HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing data

Transfer of genetic material from microbes or viruses into the host genome is known as horizontal gene transfer (HGT). The integration of viruses into the human genome is associated with multiple cancers, and these can now be detected using next-generation sequencing methods such as whole genome sequencing and RNA-sequencing.

Mayo Clinic researchers designed a novel computational workflow, HGT-ID, to identify the integration of viruses into the human genome using the sequencing data. The HGT-ID workflow primarily follows a four-step procedure: i) pre-processing of unaligned reads, ii) virus detection using subtraction approach, iii) identification of virus integration site using discordant and soft-clipped reads and iv) HGT candidates prioritization through a scoring function. Annotation and visualization of the events, as well as primer design for experimental validation, are also provided in the final report.

Overview of the HGT-ID workflow

rna-seq

The researchers evaluated the tool performance with the well-understood cervical cancer samples. The HGT-ID workflow accurately detected known human papillomavirus (HPV) integration sites with high sensitivity and specificity compared to previous HGT methods. They applied HGT-ID to The Cancer Genome Atlas (TCGA) whole-genome sequencing data (WGS) from liver tumor-normal pairs. Multiple hepatitis B virus (HBV) integration sites were identified in TCGA liver samples and confirmed by HGT-ID using the RNA-Seq data from the matched liver pairs. This shows the applicability of the method in both the data types and cross-validation of the HGT events in liver samples.

HGT-ID is a novel computational workflow to detect the integration of viruses in the human genome using the sequencing data. It is fast and accurate with functions such as prioritization, annotation, visualization and primer design for future validation of HGTs.

Availability – The HGT-ID workflow is released available at http://kalarikrlab.org/Software/HGT-ID.html

Baheti S, Tang X, O’Brien DR, Chia N, Roberts LR, Nelson H, Boughey JC, Wang L, Goetz MP, Kocher JA, Kalari KR. (2018) HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing data. BMC Bioinformatics 19(1):271. [article]

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