Evidence map›Paper›PMID 38730317›Full record

ArticleBMC bioinformatics2024

Machine learning based DNA melt curve profiling enables automated novel genotype detection.

Aaron Boussina, Lennart Langouche, Augustine C Obirieze, Mridu Sinha, Hannah Mack, William Leineweber, April Aralar, David T Pride, Todd P Coleman, Stephanie I Fraley

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2024. 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. 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

10 authors.

Aaron BoussinaDivision of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA.
Lennart LangoucheDepartment of Nanoengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Augustine C ObiriezeDepartment of Nanoengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Mridu SinhaDepartment of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Hannah MackDepartment of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
William LeineweberDepartment of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
April AralarDepartment of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
David T PrideDepartment of Pathology, University of California San Diego, La Jolla, CA, 92093, USA.
Todd P ColemanDepartment of Bioengineering, Stanford University, Stanford, CA, 94305, USA. toddcol@stanford.edu.
Stephanie I FraleyDepartment of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA. sifraley@ucsd.edu.

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
RESEARCH IN INFECTIOUS DISEASEST32AI007036 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NIZET, VICTOR · 1985 to 2025
$9.2M
Digital High Resolution Melt and Machine Learning for Rapid and Specific Diagnosis in Neonatal SepsisR01AI134982 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FRALEY, STEPHANIE IRENE · 2018 to 2022
$2.3M
NIAID NIH HHS R01 AI134982NIAID NIH HHS R01AI134982NIAID NIH HHS T32 AI007036NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications.

Indexed as

GenotypeMachine LearningAlgorithmsDNA, BacterialNucleic Acid DenaturationDNA, Bacterial16S rRNAEmerging pathogensMachine learningMelt curveNovelty detectionPathogen identification

Identifiers

PMID38730317
PMCPMC11088152

What OpenQuestion holds

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LicenceCC BY
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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.