P2T2: Protein Panoramic annoTation Tool for the interpretation of protein coding genetic variants

Author:

DeVoe Elias1,Oliver Gavin R23,Zenka Roman2,Blackburn Patrick R14,Cousin Margot A23,Boczek Nicole J23,Kocher Jean-Pierre A23,Urrutia Raul567,Klee Eric W23,Zimmermann Michael T156ORCID

Affiliation:

1. Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA

2. Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA

3. Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA

4. Center for Individualized Medicine, Mayo Clinic, Jacksonville, Florida, USA

5. Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, Wisconsin, USA

6. Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA

7. Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, 53226, USA

Abstract

Abstract Motivation Genomic data are prevalent, leading to frequent encounters with uninterpreted variants or mutations with unknown mechanisms of effect. Researchers must manually aggregate data from multiple sources and across related proteins, mentally translating effects between the genome and proteome, to attempt to understand mechanisms. Materials and methods P2T2 presents diverse data and annotation types in a unified protein-centric view, facilitating the interpretation of coding variants and hypothesis generation. Information from primary sequence, domain, motif, and structural levels are presented and also organized into the first Paralog Annotation Analysis across the human proteome. Results Our tool assists research efforts to interpret genomic variation by aggregating diverse, relevant, and proteome-wide information into a unified interactive web-based interface. Additionally, we provide a REST API enabling automated data queries, or repurposing data for other studies. Conclusion The unified protein-centric interface presented in P2T2 will help researchers interpret novel variants identified through next-generation sequencing. Code and server link available at github.com/GenomicInterpretation/p2t2.

Funder

Research Computing Center at the Medical College of Wisconsin

Advancing a Healthier Wisconsin Endowment at the Medical College of Wisconsin

The Linda T. and John A. Mellowes Endowed Innovation and Discovery Fund and the Genomic Sciences and Precision Medicine Center of Medical College of Wisconsin (R.U.), and the Mayo Foundation

Mayo Clinic Center for Individualized Medicine for funding

CTSI grant National Institutes of Health CTSA

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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