A Transcriptomic Dataset of Liver Tissues from Global and Liver-Specific Bmal1 Knockout Mice.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-03 DOI:10.1038/s41597-025-04545-4

Guohao Han, Dan Li, Haisen Zhang, Chao Li, Luda Yang, Tiantian Ma, Xuerong Wang, Bairong Ma, Xiaodie Wu, Yang Tao, Ziang Wang, Aihua Wang, Hsu-Wen Chao, Yaping Jin, Huatao Chen

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引用次数: 0

Abstract

The circadian clock regulates various physiological processes in mammals. The core circadian clock gene Bmal1 is crucial for maintaining the oscillations of the circadian clock system by controlling the rhythmic expression of numerous circadian clock-controlled genes. To explore the transcriptional changes associated with Bmal1 deletion in liver tissues, we collected liver tissues from global and liver-specific Bmal1 knockout mice, along with their respective control groups, at two circadian time points (CT2 and CT14) and used them for transcriptome sequencing analysis. Genotyping, locomotor activity analysis, and comprehensive quality control analyses, including base quality scores, GC content, and mapping rates, confirmed the high quality of sequencing data. Differential expression analysis and RT-qPCR validation confirmed the reliability and validity of the dataset. These data offer a valuable resource for researchers investigating the role of BMAL1 in liver physiology, pathology, and the broader field of circadian biology.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.