ArticleGenes2024
A New Cloud-Native Tool for Pharmacogenetic Analysis.
Article in Genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Analytical Validation of an Annotation Tool for WGS-Based Pharmacogenomics: Preparing for Clinical Implementation in Pediatric Oncology.Clinical and translational science · 2026Article
- The double-edged sword of nutraceuticals: comprehensive review of protective agents and their hidden risks.Frontiers in nutrition · 2025Review
- Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis.Frontiers in pharmacology · 2025Article
- The history and future of pharmacogenetics in Aotearoa/New Zealand.Journal of the Royal Society of New Zealand · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundThe advancement of next-generation sequencing (NGS) technologies provides opportunities for large-scale Pharmacogenetic (PGx) studies and pre-emptive PGx testing to cover a wide range of genotypes present in diverse populations. However, NGS-based PGx testing is limited by the lack of comprehensive computational tools to support genetic data analysis and clinical decisions.
methodsBioinformatics utilities specialized for human genomics and the latest cloud-based technologies were used to develop a bioinformatics pipeline for analyzing the genomic sequence data and reporting PGx genotypes. A database was created and integrated in the pipeline for filtering the actionable PGx variants and clinical interpretations. Strict quality verification procedures were conducted on variant calls with the whole genome sequencing (WGS) dataset of the 1000 Genomes Project (G1K). The accuracy of PGx allele identification was validated using the WGS dataset of the Pharmacogenetics Reference Materials from the Centers for Disease Control and Prevention (CDC).
resultsThe newly created bioinformatics pipeline, Pgxtools, can analyze genomic sequence data, identify actionable variants in 13 PGx relevant genes, and generate reports annotated with specific interpretations and recommendations based on clinical practice guidelines. Verified with two independent methods, we have found that Pgxtools consistently identifies variants more accurately than the results in the G1K dataset on GRCh37 and GRCh38.
conclusionsPgxtools provides an integrated workflow for large-scale genomic data analysis and PGx clinical decision support. Implemented with cloud-native technologies, it is highly portable in a wide variety of environments from a single laptop to High-Performance Computing (HPC) clusters and cloud platforms for different production scales and requirements.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.