Evidence map›Paper›PMID 33173909›Full record

ArticlemedRxiv : the preprint server for health sciences2020

Profiling SARS-CoV-2 mutation fingerprints that range from the viral pangenome to individual infection quasispecies.

Billy T Lau, Dmitri Pavlichin, Anna C Hooker, Alison Almeda, Giwon Shin, Jiamin Chen, Malaya K Sahoo, ChunHong Huang, Benjamin A Pinsky, HoJoon Lee and 1 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. 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, 8 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Billy T LauDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Dmitri PavlichinDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Anna C HookerDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Alison AlmedaDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Giwon ShinDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Jiamin ChenDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Malaya K SahooDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, United States.
ChunHong HuangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Benjamin A PinskyDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, United States.
HoJoon LeeDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Hanlee P JiDivision of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Stanford University · USStanford Medicine · US

Funding

WHOLE YEAST GENOME TRANSCRIPTIONAL AND FUNCTIONAL GENE MAPS--A FEASIBILITY STUDYP01HG000205 · NHGRI · STANFORD UNIVERSITY · PI DAVIS, RONALD WAYNE, HOWE, ROGER THOMAS · 1990 to 2016
$120.9M
Statistical Models for Genome Sequencing and AssociationR01HG006137 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI JI, HANLEE P, ZHANG, NANCY R · 2011 to 2022
$4.3M
Omics information maximization in single-cell sequencing with hybrid molecular and computational approachesR35HG011292 · NHGRI · STANFORD UNIVERSITY · PI LAU, BILLY TSZ CHEONG · 2020 to 2024
$2.3M
K-mer indexing for pan-genome reference annotationU01HG010963 · NHGRI · STANFORD UNIVERSITY · PI JI, HANLEE P, WEISSMAN, TSACHY · 2020 to 2023
$1.3M
NHGRI NIH HHS P01 HG000205NHGRI NIH HHS R01 HG006137NHGRI NIH HHS R35 HG011292NHGRI NIH HHS U01 HG010963
6 · The paper itself

Abstract

Background: The genome of SARS-CoV-2 is susceptible to mutations during viral replication due to the errors generated by RNA-dependent RNA polymerases. These mutations enable the SARS-CoV-2 to evolve into new strains. Viral quasispecies emerge from Methods: Leveraging thousands of sequenced SARS-CoV-2 genomes, we performed a viral pangenome analysis to identify conserved genomic sequences. We used a rapid and highly efficient computational approach that relies on k-mers, short tracts of sequence, instead of conventional sequence alignment. Using this method, we annotated viral mutation signatures that were associated with specific strains. Based on these highly conserved viral sequences, we developed a rapid and highly scalable targeted sequencing assay to identify mutations, detect quasispecies and identify mutation signatures from patients. These results were compared to the pangenome genetic fingerprints. Results: We built a k-mer index for thousands of SARS-CoV-2 genomes and identified conserved genomics regions and landscape of mutations across thousands of virus genomes. We delineated mutation profiles spanning common genetic fingerprints (the combination of mutations in a viral assembly) and rare ones that occur in only small fraction of patients. We developed a targeted sequencing assay by selecting primers from the conserved viral genome regions to flank frequent mutations. Using a cohort of SARS-CoV-2 clinical samples, we identified genetic fingerprints consisting of strain-specific mutations seen across populations and de novo quasispecies mutations localized to individual infections. We compared the mutation profiles of viral samples undergoing analysis with the features of the pangenome. Conclusions: We conducted an analysis for viral mutation profiles that provide the basis of genetic fingerprints. Our study linked pangenome analysis with targeted deep sequenced SARS-CoV-2 clinical samples. We identified quasispecies mutations occurring within individual patients, mutations demarcating dominant species and the prevalence of mutation signatures, of which a significant number were relatively unique. Analysis of these genetic fingerprints may provide a way of conducting molecular contact tracing.

Indexed as

COVID-19genetic fingerprintspandemicquasispeciesSARS-CoV-2sequencingviral mutations

Identifiers

PMID33173909
PMCPMC7654905
OpenAlexW3097778634

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.