The need for a robust multiomics data integration solution arises from significant challenges researchers face in accessing, analyzing, and integrating diverse omics data types. Current workflows are often complex and not user-friendly, particularly for non-experts, hindering a comprehensive understanding of the relationship between genomics and other molecular modalities. This understanding is crucial for advancing personalized medicine and leveraging multiomics data for population and precision health studies. Here we report on an advanced data integration workflow that seamlessly integrates various proteomics and post translational modification data, including targeted, discovery, or hybrid, with genomic sequence analysis obtained through whole genome sequencing, arrays and RNA-seq, enabling researchers to check on specific genes or gene sets and retrieve transcriptomics and proteomics toward enhanced biological insights.
Here we present a workflow for comprehensive integration of proteomics, transcriptomics and genomics data, providing a unified integrated data analysis strategy. Advanced AI algorithms are evaluated for handling diverse multiomics datasets, for improvement of the proteogenomics association insights. Rigorous testing and validation ensure the platform's accuracy and reliability, and base line performance based on published, public datasets.
We plan to present our preliminary result utilizing public proteogenomic data repositories provides an enhanced integration workflow. We report the disease-related multiomics profile, providing valuable insights into the molecular mechanisms underlying diseases like cancer as a proof-of-concept study and in a 500 sample Pan-Cancer study.
This AI-powered integrated multiomics workflow changes the way researchers’ access and analyze multiomics data for small-large cohort studies. With its comprehensive integration and user-friendly design, the platform will facilitate a deeper understanding of the relationship between genetic variations and protein changes, including post-translational modifications (PTMs) for example. This enables translational researchers to gain valuable insights into biological processes, potentially identifying novel therapeutic targets. The platform will democratize integrative multiomics profiling, foster collaboration within the scientific community and contributing to advancements in personalized medicine and drug development.
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