Evidence map›Paper›PMID 35439933›Full record

ArticleBMC bioinformatics2022

MALVIRUS: an integrated application for viral variant analysis.

Simone Ciccolella, Luca Denti, Paola Bonizzoni, Gianluca Della Vedova, Yuri Pirola, Marco Previtali

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 37% 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

6 citing papers in PubMed, 6 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.

Simone Ciccolella *Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20136, Milan, Italy.
Luca Denti *Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20136, Milan, Italy.
Paola BonizzoniDepartment of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20136, Milan, Italy.
Gianluca Della VedovaDepartment of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20136, Milan, Italy.
Yuri Pirola *Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20136, Milan, Italy. yuri.pirola@unimib.it.ORCID http://orcid.org/0000-0002-8479-7592
Marco Previtali *Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20136, Milan, Italy.
University of Milano-Bicocca · ITInstitut Pasteur · FR

Funding

H2020 Marie Sklodowska-Curie Actions 872539Università degli Studi di Milano-Bicocca 2019-ATE-0533
6 · The paper itself

Abstract

backgroundBeing able to efficiently call variants from the increasing amount of sequencing data daily produced from multiple viral strains is of the utmost importance, as demonstrated during the COVID-19 pandemic, in order to track the spread of the viral strains across the globe.

resultsWe present MALVIRUS, an easy-to-install and easy-to-use application that assists users in multiple tasks required for the analysis of a viral population, such as the SARS-CoV-2. MALVIRUS allows to: (1) construct a variant catalog consisting in a set of variations (SNPs/indels) from the population sequences, (2) efficiently genotype and annotate variants of the catalog supported by a read sample, and (3) when the considered viral species is the SARS-CoV-2, assign the input sample to the most likely Pango lineages using the genotyped variations.

conclusionsTests on Illumina and Nanopore samples proved the efficiency and the effectiveness of MALVIRUS in analyzing SARS-CoV-2 strain samples with respect to publicly available data provided by NCBI and the more complete dataset provided by GISAID. A comparison with state-of-the-art tools showed that MALVIRUS is always more precise and often have a better recall.

Indexed as

COVID-19Genome, ViralHigh-Throughput Nucleotide SequencingHumansMutationPandemicsPhylogenySARS-CoV-2GenotypingLineage classificationSARS-CoV-2Sequence analysisVirus

Identifiers

PMID35439933
PMCPMC9017741
OpenAlexW4224316085

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.