Evidence map›Paper›PMID 36334368›Full record

ArticleJournal of clinical virology : the official publication of the Pan American Society for Clinical Virology2022

Sierra SARS-CoV-2 sequence and antiviral resistance analysis program.

Philip L Tzou, Kaiming Tao, Malaya K Sahoo, Sergei L Kosakovsky Pond, Benjamin A Pinsky, Robert W Shafer

Open access · hybridAbstract read
In one paragraph

Article in Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 21% of its field
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

7 citing papers in PubMed, 11 citations in OpenAlex.

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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

6 authors at 3 institutions in 1 country.

Philip L TzouDivision of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA, USA. Electronic address: philiptz@stanford.edu.
Kaiming TaoDivision of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA, USA.
Malaya K SahooDepartment of Pathology, Stanford University, Stanford, CA, USA.
Sergei L Kosakovsky PondInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.
Benjamin A PinskyDivision of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, Stanford University, Stanford, CA, USA.
Robert W ShaferDivision of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA, USA.
Stanford University · USStanford Medicine · USTemple University · US

Funding

HIV Drug Resistance DatabaseR24AI136618 · NIAID · STANFORD UNIVERSITY · PI ROBERT William SHAFER · 2018 to 2026
$7.3M
Turning big data analysis infrastructure for HIV researchR01AI134384 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI NEKRUTENKO, ANTON, POND, SERGEI L KOSAKOVSKY · 2017 to 2021
$4.2M
NIAID NIH HHS R01 AI134384NIAID NIH HHS R24 AI136618
6 · The paper itself

Abstract

introductionAlthough most laboratories are capable of employing established protocols to perform full-genome SARS-CoV-2 sequencing, many are unable to assess sequence quality, select appropriate mutation-detection thresholds, or report on the potential clinical significance of mutations in the targets of antiviral therapy

methodsWe describe the technical aspects and benchmark the performance of Sierra SARS-CoV-2, a program designed to perform these functions on user-submitted FASTQ and FASTA sequence files and lists of Spike mutations. Sierra SARS-CoV-2 indicates which sequences contain an unexpectedly large number of unusual mutations and which mutations are associated with reduced susceptibility to clinical stage mAbs, the RdRP inhibitor remdesivir, or the Mpro inhibitor nirmatrelvir

resultsTo assess the performance of Sierra SARS-CoV-2 on FASTQ files, we applied it to 600 representative FASTQ sequences and compared the results to the COVID-19 EDGE program. To assess its performance on FASTA files, we applied it to nearly one million representative FASTA sequences and compared the results to the GISAID mutation annotation. To assess its performance on mutations lists, we applied it to 13,578 distinct Spike RBD mutation patterns and showed that exactly or partially matching annotations were available for 88% of patterns

conclusionSierra SARS-CoV-2 leverages previously published data to improve the quality control of submitted viral genomic data and to provide functional annotation on the impact of mutations in the targets of antiviral SARS-CoV-2 therapy. The program can be found at https://covdb.stanford.edu/sierra/sars2/ and its source code at https://github.com/hivdb/sierra-sars2.

Indexed as

COVID-19SARS-CoV-2Antiviral AgentsDrug Resistance, ViralGenome, ViralHumansMutationSpike Glycoprotein, CoronavirusAntiviral AgentsSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2Antiviral resistanceGenomic sequencingMutationsSARS-CoV-2

Identifiers

PMID36334368
PMCPMC9595491
OpenAlexW4307078340

What OpenQuestion holds

Textmetadata
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.