The mRNA and protein datasets after cold stress of red tilapia.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Lanmei Wang, Haoran Yang, Herbert Brightmore Munyaradzia, Wenbin Zhu, Zai-Jie Dong
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引用次数: 0

Abstract

The cold stress during overwintering is considered the bottleneck of red tilapia industry. In this study, the water temperature (WT) was reduced by 2 °C per day from 20 °C to 8 °C in the cold (C) group. Then transcriptome of brain(B), gill(G), liver(L) and skin(S) tissues and proteome of G, L and S tissues were performed in C and Normal (N) (WT: 20 °C) group. 24 transcriptomes were completed, and 168.8 Gb data were obtained, with more than 5.89 Gb clean data of each sample. A total of 30499 annotation results were obtained with 3199, 4697, 4393, and 3382 differentially expressed mRNAs in NB_vs_CB, NG_vs_CG, NL_vs_CL, NS_vs_CS. 18 DIA proteomes were performed, and 6341 proteins were obtained with 178, 500 and 166 differentially expressed proteins in NG_vs_CG, NL_vs_CL, NS_vs_CS. Our datasets can be reused for key genes and proteins identification, omics joint analysis and regulatory mechanism analysis of low temperature or cold stress in fish, which will help understanding the regulatory mechanism and facilitate the molecular selective breeding of cold-resistant varieties of fish.

红罗非鱼冷应激后的 mRNA 和蛋白质数据集。
越冬期间的低温胁迫被认为是红罗非鱼产业的瓶颈。在本研究中,低温组(C)的水温(WT)每天降低2 °C,从20 °C降至8 °C。然后对 C 组和正常组(WT:20 °C)的脑(B)、鳃(G)、肝(L)和皮肤(S)组织的转录组和 G、L 和 S 组织的蛋白质组进行研究。共完成了 24 个转录组,获得了 168.8 Gb 数据,每个样本的干净数据超过 5.89 Gb。在 NB_vs_CB、NG_vs_CG、NL_vs_CL、NS_vs_CS 中分别获得了 3199、4697、4393 和 3382 个差异表达的 mRNA,共得到 30499 个注释结果。在 NB_vs_CB、NG_vs_CG、NL_vs_CL、NS_vs_CS 中,进行了 18 个 DIA 蛋白体组的研究,获得了 6341 个蛋白质,其中 178 个、500 个和 166 个蛋白质有差异表达。我们的数据集可用于鱼类低温或寒冷胁迫的关键基因和蛋白质鉴定、omics联合分析和调控机制分析,这将有助于理解调控机制,促进抗寒鱼类品种的分子选育。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Data
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.
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