Evidence map›Paper›PMID 36032969›Full record

ArticlebioRxiv : the preprint server for biology2022

Intrahost SARS-CoV-2 k-mer identification method (iSKIM) for rapid detection of mutations of concern reveals emergence of global mutation patterns.

Ashley Thommana, Migun Shakya, Jaykumar Gandhi, Christian K Fung, Patrick S G Chain, Irina Maljkovic Berry, Matthew A Conte

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2022. 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
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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

7 authors.

Ashley ThommanaViral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
Migun ShakyaLos Alamos National Laboratory, Biosecurity and Public Health Group, Bioscience Division, Los Alamos, NM, USA.
Jaykumar GandhiViral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
Christian K FungViral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
Patrick S G ChainLos Alamos National Laboratory, Biosecurity and Public Health Group, Bioscience Division, Los Alamos, NM, USA.
Irina Maljkovic BerryViral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
Matthew A ConteViral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite unprecedented global sequencing and surveillance of SARS-CoV-2, timely identification of the emergence and spread of novel variants of concern (VoCs) remains a challenge. Several million raw genome sequencing runs are now publicly available. We sought to survey these datasets for intrahost variation to study emerging mutations of concern. We developed iSKIM ("intrahost SARS-CoV-2 k-mer identification method") to relatively quickly and efficiently screen the many SARS-CoV-2 datasets to identify intrahost mutations belonging to lineages of concern. Certain mutations surged in frequency as intrahost minor variants just prior to, or while lineages of concern arose. The Spike N501Y change common to several VoCs was found as a minor variant in 834 samples as early as October 2020. This coincides with the timing of the first detected samples with this mutation in the Alpha/B.1.1.7 and Beta/B.1.351 lineages. Using iSKIM, we also found that Spike L452R was detected as an intrahost minor variant as early as September 2020, prior to the observed rise of the Epsilon/B.1.429/B.1.427 lineages in late 2020. iSKIM rapidly screens for mutations of interest in raw data, prior to genome assembly, and can be used to detect increases in intrahost variants, potentially providing an early indication of novel variant spread.

Identifiers

PMID36032969
PMCPMC9413717

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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.