An integrated approach in gene-expression landscape profiling to identify housekeeping and tissue-specific genes in cattle
Peng Li A , Yun Zhu
A , Xiaolong Kang A , Xingang Dan A , Yun Ma A and Yuangang Shi A B
+ Author Affiliations
- Author Affiliations
A College of Agriculture, Ningxia University, Helan Mountain West Road 489, 750021, Yinchuan, Ningxia Hui Autonomous Region, China.
B Corresponding author. Email: shi_yg@nxu.edu.cn
Animal Production Science 61(16) 1643-1651 https://doi.org/10.1071/AN20689
Submitted: 25 December 2020 Accepted: 7 July 2021 Published: 13 September 2021
Journal Compilation © CSIRO 2021 Open Access CC BY-NC-ND
Abstract
Context: High-throughput transcriptome sequencing (RNA-Seq) has been widely applied in cattle studies. Public databases such as the National Center for Biotechnology Information (NCBI) contain large collections of gene expression data from various cattle tissues that can be used in gene expression analysis research
Aims: This study was conducted to investigate patterns of transcriptome variation across tissues of cattle through large-scale identification of housekeeping genes (i.e. those crucial to maintaining basic cellular activity) and tissue-specific genes in cattle tissues.
Methods: Using data available in the NCBI Sequence Read Archive database, we analysed 1377 transcriptome data sequences from 60 bovine tissue types, identified tissue-specific and housekeeping genes, and set up a web-based bovine gene expression analysis tool.
Key results: We found 101 genes widely expressed in almost all tissue and screened out five housekeeping genes: RPL35A, eIF4A2, GAPDH, IPO5 and PAK2. Focusing on 12 major organs, we found 861 genes specifically expressing in these tissues. Furthermore, 187 significantly differentially expressed genes were found among six types of muscle tissues. All expression data were made available at our new website http://cattleExp.org, which can be freely accessed for future gene expression analyses.
Conclusions: The housekeeping genes and tissue-specific genes identified will provide more information for researchers studying gene expression in cattle.
Implications: The web-based cattle gene expression analysis tool will make it easy for researchers to access large public datasets. Users can easily access all publicly available RNA data and upload their own RNA-Seq data.
Key words: beef cattle, bioinformatics, differentially expressed genes, gene regulation, genomics, muscle tissue, public database, reverse genetics, RNA-Seq.
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