A Systematic Approach to Diagnostic Laboratory Software Requirements Analysis

Overview

Journal Bioengineering (Basel)

Date 2022 Apr 21

PMID 35447704

Authors

Thomas Krause

Elena Jolkver

Paul Mc Kevitt

Michael Kramer

Matthias Hemmje

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Molecular Diagnostics in the Postgenomic Era.

Korac P, Matulic M Bioengineering (Basel). 2025; 11(12.

PMID: 39768077 PMC: 11673063. DOI: 10.3390/bioengineering11121259.

References

Behrouzi A, Nafari A, Siadat S . The significance of microbiome in personalized medicine. Clin Transl Med. 2019; 8(1):16. PMC: 6512898. DOI: 10.1186/s40169-019-0232-y. View

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A . Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002; 3(7):RESEARCH0034. PMC: 126239. DOI: 10.1186/gb-2002-3-7-research0034. View

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

Muller P, Janovjak H, Miserez A, Dobbie Z . Processing of gene expression data generated by quantitative real-time RT-PCR. Biotechniques. 2002; 32(6):1372-4, 1376, 1378-9. View

Pabinger S, Rodiger S, Kriegner A, Vierlinger K, Weinhausel A . A survey of tools for the analysis of quantitative PCR (qPCR) data. Biomol Detect Quantif. 2016; 1(1):23-33. PMC: 5129434. DOI: 10.1016/j.bdq.2014.08.002. View

Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J . qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 2007; 8(2):R19. PMC: 1852402. DOI: 10.1186/gb-2007-8-2-r19. View

Suwinski P, Ong C, Ling M, Poh Y, Khan A, Ong H . Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics. Front Genet. 2019; 10:49. PMC: 6379253. DOI: 10.3389/fgene.2019.00049. View

Wilhelm J, Pingoud A, Hahn M . SoFAR: software for fully automatic evaluation of real-time PCR data. Biotechniques. 2003; 34(2):324-32. DOI: 10.2144/03342rr03. View

Zanardi N, Morini M, Tangaro M, Zambelli F, Bosco M, Varesio L . PIPE-T: a new Galaxy tool for the analysis of RT-qPCR expression data. Sci Rep. 2019; 9(1):17550. PMC: 6879478. DOI: 10.1038/s41598-019-53155-9. View

10.

Lefever S, Hellemans J, Pattyn F, Przybylski D, Taylor C, Geurts R . RDML: structured language and reporting guidelines for real-time quantitative PCR data. Nucleic Acids Res. 2009; 37(7):2065-9. PMC: 2673419. DOI: 10.1093/nar/gkp056. View

11.

Goetz L, Schork N . Personalized medicine: motivation, challenges, and progress. Fertil Steril. 2018; 109(6):952-963. PMC: 6366451. DOI: 10.1016/j.fertnstert.2018.05.006. View

12.

Ruijter J, Ruiz Villalba A, Hellemans J, Untergasser A, van den Hoff M . Removal of between-run variation in a multi-plate qPCR experiment. Biomol Detect Quantif. 2016; 5:10-4. PMC: 4822202. DOI: 10.1016/j.bdq.2015.07.001. View

13.

Livak K, Schmittgen T . Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2002; 25(4):402-8. DOI: 10.1006/meth.2001.1262. View

14.

Barrat F, Crow M, Ivashkiv L . Interferon target-gene expression and epigenomic signatures in health and disease. Nat Immunol. 2019; 20(12):1574-1583. PMC: 7024546. DOI: 10.1038/s41590-019-0466-2. View