Evidence map›Paper›PMID 36518292›Full record

ArticlePeerJ2022

PredictION: a predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2.

David E Valencia-Valencia, Diana Lopez-Alvarez, Nelson Rivera-Franco, Andres Castillo, Johan S Piña, Carlos A Pardo, Beatriz Parra

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

7 authors.

David E Valencia-Valencia *Laboratorio de Técnicas y Análisis Ómicos-TAOLab/CiBioFi, Facultad de Ciencias Naturales y Exactas, Universidad del Valle, Cali, Valle del Cauca, Colombia.
Diana Lopez-Alvarez *Laboratorio de Técnicas y Análisis Ómicos-TAOLab/CiBioFi, Facultad de Ciencias Naturales y Exactas, Universidad del Valle, Cali, Valle del Cauca, Colombia.
Nelson Rivera-FrancoLaboratorio de Técnicas y Análisis Ómicos-TAOLab/CiBioFi, Facultad de Ciencias Naturales y Exactas, Universidad del Valle, Cali, Valle del Cauca, Colombia.
Andres CastilloLaboratorio de Técnicas y Análisis Ómicos-TAOLab/CiBioFi, Facultad de Ciencias Naturales y Exactas, Universidad del Valle, Cali, Valle del Cauca, Colombia.
Johan S PiñaDepartment of Data Science, People Contact, Manizales, Caldas, Colombia.
Carlos A PardoDepartment of Neurology, Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.
Beatriz ParraGrupo VIREM-Virus Emergentes y Enfermedad, Escuela de Ciencias Básicas, Facultad de Salud, Universidad del Valle, Cali, Valle del Cauca, Colombia.

Funding

Emerging Neuroviruses and Neurological Inflammatory DiseasesR01NS110122 · NINDS · JOHNS HOPKINS UNIVERSITY · PI PARDO-VILLAMIZAR, CARLOS A · 2019 to 2023
$1.7M
NINDS NIH HHS R01 NS110122
6 · The paper itself

Abstract

The optimization of resources for research in developing countries forces us to consider strategies in the wet lab that allow the reuse of molecular biology reagents to reduce costs. In this study, we used linear regression as a method for predictive modeling of coverage depth given the number of MinION reads sequenced to define the optimum number of reads necessary to obtain >200X coverage depth with a good lineage-clade assignment of SARS-CoV-2 genomes. The research aimed to create and implement a model based on machine learning algorithms to predict different variables (

Indexed as

COVID-19SARS-CoV-2GenomeHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNAGenomesLinear modelsMachine learningOxford nanopore technologiesSequences

Identifiers

PMID36518292
PMCPMC9744141

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.