DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis. Issue 7 (17th February 2023)

• Record Type: Journal Article
• Authors: Wang, Ruheng; Jiang, Yi; Jin, Junru; Yin, Chenglin; Yu, Haoqing; Wang, Fengsheng; Feng, Jiuxin; Su, Ran; Nakai, Kenta; Zou, Quan; Wei, Leyi
• Is Part Of: Nucleic acids research. Volume 51:Issue 7(2023)
• Page Start: 3017
• Page End: 3029
• Publication Date: 2023-02-17
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkad055
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis and functional sequential region discovery. Additionally, DeepBIO supports nine base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologists and provide meaningful functional insights at both the sequence level and base level from biological sequences alone. DeepBIO is publicly available at https://inner.wei-group.net/DeepBIO . Lay Summary: The development of next-generation sequencing techniques has led to an exponential increase in the amount of biological sequence data accessible. It naturally poses a fundamental challenge—how to build the relationships from such large-scale sequences to their functions. In this work, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. It enables researchers to develop new deep-learning architectures to answer any biological question in a fully automated pipeline. We expect DeepBIO to ensure the reproducibility of deep-learning-based biological sequence analysis, lessen the programming and hardware burden for biologists and provide meaningful functional insights at both the sequence level and base level from biological sequences alone. Graphical Abstract: