Evidence map›Paper›PMID 34214168›Full record

ArticleNucleic acids research2021

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction.

Sergey Knyazev, Viachaslau Tsyvina, Anupama Shankar, Andrew Melnyk, Alexander Artyomenko, Tatiana Malygina, Yuri B Porozov, Ellsworth M Campbell, William M Switzer, Pavel Skums and 2 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

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  6. Intra-host diversity of proviral FeLV and clinical outcomes.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2025
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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

12 authors.

Sergey KnyazevDepartment of Computer Science, Georgia State University, Atlanta, GA 30302, USA.ORCID 0000-0003-0385-1831
Viachaslau TsyvinaDepartment of Computer Science, Georgia State University, Atlanta, GA 30302, USA.
Anupama ShankarDivision of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Andrew MelnykDepartment of Computer Science, Georgia State University, Atlanta, GA 30302, USA.
Alexander ArtyomenkoGuardant Health Inc., Redwood City, CA 94063, USA.
Tatiana MalyginaInternational Scientific and Research Institute of Bioengineering, ITMO University, St. Petersburg 197101, Russia.
Yuri B PorozovWorld-Class Research Center "Digital biodesign and personalized healthcare", I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Ellsworth M CampbellDivision of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
William M SwitzerDivision of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Pavel SkumsDepartment of Computer Science, Georgia State University, Atlanta, GA 30302, USA.ORCID 0000-0003-4007-5624
Serghei MangulDepartment of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA 90089, USA.
Alex ZelikovskyDepartment of Computer Science, Georgia State University, Atlanta, GA 30302, USA.ORCID 0000-0003-4424-4691

Funding

QUBBD: VIRAL EVOLUTION AND SPREAD OF INFECTIOUS DISEASE IN COMPLEX NETWORK: BIG DATA ANALYSIS AND MODELINGR01EB025022 · NIBIB · GEORGIA STATE UNIVERSITY · PI SKUMS, PAVEL, VANNBERG, FREDRIK · 2017 to 2019
$891k
NIBIB NIH HHS R01 EB025022
6 · The paper itself

Abstract

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient's treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

Indexed as

AlgorithmsHaplotypesComputational BiologyCOVID-19Gene FrequencyHigh-Throughput Nucleotide SequencingHIV-1HIV InfectionsHumansMutationPolymorphism, Single NucleotideReproducibility of ResultsRNA VirusesRNA Virus InfectionsSARS-CoV-2Sensitivity and Specificity

Identifiers

PMID34214168
PMCPMC8464054

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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.