Genopyc: a Python library for investigating the functional effects of genomic variants associated to complex diseases

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Understanding the genetic basis of complex diseases is a major challenge in genomics.
• Existing tools struggle to analyze the complex relationships between genetic variations and disease outcomes.
• There is a need for versatile tools to integrate and analyze large-scale genomic data for disease research.

Purpose of the Study:

• Introduce Genopyc, a novel Python library for comprehensive investigation of genetic variations in complex diseases.
• Enable researchers to analyze how genetic variants affect downstream pathways and disease mechanisms.
• Facilitate the integration and visualization of heterogeneous data for large-scale genomic datasets.

Main Methods:

• Developed Genopyc, a Python library with functions for heterogeneous data mining and visualization.
• Applied Genopyc to real-world genome-wide association study (GWAS) variants.
• Utilized Genopyc to investigate functional consequences of variants associated with intervertebral disc degeneration.

Main Results:

• Genopyc facilitates a deeper understanding of potential dysregulated pathways in complex diseases.
• The library enables exploration and visualization of functional consequences of genetic variants.
• Demonstrated Genopyc's utility in analyzing genome-wide association study data for disease insights.

Conclusions:

• Genopyc is a powerful tool for researchers investigating complex diseases and their genetic underpinnings.
• The library aids in uncovering potential therapeutic targets by elucidating disease-associated pathways.
• Genopyc streamlines the analysis of genomic data, paving the way for more targeted therapeutic interventions.