Mohamed Salem, Rafet Al-Tobasei, Ali Ali, Liqi An, Ying Wang, Xuechen Bai, Ye Bi, Huaijun Zhou
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引用次数: 0
Abstract
Rainbow trout (RBT) has gained widespread attention as a biological model across various fields and has been rapidly adopted for aquaculture and recreational purposes on 6 continents. Despite significant efforts to develop genome sequences for RBT, the functional genomic basis of RBT's environmental, phenotypic, and evolutionary variations still requires epigenome reference annotations. This study has produced a comprehensive catalog and epigenome annotation tracks of RBT, detecting gene regulatory elements, including chromatin histone modifications, chromatin accessibility, and DNA methylation. By integrating chromatin immunoprecipitation sequencing, ATAC sequencing, Methyl Mini-seq, and RNA sequencing data, this new regulatory element catalog has helped to characterize the epigenome dynamics and its correlation with gene expression. The study has also identified potential causal variants and transcription factors regulating complex domestication phenotypic traits. This research also provides valuable insights into the epigenome's role in gene evolution and the mechanism of duplicate gene retention 100 million years after RBT whole-genome duplication and during re-diploidization. The newly developed epigenome annotation maps are among the first in fish and are expected to enhance the accuracy and efficiency of genomic studies and applications, including genome-wide association studies, causative variation identification, and genomic selection in RBT and fish comparative genomics.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.