Evidence map›Paper›PMID 40285027›Full record

ArticleViruses2025

Short-Read and Long-Read Whole Genome Sequencing for SARS-CoV-2 Variants Identification.

Mengfei Peng, Morgan L Davis, Meghan L Bentz, Alex Burgin, Mark Burroughs, Jasmine Padilla, Sarah Nobles, Yvette Unoarumhi, Kevin Tang

Abstract read
In one paragraph

Article in Viruses, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

9 authors.

Mengfei PengDivision of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.
Morgan L DavisDivision of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.
Meghan L BentzDivision of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.ORCID 0000-0001-9432-5858
Alex BurginDivision of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.
Mark BurroughsDivision of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.
Jasmine PadillaDivision of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.
Sarah NoblesDivision of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.
Yvette UnoarumhiDivision of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.
Kevin TangDivision of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.ORCID 0000-0002-4974-3951

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic surveillance of SARS-CoV-2 is crucial for detecting emerging variants and informing public health responses. Various sequencing technologies are used for whole genome sequencing of SARS-CoV-2. This cross-platform benchmark study applied established bioinformatics tools to assess and improve the performance of Illumina NovaSeq, Oxford Nanopore Technologies MinION, and Pacific Biosciences Sequel II sequencing platforms in identifying SARS-CoV-2 variants and lineage assignment. NovaSeq produced the highest number of reads and bases, depth of coverage, completeness of consensus genomes, stable mapping coverage across open reading frames in the genome, and consistent lineage assignments. The long-read sequencing platforms had lower yields, sequencing depth, and mapping coverage, limiting the number of qualified sequences for lineage assignment and variant identification. However, implementing proper quality controls on sequence data overcame these limitations and achieved consistent SARS-CoV-2 lineage assignments across all three sequencing platforms. The advancements in library preparation and technology for long-read sequencing are likely to enhance sequence quality and expand genome coverage, effectively addressing current limitations in genome analysis. By merging the unique advantages of both short- and long-read methods, we can significantly improve SARS-CoV-2 genomic surveillance and provide insights into sequencing strategies for other RNA viruses, pending further validation. This may lead to precise tracking of viral evolution and support public health policy decisions.

Indexed as

COVID-19Genome, ViralSARS-CoV-2Whole Genome SequencingComputational BiologyHigh-Throughput Nucleotide SequencingHumansgenome coveragegenomic surveillancelineageSARS-CoV-2sequencing depthvariantswhole-genome sequencing

Identifiers

PMID40285027
PMCPMC12031342

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.