Evidence map›Paper›PMID 34518837›Full record

ArticlebioRxiv : the preprint server for biology2021

Rescuing Low Frequency Variants within Intra-Host Viral Populations directly from Oxford Nanopore sequencing data.

Yunxi Liu, Joshua Kearney, Medhat Mahmoud, Bryce Kille, Fritz J Sedlazeck, Todd J Treangen

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yunxi LiuDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA.
Joshua KearneyDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA.
Medhat MahmoudHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.
Bryce KilleDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA.
Fritz J SedlazeckHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.
Todd J TreangenDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Funding

Viral Diversity and Pathogenicity in Mucosal Respiratory and Gastrointestinal DiseaseU19AI144297 · NIAID · BAYLOR COLLEGE OF MEDICINE · PI PETROSINO, JOSEPH FRANK · 2019 to 2024
$30.1M
Project 3: Functional Microbiome and Host Signatures in Transition from Commensal to pathogenP01AI152999 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI SAVIDGE, TOR C. · 2020 to 2025
$12.0M
NIAID NIH HHS P01 AI152999NIAID NIH HHS U19 AI144297
6 · The paper itself

Abstract

Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost, exemplified by well over a half of million ONT SARS-COV-2 datasets. Tracking low frequency intra-host variants has provided important insights with respect to elucidating within host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable solutions available. In response to this need, we present Variabel, a novel approach and first method designed for rescuing low frequency intra-host variants from ONT data alone. We evaluated Variabel on both within patient and across patient paired Illumina and ONT datasets; our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel.

Identifiers

PMID34518837
PMCPMC8437309

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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.