appMAGI: A complete laboratory information management system for clinical diagnostics

Main Article Content

Giuseppe Marceddu
Tiziano Dallavilla
Aleksander Xhuvani
Muharrem Daja
Luca De Antoni
Arianna Casadei
Matteo Bertelli

Keywords

NGS, LIMS, Diagnostics, Information management

Abstract

Background: The increasing demand for genetic testing for clinical diagnosis and research challenges genetic laboratory capacity to track an increasing number of patient samples through all steps of analysis, from sample collection to report generation. This task is usually performed with the help of a laboratory information management system (LIMS), software that makes it possible to collect, store and retrieve laboratory and sample data. To date there are no open-source options that can manage the entire analytical flow of a genetic laboratory. appMAGI seeks to include all the management aspects of a clinical diagnostic laboratory, making it simpler to process many samples while maintaining the high security and quality standards required in clinical diagnostic practice. Methods: appMAGI is written in python using Django. It is a web application that does not require local installation, making development, updates and maintenance a much easier task. appMAGI runs on the Ubuntu server and uses SQLite as engine database. Results: In this work we describe an innovative LIMS called appMAGI designed to support all aspects of a clinical diagnostic laboratory. appMAGI can track samples throughout the diagnostic workflow and NGS analysis by virtue of a customizable bioinformatics pipeline. It can handle sample non-compliance, manage laboratory stocks, help generate reports and provide insights into sample data by means of special tools. Conclusions: appMAGI is a LIMS endowed with all the features required to manage thousands of samples. Allowing efficient management of patient samples from sample collection to diagnostic report generation.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Abstract 17 |

References

[1] Su Z, Ning B, Fang H, et al. Next-generation sequencing and its applications in molecular diagnostics. Expert Rev Mol Diagn 2011; 11: 333–43.
[2] Sommariva E, Pappone C, Martinelli Boneschi F, et al. Genetics can contribute to the prognosis of Brugada syndrome: a pilot model for risk stratification. Eur J Hum Genet 2013; 21: 911–7.
[3] Daoud H, Luco SM, Li R, et al. Next-generation sequencing for diagnosis of rare diseases in the neonatal intensive care unit. CMAJ 2016; 188: 254–60.
[4] Rehm HL. Disease-targeted sequencing: a cornerstone in the clinic. Nat Rev Genet 2013; 14: 295–300.
[5] LePichon JB, Saunders CJ, Soden SE. The future of next-generation sequencing in neurology. JAMA Neurol 2015; 72: 971–2.
[6] Peters DG, Yatsenko SA, Surti U, Rajkovic A. Recent advances of genomic testing in perinatal medicine. Semin Perinatol 2015; 39: 44–54.
[7] Phillips KA, Deverka PA, Hooker GW, Douglas MP. Genetic test availability and spending: Where are we now? Where are we going? Health Aff (Millwood) 2018; 37: 710–6.
[8] Scholtalbers J, Rößler J, Sorn P, et al. Galaxy LIMS for next-generation sequencing. Bioinformatics 2013; 29: 1233–4.
[9] Van Rossum T, Tripp B, Daley D. Slims – A user-friendly sample operations and inventory management system for genotyping labs. Bioinformatics 2010; 26: 1808–10.
[10] Bath T, Bozdag S, Afzal V, Crowther D. Limsportal and bonsailims: Development of a lab information management system for translational medicine. Source Code Biol Med 2011; 6: 9.
[11] Grimes S, Ji H. Mendelims: A web-based laboratory information management system for clinical genome sequencing. BMC Bioinformatics 2014; 15: 290.
[12] Viksna J, Celms E, Opmanis M, et al. Passim - An open source software system for managing information in biomedical studies. BMC Bioinformatics 2007; 8: 52.
[13] Marceddu G, Dallavilla T, Guerri G, Manara E, Bertelli M. PipeMAGI: An integrated and validated workflow for analysis of NGS data for clinical diagnostics. Eur Rev Med Pharmacol Sci 2019; 23: 12.
[14] Maltese PE, Orlova N, Krasikova E, et al. Gene-targeted analysis of clinically diagnosed long QT russian families. Int Heart J 2017; 1: 81–7.
[15] Marceddu G, Dallavilla T, Guerri G, Zulian A, Marinelli C, Bertelli M. Analysis of machine learning algorithms as integrative tools for validation of next generation sequencing data. Eur Rev Med Pharmacol Sci 2019; 23: 8139–47.
[16] Johansson LF, van Dijk F, de Boer EN, et al. CoNVaDING: Single exon variation detection in targeted NGS data. Hum Mutat 2016 ;37: 457–64.