PIPE-T: a new Galaxy tool for the analysis of RT-qPCR expression data

Abstract

Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is an accurate and fast method to measure gene expression. Reproducibility of the analyses is the main limitation of RT-qPCR experiments. Galaxy is an open, web-based, genomic workbench for a reproducible, transparent, and accessible science. Our aim was developing a new Galaxy tool for the analysis of RT-qPCR expression data. Our tool was developed using Galaxy workbench version 19.01 and functions implemented in several R packages. We developed PIPE-T, a new Galaxy tool implementing a workflow, which offers several options for parsing, filtering, normalizing, imputing, and analyzing RT-qPCR data. PIPE-T requires two input files and returns seven output files. We tested the ability of PIPE-T to analyze RT-qPCR data on two example datasets available in the gene expression omnibus repository. In both cases, our tool successfully completed execution returning expected results. PIPE-T can be easily installed from the Galaxy main tool shed or from Docker. Source code, step-by-step instructions, and example files are available on GitHub to assist new users to install, execute, and test PIPE-T. PIPE-T is a new tool suitable for the reproducible, transparent, and accessible analysis of RT-qPCR expression data.

Circulating exosomal microRNAs as potential biomarkers of hepatic injury and inflammation in a murine model of glycogen storage disease type 1a

“…Data processing, categorization, normalization, filtering, imputation and differential expression were carried out using the PIPE-T galaxy tool ( Zanardi et al, 2019 ). The Ct values falling within the range 14-32 were categorized as reliable values as recommended by the guidelines of the manufacturer.…”

“…The Ct values falling within the range 14-32 were categorized as reliable values as recommended by the guidelines of the manufacturer. Global mean normalization was used to reduce any technical variability introduced in the data by the RT-qPCR experiments ( Zanardi et al, 2019 ). Only Exo-miRs with ≤5% of missing values were retained for the analysis to reduce the bias introduced by imputation.…”

“…Only Exo-miRs with ≤5% of missing values were retained for the analysis to reduce the bias introduced by imputation. The Mestdagh method ( Zanardi et al, 2019 ) was used to assign a numeric expression value to missing values. The rank product method ( Zanardi et al, 2019 ) implemented with the RankProd R package was used to identify significant differentially expressed microRNAs.…”

“…The Mestdagh method ( Zanardi et al, 2019 ) was used to assign a numeric expression value to missing values. The rank product method ( Zanardi et al, 2019 ) implemented with the RankProd R package was used to identify significant differentially expressed microRNAs. Pathway analysis was performed for both predicted and validated targets of an Exo-miR using mirWalk version 3.0 ( Sticht et al, 2018 ) and carried out using GO and KEGG gene set collections.…”

Auto-qPCR: A Python-based web app for automated and reproducible analysis of qPCR data

“…Together, Auto-qPCR provides an all-in-one solution for the user, going from datasets to graphs, all within one web-based software package. While other open source qPCR analysis software programs and web apps (Rancurel et al, 2019; Zanardi et al, 2019; Krahenbuhl et al, 2020) are available, they are only able to normalize, compare and display qPCR data generated with one of the two models of quantification (Bustin, 2000; Pfaffl, 2001). In contrast, Auto-qPCR provides a comprehensive data analysis package for qPCR experiments.…”