MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations

We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG classification algorithm uses information retrieved through the VarSome Application Programming Interface (API), integrates the AutoPVS1 tool in order to evaluate more precisely the attribution of the PVS1 criterion, and performs the customized assignment of specific criteria. In addition, we propose a sub-classification scheme for variants of uncertain significance (VUS) according to their proximity either towards the "likely pathogenic" or "likely benign" classes. We also conceived a pathogenicity potential criterion (P_POT) as a proxy for segregation criteria that might be added to a VUS after posterior testing, thus allowing it to upgrade its clinical significance in a diagnostic reporting setting. Finally, we have developed a user-friendly web application based on the MAGI-ACMG algorithm, available to geneticists for variant interpretation.

CNV analysis in a diagnostic setting using target panel

OBJECTIVE

Copy-number variation (CNV) is an important source of genetic diversity in humans. It can cause Mendelian or sporadic traits or be associated with complex diseases by various molecular mechanisms, including gene dosage, gene disruption, gene fusion and position effects. In clinical diagnostics, it is therefore fundamental to be able to identify such variations. The preferred techniques for CNV detection are MLPA, aCGH and qPCR, which have proven to be valuable, and they are complex, costly and require prior knowledge of the region to analyze. CNV calling from NGS data still suffers from data variability. Coverage can vary greatly from one region of the genome to another, depending on many factors like complexity, GC content, repeated regions and many others. In this paper, we describe how we developed a method for CNV detection.

MATERIALS AND METHODS

Our method exploits CoNVaDING to detect single- and multiple-exon CNVs in targeted NGS data.

RESULTS

We demonstrated that our CNV analysis has 100% specificity and 99.998% sensitivity. We also show how we evaluated the performance of this method based on internal analysis.

CONCLUSIONS

The results indicate that the method can be used to screen prior to standard labs technologies, thus reducing the number of analyses, as well as costs, and increasing test conclusiveness.

appMAGI: A complete laboratory information management system for clinical diagnostics

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. (www.actabiomedica.it)