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

Improving the quality of quantitative polymerase chain reaction experiments: 15 years of MIQE

The quantitative polymerase chain reaction (qPCR) is fundamental to molecular biology. It is not just a laboratory technique, qPCR is a bridge between research and clinical practice. Its theoretical foundations guide the design of experiments, while its practical implications extend to diagnostics, treatment, and research advancements in the life sciences, human and veterinary medicine, agriculture, and forensics. However, the accuracy, reliability and reproducibility of qPCR data face challenges arising from various factors associated with experimental design, execution, data analysis and inadequate reporting details. Addressing these concerns, the Minimum Information for the Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines have emerged as a cohesive framework offering a standardised set of recommendations that describe the essential information required for assessing qPCR experiments. By emphasising the importance of methodological rigour, the MIQE guidelines have made a major contribution to improving the trustworthiness, consistency, and transparency of many published qPCR results. However, major challenges related to awareness, resources, and publication pressures continue to affect their consistent application.

deltaXpress (ΔXpress): a tool for mapping differentially correlated genes using single-cell qPCR data

BACKGROUND

High-throughput experiments provide deep insight into the molecular biology of different species, but more tools need to be developed to handle this type of data. At the transcriptomics level, quantitative Polymerase Chain Reaction technology (qPCR) can be affordably adapted to produce high-throughput results through a single-cell approach. In addition to comparative expression profiles between groups, single-cell approaches allow us to evaluate and propose new dependency relationships among markers. However, this alternative has not been explored before for large-scale qPCR-based experiments.

RESULTS

Herein, we present deltaXpress (ΔXpress), a web app for analyzing data from single-cell qPCR experiments using a combination of HTML and R programming languages in a friendly environment. This application uses cycle threshold (Ct) values and categorical information for each sample as input, allowing the best pair of housekeeping genes to be chosen to normalize the expression of target genes. ΔXpress emulates a bulk analysis by observing differentially expressed genes, but in addition, it allows the discovery of pairwise genes differentially correlated when comparing two experimental conditions. Researchers can download normalized data or use subsequent modules to map differentially correlated genes, perform conventional comparisons between experimental groups, obtain additional information about their genes (gene glossary), and generate ready-to-publication images (600 dots per inch).

CONCLUSIONS

ΔXpress web app is freely available to non-commercial users at https://alexismurillo.shinyapps.io/dXpress/ and can be used for different experiments in all technologies involving qPCR with at least one housekeeping region.

Wastewater-Based Epidemiology for COVID-19: Handling qPCR Nondetects and Comparing Spatially Granular Wastewater and Clinical Data Trends

Wastewater-based epidemiology (WBE) is a useful complement to clinical testing for managing COVID-19. While community-scale wastewater and clinical data frequently correlate, less is known about subcommunity relationships between the two data types. Moreover, nondetects in qPCR wastewater data are typically handled through methods known to bias results, overlooking perhaps better alternatives. We address these knowledge gaps using data collected from September 2020-June 2021 in Davis, California (USA). We hypothesize that coupling the expectation maximization (EM) algorithm with the Markov Chain Monte Carlo (MCMC) method could improve estimation of "missing" values in wastewater qPCR data. We test this hypothesis by applying EM-MCMC to city wastewater treatment plant data and comparing output to more conventional nondetect handling methods. Dissimilarities in results (i) underscore the importance of specifying nondetect handling method in reporting and (ii) suggest that using EM-MCMC may yield better agreement between community-scale clinical and wastewater data. We also present a novel framework for spatially aligning clinical data with wastewater data collected upstream of a treatment plant (i.e., distributed across a sewershed). Applying the framework to data from Davis reveals reasonable agreement between wastewater and clinical data at highly granular spatial scales-further underscoring the public-health value of WBE.

qRAT: an R-based stand-alone application for relative expression analysis of RT-qPCR data

Background

Reverse transcription quantitative real-time PCR (RT-qPCR) is a well-established method for analysing gene expression. Most RT-qPCR experiments in the field of microbiology aim for the detection of transcriptional changes by relative quantification, which means the comparison of the expression level of a specific gene between different samples by the application of a calibration condition and internal reference genes. Due to the numerous data processing procedures and factors that can influence the final result, relative expression analysis and interpretation of RT-qPCR data are still not trivial and often necessitate the use of multiple separate software packages capable of performing specific functions.

Results

Here we present qRAT, a stand-alone desktop application based on R that automatically processes raw output data from any qPCR machine using well-established and state-of-the-art statistical and graphical techniques. The ability of qRAT to analyse RT-qPCR data was evaluated using two example datasets generated in our laboratory. The tool successfully completed the procedure in both cases, returning the expected results. The current implementation includes functionalities for parsing, filtering, normalizing and visualisation of relative RT-qPCR data, like the determination of the relative quantity and the fold change of differentially expressed genes as well as the correction of inter-plate variation for multiple-plate experiments.

Conclusion

qRAT provides a comprehensive, straightforward, and easy-to-use solution for the relative quantification of RT-qPCR data that requires no programming knowledge or additional software installation. All application features are available for free and without requiring a login or registration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04823-7.

A Systematic Approach to Diagnostic Laboratory Software Requirements Analysis

Genetics plays an ever-increasing role in medical diagnostics. The requirements for laboratory diagnostics are constantly changing due to new emerging diagnostic procedures, methodologies, devices, and regulatory requirements. Standard software already available for laboratories often cannot keep up with the latest developments or is focused on research rather than process automation. Although the software utilized in diagnostic laboratories is subject to regulatory requirements, there is no well-defined formal procedure for software development. Reference models have been developed to formalize these solutions, but they do not facilitate the initial requirements analysis or the development process itself. A systematic requirements engineering process is however not only essential to ensure the quality of the final product but is also required by regulations such as the European In Vitro Diagnostic Regulation and international standards such as IEC 62304. This paper shows, by example, the systematic requirements analysis of a system for qPCR-based (quantitative polymerase chain reaction) gene expression analysis. Towards this goal, a multi-step research approach was employed, which included literature review, user interviews, and market analysis. Results revealed the complexity of the field with many requirements to be considered for future implementation.

Exosomal MicroRNAs as Potential Biomarkers of Hepatic Injury and Kidney Disease in Glycogen Storage Disease Type Ia Patients

Glycogen storage disease type Ia (GSDIa) is an inherited metabolic disorder caused by mutations in the enzyme glucose-6-phosphatase-α (G6Pase-α). Affected individuals develop renal and liver complications, including the development of hepatocellular adenoma/carcinoma and kidney failure. The purpose of this study was to identify potential biomarkers of the evolution of the disease in GSDIa patients. To this end, we analyzed the expression of exosomal microRNAs (Exo-miRs) in the plasma exosomes of 45 patients aged 6 to 63 years. Plasma from age-matched normal individuals were used as controls. We found that the altered expression of several Exo-miRs correlates with the pathologic state of the patients and might help to monitor the progression of the disease and the development of late GSDIa-associated complications.

Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service

BACKGROUND

Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of "on-demand" Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server's initial configuration and deployment.

RESULTS

"Laniakea@ReCaS", the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform.

CONCLUSIONS

During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers' work.

Auto-qPCR; a python-based web app for automated and reproducible analysis of qPCR data

Quantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app (https://auto-q-pcr.com/) is easy to use and does not require programming knowledge or software installation. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events; (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample. Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs.

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

Most patients affected by glycogen storage disease type 1a (GSD1a), an inherited metabolic disorder caused by mutations in the enzyme glucose-6-phosphatase-α (G6Pase-α), develop renal and liver complications, including the development of hepatocellular adenoma/carcinoma. The purpose of this study was to identify potential biomarkers of the pathophysiology of the GSD1a-affected liver. To this end, we used the plasma exosomes of a murine model of GSD1a, the LS-G6pc ^{-/ -} mouse, to uncover the modulation in microRNA expression associated with the disease. The microRNAs differentially expressed between LS-G6pc ^-/- and wild-type mice, LS-G6pc ^-/- mice with hepatocellular adenoma and LS-G6pc ^-/- mice without adenoma, and LS-G6pc ^-/- mice with amyloidosis and LS-G6pc ^-/- mice without amyloidosis were identified. Pathway analysis demonstrated that the target genes of the differentially expressed microRNA were significantly enriched for the insulin signaling pathway, glucose and lipid metabolism, Wnt/β-catenin, telomere maintenance and hepatocellular carcinoma, and chemokine and immune regulation signaling pathways. Although some microRNAs were common to the different pathologic conditions, others were unique to the cancerous or inflammatory status of the animals. Therefore, the altered expression of several microRNAs is correlated with various pathologic liver states and might help to distinguish them during the progression of the disease and the development of late GSD1a-associated complications.

Laniakea: an open solution to provide Galaxy "on-demand" instances over heterogeneous cloud infrastructures

BACKGROUND

While the popular workflow manager Galaxy is currently made available through several publicly accessible servers, there are scenarios where users can be better served by full administrative control over a private Galaxy instance, including, but not limited to, concerns about data privacy, customisation needs, prioritisation of particular job types, tools development, and training activities. In such cases, a cloud-based Galaxy virtual instance represents an alternative that equips the user with complete control over the Galaxy instance itself without the burden of the hardware and software infrastructure involved in running and maintaining a Galaxy server.

RESULTS

We present Laniakea, a complete software solution to set up a "Galaxy on-demand" platform as a service. Building on the INDIGO-DataCloud software stack, Laniakea can be deployed over common cloud architectures usually supported both by public and private e-infrastructures. The user interacts with a Laniakea-based service through a simple front-end that allows a general setup of a Galaxy instance, and then Laniakea takes care of the automatic deployment of the virtual hardware and the software components. At the end of the process, the user gains access with full administrative privileges to a private, production-grade, fully customisable, Galaxy virtual instance and to the underlying virtual machine (VM). Laniakea features deployment of single-server or cluster-backed Galaxy instances, sharing of reference data across multiple instances, data volume encryption, and support for VM image-based, Docker-based, and Ansible recipe-based Galaxy deployments. A Laniakea-based Galaxy on-demand service, named Laniakea@ReCaS, is currently hosted at the ELIXIR-IT ReCaS cloud facility.

CONCLUSIONS

Laniakea offers to scientific e-infrastructures a complete and easy-to-use software solution to provide a Galaxy on-demand service to their users. Laniakea-based cloud services will help in making Galaxy more accessible to a broader user base by removing most of the burdens involved in deploying and running a Galaxy service. In turn, this will facilitate the adoption of Galaxy in scenarios where classic public instances do not represent an optimal solution. Finally, the implementation of Laniakea can be easily adapted and expanded to support different services and platforms beyond Galaxy.