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.

PacMAGI: A pipeline including accurate indel detection for the analysis of PacBio sequencing data applied to RPE65.

Third generation sequencing methods, like PacBio, provide information about structural variants, introns, enhancers and promoters. We developed an automated pipeline, called PacMAGI, including quality control, alignment, SNV, INDELs, structural variant calling, phasing, annotation and variant interpretation, for the analysis of PacBio data for any target region. Bi-allelic mutations in the RPE65 gene are associated with different inherited retinal dystrophies, such as Leber congenital amaurosis (LCA) and retinitis pigmentosa (RP). Diagnostic panel-based NGS analysis is performed on coding regions and intron/exon junctions of genes. To obtain a more conclusive diagnosis, we applied PacMAGI to obtain a second hit on RPE65 in LCA or RP patients who showed a single heterozygous variant by NGS. We used PacBio to sequence the full gene and identify putative second-hits in intronic, problematic and promoter regions. All variants identified in the diagnostic setting with NGS were correctly detected by the pipeline, and thanks to our custom algorithm for INDELs, a previously undetected 'Pathogenic' frameshift variant was found in a RP patient already identified to carry a 'Likely Pathogenic' variant.

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.