sierra-local: A lightweight standalone application for drug resistance prediction

Ho, Jasper C.; Ng, Garway T; Renaud, Mathias S.; Poon, Art F. Y.

doi:10.21105/joss.01186

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Quasitools outputs filtered FASTQ files, amino acid variant call files, a mixed base consensus sequence in FASTA format and a drug resistance mutation report (consisting of identified drug resistance mutations and corresponding mutational frequencies) in comma separated values format. The consensus sequences are parsed onto sierralocal ( Ho et al , 2019 ) for scoring of identified drug-resistant mutations. Sierra-local generates a JSON object which is parsed to the R programming environment to generate a drug resistance report ( Fig.…”

Section: Methodsmentioning

confidence: 99%

QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data

Ssekagiri

Jjingo

Lujumba

et al. 2022

Bioinformatics Advances

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Next Generation Sequencing (NGS) enables reliable detection of resistance mutations in minority variants of Human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%. Availability and implementation QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm. Supplementary information Supplementary data are available at Bioinformatics Advances online.

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Section: Methodsmentioning

confidence: 99%

QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data

Ssekagiri

Jjingo

Lujumba

et al. 2022

Bioinformatics Advances

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“…It was compiled and tested on Ubuntu 18.0.4 LTS (Bionic Beaver). HIV-DRIVES was put together using different packages that include; trim_galore, bowtie2, samtools, quasitools, and sierra-local [6]- [10]. All these packages and their dependencies are housed in the HIV-DRIVES conda environment so that they do not interfere with already existing programs.…”

Section: Pipeline Architecturementioning

confidence: 99%

HIV-DRIVES: HIV Drug Resistance Identification, Variant Evaluation, & Surveillance Pipeline

Kanyerezi,

Sserwadda,

Ssemaganda

et al. 2023

Preprint

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The global prevalence of resistance to the Human Immunodeficiency Virus (HIV) combined antiretroviral therapy (cART) emphasizes the need to continuous monitoring to better understand the dynamics of drug-resistant mutations to guide treatment optimization and patient management as well as check the spread of resistant viral strains. We have recently, integrated next-generation sequencing (NGS) into routine HIV drug resistance (HIVDR) monitoring, with key challenges in the bioinformatic analysis and interpretation of the complex data generated while ensuring data security and privacy of patient information. To address these challenges, here, we present HIV-DRIVES (HIV Drug Resistance Identification, Variant Evaluation, and Surveillance), an NGS-HIVDR bioinformatics pipeline that has been developed and validated using Illumina short-reads, FASTA, and sanger ab1.seq files.Availability and implementationHIV-DRIVES source codes and operation manual freely available athttps://github.com/MicroBioGenoHub/HIV-DRIVES.

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ChatGPT in medical school: how successful is AI in progress testing?

Friederichs,

März

2023

Medical Education Online

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Background As generative artificial intelligence (AI), ChatGPT provides easy access to a wide range of information, including factual knowledge in the field of medicine. Given that knowledge acquisition is a basic determinant of physicians’ performance, teaching and testing different levels of medical knowledge is a central task of medical schools. To measure the factual knowledge level of the ChatGPT responses, we compared the performance of ChatGPT with that of medical students in a progress test. Methods A total of 400 multiple-choice questions (MCQs) from the progress test in German-speaking countries were entered into ChatGPT’s user interface to obtain the percentage of correctly answered questions. We calculated the correlations of the correctness of ChatGPT responses with behavior in terms of response time, word count, and difficulty of a progress test question. Results Of the 395 responses evaluated, 65.5% of the progress test questions answered by ChatGPT were correct. On average, ChatGPT required 22.8 s (SD 17.5) for a complete response, containing 36.2 (SD 28.1) words. There was no correlation between the time used and word count with the accuracy of the ChatGPT response (correlation coefficient for time rho = −0.08, 95% CI [−0.18, 0.02], t(393) = −1.55, p = 0.121; for word count rho = −0.03, 95% CI [−0.13, 0.07], t(393) = −0.54, p = 0.592). There was a significant correlation between the difficulty index of the MCQs and the accuracy of the ChatGPT response (correlation coefficient for difficulty: rho = 0.16, 95% CI [0.06, 0.25], t(393) = 3.19, p = 0.002). Conclusion ChatGPT was able to correctly answer two-thirds of all MCQs at the German state licensing exam level in Progress Test Medicine and outperformed almost all medical students in years 1–3. The ChatGPT answers can be compared with the performance of medical students in the second half of their studies.

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sierra-local: A lightweight standalone application for drug resistance prediction

Abstract: License Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY).

Cited by 5 publications

References 24 publications

QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data

QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data

HIV-DRIVES: HIV Drug Resistance Identification, Variant Evaluation, & Surveillance Pipeline

ChatGPT in medical school: how successful is AI in progress testing?

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