Evidence map›Paper›PMID 30016933›Full record

ArticleBMC bioinformatics2018

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

Saurabh Baheti, Xiaojia Tang, Daniel R O'Brien, Nicholas Chia, Lewis R Roberts, Heidi Nelson, Judy C Boughey, Liewei Wang, Matthew P Goetz, Jean-Pierre A Kocher and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.2field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 34 citations in OpenAlex.

  1. The authors respond to feedback onFrontiers in oncology · 2026
    Article
  2. Review
  3. Article
  4. Article
  5. hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer.International journal of molecular sciences · 2022
    Article
  6. Review
  7. Article
  8. Article
  9. Mutations inFrontiers in oncology · 2021
    Article
  10. Article
  11. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Saurabh BahetiDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Xiaojia TangDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Daniel R O'BrienDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Nicholas ChiaDepartment of Surgery, Mayo Clinic, Rochester, MN, USA.
Lewis R RobertsDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
Heidi NelsonDepartment of Surgery, Mayo Clinic, Rochester, MN, USA.
Judy C BougheyDepartment of Surgery, Mayo Clinic, Rochester, MN, USA.
Liewei WangDepartment of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
Matthew P GoetzDepartment of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
Jean-Pierre A KocherDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Krishna R KalariDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. kalari.krishna@mayo.edu.
Mayo Clinic in Arizona · USMayo Clinic in Florida · USMayo Clinic · US

Funding

The Role of CHFR in Tumorigenesis and Paclitaxel-Sensitivity in Breast CancerP50CA116201 · NCI · MAYO CLINIC ROCHESTER · PI Fergus Joseph Couch · 2005 to 2026
$49.9M
TRAININGU54GM114838 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI HAN, JIAWEI, SINHA, SAURABH · 2014 to 2017
$10.8M
Mayo Clinic Breast Specialized Program of Research Excellence P50CA116201NCI NIH HHS P50 CA116201NIGMS NIH HHS U54 GM114838NIGMS NIH HHS U54GM114838
6 · The paper itself

Abstract

backgroundTransfer 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.

resultsWe 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. We 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. We 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. We also processed 220 breast tumor WGS data through the workflow; however, there were no HGT events detected in those samples.

conclusionsHGT-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. The HGT-ID workflow is released under the MIT License and available at http://kalarikrlab.org/Software/HGT-ID.html .

Indexed as

Genome, HumanAlgorithmsBase SequenceBreast NeoplasmsCell Line, TumorComputer SimulationFemaleGene Transfer, HorizontalHigh-Throughput Nucleotide SequencingHumansROC CurveSoftwareVirus IntegrationWhole Genome SequencingWorkflowHorizontal gene transferNext-generation sequencingRNA-Seq – CancerViral integrationWhole-genome sequencing

Identifiers

PMID30016933
PMCPMC6050683
OpenAlexW2884116871

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.