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Updated: Aug 23, 2025

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

Published on: March 30, 2014

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

Philip L Tzou¹, Kaiming Tao¹, Malaya K Sahoo²

¹Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA, USA.

Journal of Clinical Virology : the Official Publication of the Pan American Society for Clinical Virology

|November 5, 2022

Summary

Sierra SARS-CoV-2 is a new program that improves the quality control of viral genomic data and assesses the clinical significance of mutations impacting antiviral therapy effectiveness for SARS-CoV-2.

Keywords:

Antiviral resistance Genomic sequencing Mutations SARS-CoV-2

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Area of Science:

Genomic surveillance
Virology
Bioinformatics

Background:

Full-genome sequencing of SARS-CoV-2 is common, but analyzing sequence quality and mutation impact on antiviral therapy is challenging.
Laboratories need tools to assess sequence quality, set mutation thresholds, and understand clinical significance of mutations.

Purpose of the Study:

To introduce and benchmark Sierra SARS-CoV-2, a program for analyzing SARS-CoV-2 genomic data.
To assess sequence quality, detect mutations, and annotate their potential impact on antiviral drug susceptibility.

Main Methods:

Sierra SARS-CoV-2 processes FASTQ and FASTA sequence files and lists of Spike mutations.
The program identifies sequences with unusual mutations and those affecting susceptibility to monoclonal antibodies (mAbs), remdesivir, and nirmatrelvir.

Main Results:

Performance on FASTQ files was benchmarked against the COVID-19 EDGE program using 600 sequences.
Analysis of nearly one million FASTA sequences was compared to GISAID mutation annotation.
88% of 13,578 Spike RBD mutation patterns had matching annotations, validating the program's accuracy.

Conclusions:

Sierra SARS-CoV-2 enhances viral genomic data quality control.
It provides functional annotation on the impact of mutations on SARS-CoV-2 antiviral targets.
The program is available online for researchers to improve SARS-CoV-2 variant analysis.