Ruheng Wang, Yi Jiang, Junru Jin, Chenglin Yin, Haoqing Yu, Fengsheng Wang, Jiuxin Feng, Ran Su, Kenta Nakai, Quan Zou, Leyi Wei
Nucleic Acids Research, gkad055, https://doi.org/10.1093/nar/gkad055
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 sequence-level and base-level from biological sequences alone.