Cruxome: a powerful tool for annotating, interpreting and reporting genetic variants

Area of Science:

• Genomics
• Bioinformatics
• Medical Genetics

Background:

• Next-generation sequencing (NGS) is crucial for identifying pathogenic variants in Mendelian disorders.
• Challenges include limited bioinformatics expertise for variant interpretation and non-standardized disease phenotyping.
• Efficient solutions are needed to streamline variant annotation and interpretation.

Purpose of the Study:

• To develop an automated tool, Cruxome, for annotating and interpreting single nucleotide variants (SNVs) and small insertions/deletions (InDels).
• To simplify the process for geneticists and scientists in identifying causative pathogenic variants and building knowledge bases.
• To address the need for integrated analysis pathways in genetic variant interpretation.

Main Methods:

• Developed Cruxome with an interactive, user-friendly interface integrating electronic health records.
• Utilized natural language processing to standardize clinical disease descriptions to Human Phenotype Ontology (HPO) vocabularies.
• Employed machine learning, in silico algorithms, multiple databases, and supplementary tools for variant processing and interpretation following ACMG/AMP guidelines.

Main Results:

• Cruxome automatically annotates, scores, identifies, and interprets SNVs and InDels from patient data (trio or proband-only).
• Generates standardized clinical reports aligned with ACMG/AMP guidelines.
• Provides tools for visualizing genes and variations in historical cases to aid disease understanding.

Conclusions:

• Cruxome efficiently annotates and interprets genetic variations, significantly reducing workload for clinical geneticists and researchers.
• The tool simplifies decision-making processes in interpreting NGS results.
• An online, freely accessible version of Cruxome is available for academic and clinical researchers.