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bioRxiv · 10.1101/2022.06.08.495354

Highly accurate isoform identification for the human transcriptome

Sommer, M. J.·Cha, S.·Varabyou, A.·Rincon, N.·Park, S.·Minkin, I.·Pertea, M.·Steinegger, M.·Salzberg, S.
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Abstract

We explore a new hypothesis in genome annotation, namely whether computationally predicted protein structures can help to identify which of multiple possible gene isoforms represents a functional protein product. Guided by structure predictions, we evaluated over 140,000 isoforms of human protein-coding genes assembled from over 10,000 RNA sequencing experiments across many human tissues. We illustrate our new method with examples where structure provides a guide to function in combination with expression and evolutionary evidence. Additionally, we provide the complete set of structures as a resource to better understand the function of human genes and their isoforms. These results demonstrate the promise of protein structure prediction as a genome annotation tool, allowing us to refine even the most highly-curated catalog of human proteins. One-Sentence SummaryWe describe the use of 3D protein structures on a genome-wide scale to evaluate human protein isoforms for biological functionality.

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Sommer, M. J., Cha, S., Varabyou, A., Rincon, N., Park, S., Minkin, I., Pertea, M., Steinegger, M., Salzberg, S.. 2022-06-09. Highly accurate isoform identification for the human transcriptome. https://doi.org/10.1101/2022.06.08.495354

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