Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models
Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF deposition. Machine learning (ML) is a new big data fitting method that can effectively fit complex data, accurately identify samples and genes, and i...
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| Format: | Article |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2024.1503148/full |
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| author | Yumei Shi Yumei Shi Xini Wang Shaokang Chen Yanhui Zhao Yan Wang Xihui Sheng Xiaolong Qi Lei Zhou Yu Feng Jianfeng Liu Chuduan Wang Kai Xing |
| author_facet | Yumei Shi Yumei Shi Xini Wang Shaokang Chen Yanhui Zhao Yan Wang Xihui Sheng Xiaolong Qi Lei Zhou Yu Feng Jianfeng Liu Chuduan Wang Kai Xing |
| author_sort | Yumei Shi |
| collection | DOAJ |
| description | Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF deposition. Machine learning (ML) is a new big data fitting method that can effectively fit complex data, accurately identify samples and genes, and it plays an important role in omics research. Therefore, this study aimed to analyze RNA-seq data by ML method to identify differentially expressed genes (DEGs) affecting IMF deposition in pigs. In this study, a total of 74 RNA-seq data from muscle tissue samples were used. A total of 155 DEGs were identified using a limma package between the two groups. 100 and 11 significant genes were identified by support vector machine recursive feature elimination (SVM-RFE) and random forest (RF) models, respectively. A total of six intersecting genes were in both models. KEGG pathway enrichment analysis of the intersecting genes revealed that these genes were enriched in pathways associated with lipid deposition. These pathways include α-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism. Four key genes affecting intramuscular fat deposition, PLA2G6, MPV17, NUDT2, and ND4L, were identified based on significant pathways. The results of this study are important for the elucidation of the molecular regulatory mechanism of intramuscular fat deposition and the effective improvement of IMF content in pigs. |
| format | Article |
| id | doaj-art-790519b8ef1948d686e7fc16a1be798f |
| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-790519b8ef1948d686e7fc16a1be798f2025-01-06T06:59:07ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-01-011510.3389/fgene.2024.15031481503148Identification of key genes affecting intramuscular fat deposition in pigs using machine learning modelsYumei Shi0Yumei Shi1Xini Wang2Shaokang Chen3Yanhui Zhao4Yan Wang5Xihui Sheng6Xiaolong Qi7Lei Zhou8Yu Feng9Jianfeng Liu10Chuduan Wang11Kai Xing12College of Animal Science and Technology, China Agricultural University, Beijing, ChinaCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing, ChinaCollege of Animal Science and Technology, China Agricultural University, Beijing, ChinaBeijing Animal Husbandry Station, Beijing, ChinaCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing, ChinaCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing, ChinaCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing, ChinaCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing, ChinaCollege of Animal Science and Technology, China Agricultural University, Beijing, ChinaCollege of Animal Science and Technology, China Agricultural University, Beijing, ChinaCollege of Animal Science and Technology, China Agricultural University, Beijing, ChinaCollege of Animal Science and Technology, China Agricultural University, Beijing, ChinaCollege of Animal Science and Technology, China Agricultural University, Beijing, ChinaIntramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF deposition. Machine learning (ML) is a new big data fitting method that can effectively fit complex data, accurately identify samples and genes, and it plays an important role in omics research. Therefore, this study aimed to analyze RNA-seq data by ML method to identify differentially expressed genes (DEGs) affecting IMF deposition in pigs. In this study, a total of 74 RNA-seq data from muscle tissue samples were used. A total of 155 DEGs were identified using a limma package between the two groups. 100 and 11 significant genes were identified by support vector machine recursive feature elimination (SVM-RFE) and random forest (RF) models, respectively. A total of six intersecting genes were in both models. KEGG pathway enrichment analysis of the intersecting genes revealed that these genes were enriched in pathways associated with lipid deposition. These pathways include α-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism. Four key genes affecting intramuscular fat deposition, PLA2G6, MPV17, NUDT2, and ND4L, were identified based on significant pathways. The results of this study are important for the elucidation of the molecular regulatory mechanism of intramuscular fat deposition and the effective improvement of IMF content in pigs.https://www.frontiersin.org/articles/10.3389/fgene.2024.1503148/fullmachine learningpigtranscriptomeintramuscular fatkey genes |
| spellingShingle | Yumei Shi Yumei Shi Xini Wang Shaokang Chen Yanhui Zhao Yan Wang Xihui Sheng Xiaolong Qi Lei Zhou Yu Feng Jianfeng Liu Chuduan Wang Kai Xing Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models Frontiers in Genetics machine learning pig transcriptome intramuscular fat key genes |
| title | Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models |
| title_full | Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models |
| title_fullStr | Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models |
| title_full_unstemmed | Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models |
| title_short | Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models |
| title_sort | identification of key genes affecting intramuscular fat deposition in pigs using machine learning models |
| topic | machine learning pig transcriptome intramuscular fat key genes |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2024.1503148/full |
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