Evidence map›Paper›PMID 33541262›Full record

ArticleBMC bioinformatics2021

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

Elisa Pischedda, Cristina Crava, Martina Carlassara, Susanna Zucca, Leila Gasmi, Mariangela Bonizzoni

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

5 citing papers in PubMed, 13 citations in OpenAlex.

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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

6 authors at 2 institutions in 2 countries.

Elisa PischeddaDepartment of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy.
Cristina CravaDepartment of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy.
Martina CarlassaraDepartment of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy.
Susanna ZuccaEngenome Srl, 27100, Pavia, Italy.
Leila GasmiDepartment of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy.
Mariangela BonizzoniDepartment of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy. mariangela.bonizzoni@unipv.it.ORCID http://orcid.org/0000-0003-0568-8564
University of Pavia · ITUniversitat de València · ES

Funding

H2020 European Research Council 682394Human Frontier Science Program RGP0007/2017MIUR R1623HZAH5
6 · The paper itself

Abstract

backgroundSeveral 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.

resultsTo 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.

conclusionViR 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.

Indexed as

Mosquito VectorsVirus IntegrationWhole Genome SequencingAnimalsComputational BiologyGenome, ViralGenomicsHumansLateral gene transferNon-model organismsRepetitive DNAViral integration

Identifiers

PMID33541262
PMCPMC7863434
OpenAlexW3126673655

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Registered trials

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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.