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In recent years, significant advancements have been observed in the domain of Natural Language Processing(NLP) with the introduction of pre-trained foundational models, paving the way for utilizing similar AI technologies to interpret the language of biology. In this research, we introduce “LucaOne”, a novel pre-trained foundational model designed to integratively learn from the genetic and proteomic languages, encapsulating data from 169,861 species en-compassing DNA, RNA, and proteins. This work illuminates the potential for creating a biological language model aimed at universal bioinformatics appli-cation. Remarkably, through few-shot learning, this model efficiently learns the central dogma of molecular biology and demonstrably outperforms com-peting models. Furthermore, in tasks requiring inputs of DNA, RNA, proteins, or a combination thereof, LucaOne exceeds the state-of-the-art performance using a streamlined downstream architecture, thereby providing empirical ev-idence and innovative perspectives on the potential of foundational models to comprehend complex biological systems.