ArticleBMC genomics2024
RNA-seq validation: software for selection of reference and variable candidate genes for RT-qPCR.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Candida Tropicalis Biofilm Formation Under Secondary Bile Salt Sodium Deoxycholate.Current microbiology · 2026Article
- Optimizing bioinformatic workflows to extract clinically usable gene expression data from targeted tumor RNA sequencing panels: comparison with total RNA-seq in cancer samples.Bioinformatics advances · 2026Article
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- BCG overexpressing Ag85B reprograms host immune response through coordinated transcriptional networks.Frontiers in immunology · 2026Article
- Selection ofInsects · 2025Article
- Determination of Stable Reference Genes for Gene Expression Analysis in Black Rockfish (Genes · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundReal-time quantitative PCR (RT-qPCR) is one of the most widely used gene expression analyses for validating RNA-seq data. This technique requires reference genes that are stable and highly expressed, at least across the different biological conditions present in the transcriptome. Reference and variable candidate gene selection is often neglected, leading to misinterpretation of the results.
resultsWe developed a software named "Gene Selector for Validation" (GSV), which identifies the best reference and variable candidate genes for validation within a quantitative transcriptome. This tool also filters the candidate genes concerning the RT-qPCR assay detection limit. GSV was compared with other software using synthetic datasets and performed better, removing stable low-expression genes from the reference candidate list and creating the variable-expression validation list. GSV software was used on a real case, an Aedes aegypti transcriptome. The top GSV reference candidate genes were selected for RT-qPCR analysis, confirming that eiF1A and eiF3j were the most stable genes tested. The tool also confirmed that traditional mosquito reference genes were less stable in the analyzed samples, highlighting the possibility of inappropriate choices. A meta-transcriptome dataset with more than ninety thousand genes was also processed successfully.
conclusionThe GSV tool is a time and cost-effective tool that can be used to select reference and validation candidate genes from the biological conditions present in transcriptomic data.
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Registered trials
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