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RNA-seq数据差异表达分析流程比较
赵延辉1,陈少康2,翟丽维3,侍玉梅1,原佳妮1,盛熙晖1,齐晓龙1,郭勇1,王楚端3,邢凯1*
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(1.北京农学院 动物科学技术学院, 北京 102206;2.北京市畜牧总站, 北京 100107;3.中国农业大学 动物科学技术学院, 北京 100193)
摘要:
为选择出合适的基因差异表达分析流程,本研究基于松辽黑猪和长白猪脂肪转录组数据,对TopHat2、HISAT2、STAR3种比对工具以及DESeq2、edgeR、limma 3种差异表达基因筛选工具的性能进行分析,并结合KEGG通路富集结果进行综合评价。结果表明:1)HISAT2拥有最快的运行速度,STAR拥有最高的唯一比对率。经综合考虑,本研究选取HISAT2数据进行后续差异表达基因筛选分析。2)DESeq2筛选出616个差异基因,edgeR筛选出890个差异基因,limma筛选出829个差异基因,三者有246个差异基因重合。3)DESeq2、edgeR、limma的上调差异表达基因分别富集到110、108和142条通路,其中有72条通路重合,而下调差异表达基因分别富集到190、247和177条通路,其中有158条通路重合。本研究推荐使用HISAT2进行基因组比对。当研究不存在生物学重复时,推荐使用edgeR进行差异表达基因筛选。而为了减少分析过程中的假阳性,可以选择DESeq2或者两个及以上工具的差异表达基因的交集。本研究将有助于研究人员从转录组数据中获得更好、更全面的生物学见解。
关键词:  差异表达  RNA-seq  分析工具
DOI:10.11841/j.issn.1007-4333.2023.06.14
投稿时间:2022-09-25
基金项目:2022年北京市教委分类发展项目
Comparative study on differential expression analysis process of RNA-seq data
ZHAO Yanhui1,CHEN Shaokang2,ZHAI Liwei3,SHI Yumei1,YUAN Jiani1,SHENG Xihui1,QI Xiaolong1,GUO Yong1,WANG Chuduan3,XING Kai1*
(1. School of Animal Science and Technology, Beijing University of Agricultural, Beijing 102206, China;2. Beijing Animal Husbandry Station, Beijing 100107, China;3. School of Animal Science and Technology, China Agricultural University, Beijing 100193, China)
Abstract:
To select an appropriate process for gene differential expression analysis, the study analyzed the performance of three comparison tools TopHat2, HISAT2 and STAR and three differentially expressed gene screening tools DESeq2, edgeR and limma based on the fat transcriptome data of Songliao Black Pig and landrace pig, and comprehensively evaluated the performance in combination with the enrichment results of KEGG pathway. The results show that: 1)HISAT2 has the fastest running speed and STAR has the highest unique mapping ratio. After comprehensive consideration, this study selects HISAT2 data for subsequent screening and analysis of differentially expressed genes. 2)616 differential genes were screened by DESeq2, 890 differential genes were screened by edgeR and 829 differential genes were screened by limma, and 246 differential genes overlapped among the three. 3)The up-regulated differentially expressed genes of DESeq2, edgeR, and limma were enriched to 110, 108, and 142 pathways respectively, of which 72 were overlapped, while the down-regulated differentially expressed genes were enriched to 190, 247, and 177 pathways, of which 158 were overlapped. HISAT2 is recommended for genome mapping in this study. When there is no biological duplication, edgeR is recommended to screen differentially expressed genes. In order to reduce false positives during analysis, DESeq2 or the intersection of differentially expressed genes of two or more tools can be selected. The results of this study will help researchers obtain better and more comprehensive biological insights from transcriptome data.
Key words:  differential expression  RNA-seq  analysis tools