SummonChimera infers integrated viral genomes with nucleotide precision from NGS data

Background

Viral integration into a host genome is defined by two chimeric junctions that join viral and host DNA. Recently, computational tools have been developed that utilize NGS data to detect chimeric junctions. These methods identify individual viral-host junctions but do not associate chimeric pairs as an integration event. Without knowing the chimeric boundaries of an integration, its genetic content cannot be determined.

Results

Summonchimera is a Perl program that associates chimera pairs to infer the complete viral genomic integration event to the nucleotide level within single or paired-end NGS data. SummonChimera integration prediction was verified on a set of single-end IonTorrent reads from a purified Salmonella bacterium with an integrated bacteriophage. Furthermore, SummonChimera predicted integrations from experimentally verified Hepatitis B Virus chimeras within a paired-end Whole Genome Sequencing hepatocellular carcinoma tumor database.

Conclusions

SummonChimera identified all experimentally verified chimeras detected by current computational methods. Further, SummonChimera integration inference precisely predicted bacteriophage integration. The application of SummonChimera to cancer NGS accurately identifies deletion of host and viral sequence during integration. The precise nucleotide determination of an integration allows prediction of viral and cellular gene transcription patterns.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0348-4) contains supplementary material, which is available to authorized users.

Development of a virus detection and discovery pipeline using next generation sequencing

Next generation sequencing (NGS) has revolutionized virus discovery. Notwithstanding, a vertical pipeline, from sample preparation to data analysis, has not been available to the plant virology community. We developed a degenerate oligonucleotide primed RT-PCR method with multiple barcodes for NGS, and constructed VirFind, a bioinformatics tool specifically for virus detection and discovery able to: (i) map and filter out host reads, (ii) deliver files of virus reads with taxonomic information and corresponding Blastn and Blastx reports, and (iii) perform conserved domain search for reads of unknown origin. The pipeline was used to process more than 30 samples resulting in the detection of all viruses known to infect the processed samples, the extension of the genomic sequences of others, and the discovery of several novel viruses. VirFind was tested by four external users with datasets from plants or insects, demonstrating its potential as a universal virus detection and discovery tool.

Alternative applications for distinct RNA sequencing strategies

Recent advances in RNA library preparation methods, platform accessibility and cost efficiency have allowed high-throughput RNA sequencing (RNAseq) to replace conventional hybridization microarray platforms as the method of choice for mRNA profiling and transcriptome analyses. RNAseq is a powerful technique to profile both long and short RNA expression, and the depth of information gained from distinct RNAseq methods is striking and facilitates discovery. In addition to expression analysis, distinct RNAseq approaches also allow investigators the ability to assess transcriptional elongation, DNA variance and exogenous RNA content. Here we review the current state of the art in transcriptome sequencing and address epigenetic regulation, quantification of transcription activation, RNAseq output and a diverse set of applications for RNAseq data. We detail how RNAseq can be used to identify allele-specific expression, single-nucleotide polymorphisms and somatic mutations and discuss the benefits and limitations of using RNAseq to monitor DNA characteristics. Moreover, we highlight the power of combining RNA- and DNAseq methods for genomic analysis. In summary, RNAseq provides the opportunity to gain greater insight into transcriptional regulation and output than simply miRNA and mRNA profiling.

Viral-human chimeric transcript predisposes risk to liver cancer development and progression

The mutagenic effect of hepatitis B (HBV) integration in predisposing risk to hepatocellular carcinoma (HCC) remains elusive. In this study, we performed transcriptome sequencing of HBV-positive HCC cell lines and showed transcription of viral-human gene fusions from the site of genome integrations. We discovered tumor-promoting properties of a chimeric HBx-LINE1 that, intriguingly, functions as a hybrid RNA. HBx-LINE1 can be detected in 23.3% of HBV-associated HCC tumors and correlates with poorer patient survival. HBx-LINE1 transgenic mice showed heightened susceptibility to diethylnitrosamine-induced tumor formation. We further show that HBx-LINE1 expression affects β-catenin transactivity, which underlines a role in activating Wnt signaling. Thus, this study identifies a viral-human chimeric fusion transcript that functions like a long noncoding RNA to promote HCC.

Sensitive detection of viral transcripts in human tumor transcriptomes

In excess of % of human cancer incidents have a viral cofactor. Epidemiological studies of idiopathic human cancers indicate that additional tumor viruses remain to be discovered. Recent advances in sequencing technology have enabled systematic screenings of human tumor transcriptomes for viral transcripts. However, technical problems such as low abundances of viral transcripts in large volumes of sequencing data, viral sequence divergence, and homology between viral and human factors significantly confound identification of tumor viruses. We have developed a novel computational approach for detecting viral transcripts in human cancers that takes the aforementioned confounding factors into account and is applicable to a wide variety of viruses and tumors. We apply the approach to conducting the first systematic search for viruses in neuroblastoma, the most common cancer in infancy. The diverse clinical progression of this disease as well as related epidemiological and virological findings are highly suggestive of a pathogenic cofactor. However, a viral etiology of neuroblastoma is currently contested. We mapped transcriptomes of neuroblastoma as well as positive and negative controls to the human and all known viral genomes in order to detect both known and unknown viruses. Analysis of controls, comparisons with related methods, and statistical estimates demonstrate the high sensitivity of our approach. Detailed investigation of putative viral transcripts within neuroblastoma samples did not provide evidence for the existence of any known human viruses. Likewise, de-novo assembly and analysis of chimeric transcripts did not result in expression signatures associated with novel human pathogens. While confounding factors such as sample dilution or viral clearance in progressed tumors may mask viral cofactors in the data, in principle, this is rendered less likely by the high sensitivity of our approach and the number of biological replicates analyzed. Therefore, our results suggest that frequent viral cofactors of metastatic neuroblastoma are unlikely.

Many human cancers are caused by infections with tumor viruses and identification of these pathogens is considered a critical contribution to cancer prevention. Deep sequencing enables us to systematically investigate viral nucleotide signatures in order to either verify or exclude the existence of viruses in idiopathic human cancers. We have developed Virana, a novel computational approach for identifying tumor viruses in human cancers that is applicable to a wide variety of tumors and viruses. Virana firstly addresses several important biological confounding factors that may hinder successful detection of these pathogens. We applied our approach in the first systematic search for cancer-causing viruses in metastatic neuroblastoma, the most common form of cancer in infancy. Although the heterogeneous clinical progression of this disease as well as epidemiological and virological findings are suggestive of a pathogenic cofactor, the viral etiology of neuroblastoma is currently contested. We conducted an analysis of experimental controls, comparisons with related approaches, as well as statistical analyses in order to validate our method. In spite of the high sensitivity of our approach, analyses of neuroblastoma transcriptomes did not provide evidence for the existence of any known or unknown human viruses. Our results therefore suggest that frequent viral cofactors of metastatic neuroblastoma are unlikely.

Finding the lost treasures in exome sequencing data

Exome sequencing is one of the most cost-efficient sequencing approaches for conducting genome research on coding regions. However, significant portions of the reads obtained in exome sequencing come from outside of the designed target regions. These additional reads are generally ignored, potentially wasting an important source of genomic data. There are three major types of unintentionally sequenced read that can be found in exome sequencing data: reads in introns and intergenic regions, reads in the mitochondrial genome, and reads originating in viral genomes. All of these can be used for reliable data mining, extending the utility of exome sequencing. Large-scale exome sequencing data repositories, such as The Cancer Genome Atlas (TCGA), the 1000 Genomes Project, National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project, and The Sequence Reads Archive, provide researchers with excellent secondary data-mining opportunities to study genomic data beyond the intended target regions.

VirusFinder: software for efficient and accurate detection of viruses and their integration sites in host genomes through next generation sequencing data

Next generation sequencing (NGS) technologies allow us to explore virus interactions with host genomes that lead to carcinogenesis or other diseases; however, this effort is largely hindered by the dearth of efficient computational tools. Here, we present a new tool, VirusFinder, for the identification of viruses and their integration sites in host genomes using NGS data, including whole transcriptome sequencing (RNA-Seq), whole genome sequencing (WGS), and targeted sequencing data. VirusFinder’s unique features include the characterization of insertion loci of virus of arbitrary type in the host genome and high accuracy and computational efficiency as a result of its well-designed pipeline. The source code as well as additional data of VirusFinder is publicly available at http://bioinfo.mc.vanderbilt.edu/VirusFinder/.