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Yang Y, Song J, Shen S, Wang Y, Pan C, Wu J, Lan X. InDel mutations within the bovine PER2 gene are significantly associated with reproductive traits. Anim Biotechnol 2024:2397806. [PMID: 39222161 DOI: 10.1080/10495398.2024.2397806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024]
Abstract
Functioning as a key regulator of circadian rhythms, the PER2 gene exerts a substantial impact on the reproductive traits of animals. However, the effect of the PER2 gene on ovarian development remains unclear. In order to examine the relationship between bovine reproductive trait and the PER2 gene, a total of 901 ovarian samples were collected, categorized into different oestrus cycles (proestrus, oestrus, post-oestrus, anoestrous), and subjected to analysis for two potential insertion/deletions (InDels) in the PER2 gene. Through agarose gel electrophoresis and DNA sequencing, two polymorphic deletion mutations (P2-D5-bp, P3-D13-bp) were identified. Furthermore, a significant association between mature follicle diameter and P2-D5-bp was found (P < 0.05). Additionally, several significant correlations with ovarian length, width, height, and white body diameter were found for P3-D13-bp (P < 0.05). These findings suggested that the bovine PER2 gene plays an important role in above-mentioned reproductive traits, offering new avenues for improving cow fertility through marker-assisted selection (MAS).
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Affiliation(s)
- Yuanzhe Yang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Jiajun Song
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Siyuan Shen
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Yongsheng Wang
- College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, China
| | - Chuanying Pan
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Jiyao Wu
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - Xianyong Lan
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
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Dvorak P, Hlavac V, Hanicinec V, Rao BH, Soucek P. Genes divided according to the relative position of the longest intron show increased representation in different KEGG pathways. BMC Genomics 2024; 25:649. [PMID: 38943073 PMCID: PMC11214234 DOI: 10.1186/s12864-024-10558-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 06/24/2024] [Indexed: 07/01/2024] Open
Abstract
Despite the fact that introns mean an energy and time burden for eukaryotic cells, they play an irreplaceable role in the diversification and regulation of protein production. As a common feature of eukaryotic genomes, it has been reported that in protein-coding genes, the longest intron is usually one of the first introns. The goal of our work was to find a possible difference in the biological function of genes that fulfill this common feature compared to genes that do not. Data on the lengths of all introns in genes were extracted from the genomes of six vertebrates (human, mouse, koala, chicken, zebrafish and fugu) and two other model organisms (nematode worm and arabidopsis). We showed that more than 40% of protein-coding genes have the relative position of the longest intron located in the second or third tertile of all introns. Genes divided according to the relative position of the longest intron were found to be significantly increased in different KEGG pathways. Genes with the longest intron in the first tertile predominate in a range of pathways for amino acid and lipid metabolism, various signaling, cell junctions or ABC transporters. Genes with the longest intron in the second or third tertile show increased representation in pathways associated with the formation and function of the spliceosome and ribosomes. In the two groups of genes defined in this way, we further demonstrated the difference in the length of the longest introns and the distribution of their absolute positions. We also pointed out other characteristics, namely the positive correlation between the length of the longest intron and the sum of the lengths of all other introns in the gene and the preservation of the exact same absolute and relative position of the longest intron between orthologous genes.
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Affiliation(s)
- Pavel Dvorak
- Department of Biology, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic.
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic.
- Institute of Medical Genetics, University Hospital Pilsen, Dr. Edvarda Benese 13, 30599, Pilsen, Czech Republic.
| | - Viktor Hlavac
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic
- Toxicogenomics Unit, National Institute of Public Health, Srobarova 48, 10042, Prague, Czech Republic
| | - Vojtech Hanicinec
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic
| | - Bhavana Hemantha Rao
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic
| | - Pavel Soucek
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic
- Toxicogenomics Unit, National Institute of Public Health, Srobarova 48, 10042, Prague, Czech Republic
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Dall'Alba G, Casa PL, Abreu FPD, Notari DL, de Avila E Silva S. A Survey of Biological Data in a Big Data Perspective. BIG DATA 2022; 10:279-297. [PMID: 35394342 DOI: 10.1089/big.2020.0383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.
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Affiliation(s)
- Gabriel Dall'Alba
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
- Genome Science and Technology Program, Faculty of Science, The University of British Columbia, Vancouver, Canada
| | - Pedro Lenz Casa
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Fernanda Pessi de Abreu
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Daniel Luis Notari
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Scheila de Avila E Silva
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
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