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Artículos de revistas sobre el tema "Gene expression analysis"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 11 de marzo de 2023

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1

R, Dr Prema. "Feature Selection for Gene Expression Data Analysis – A Review". International Journal of Psychosocial Rehabilitation 24, n.º 5 (25 de mayo de 2020): 6955–64. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020695.

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2

Liu, Junjie, Peng Li, Liuyang Lu, Lanfen Xie, Xiling Chen y Baizhong Zhang. "Selection and evaluation of potential reference genes for gene expression analysis in Avena fatua Linn". Plant Protection Science 55, No. 1 (20 de noviembre de 2018): 61–71. http://dx.doi.org/10.17221/20/2018-pps.

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Eight commonly used candidate reference genes, 18S ribosomal RNA (rRNA) (18S), 28S rRNA (28S), actin (ACT), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), elongation factor 1 alpha (EF1α), ribosomal protein L7 (RPL7), Alpha-tubulin (α-TUB), and TATA box binding protein-associated factor (TBP), were evaluated under various experimental conditions to assess their suitability in different developmental stages, tissues and herbicide treatments in Avena fatua. The results indicated the most suitable reference genes for the different experimental conditions. For developmental stages, 28S and EF1α were the optimal reference genes, both EF1α and 28S were suitable for experiments of different tissues, whereas for herbicide treatments, GAPDH and ACT were suitable for normalizations of expression data. In addition, GAPDH and EF1α were the suitable reference genes.

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3

Anitha, S. y Dr C. P. Chandran. "Review on Analysis of Gene Expression Data Using Biclustering Approaches". Bonfring International Journal of Data Mining 6, n.º 2 (30 de abril de 2016): 16–23. http://dx.doi.org/10.9756/bijdm.8135.

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4

YASUE, Hiroshi, Koji DOI y Hideki HIRAIWA. "Gene Expression Analysis". Journal of Animal Genetics 48, n.º 1 (2019): 9–18. http://dx.doi.org/10.5924/abgri.48.9.

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5

Oetting, William S. "Gene Expression Analysis". Pigment Cell Research 13, n.º 1 (febrero de 2000): 21–27. http://dx.doi.org/10.1034/j.1600-0749.2000.130105.x.

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6

Carvalho, Felicia I., Christopher Johns y Marc E. Gillespie. "Gene expression analysis". Biochemistry and Molecular Biology Education 40, n.º 3 (15 de febrero de 2012): 181–90. http://dx.doi.org/10.1002/bmb.20588.

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7

Mikami, Koji. "Requirement for Different Normalization Genes for Quantitative Gene Expression Analysis Under Abiotic Stress Conditions in ‘Bangia’ sp. ESS1". Journal of Aquatic Research and Marine Sciences 02, n.º 03 (28 de agosto de 2019): 194–205. http://dx.doi.org/10.29199/2639-4618/arms.202037.

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8

Mikami, Koji. "Requirement for Different Normalization Genes for Quantitative Gene Expression Analysis Under Abiotic Stress Conditions in ‘Bangia’ sp. ESS1". Journal of Aquatic Research and Marine Sciences 02, n.º 03 (28 de agosto de 2019): 194–205. http://dx.doi.org/10.29199/2639-4618/arms.203037.

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9

Winter, Holger, Kerstin Korn y Rudolf Rigler. "Direct Gene Expression Analysis". Current Pharmaceutical Biotechnology 5, n.º 2 (1 de abril de 2004): 191–97. http://dx.doi.org/10.2174/1389201043376995.

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10

Stein,, Richard A. "Gene-Expression Analysis Redefined". Genetic Engineering & Biotechnology News 31, n.º 7 (abril de 2011): 1–31. http://dx.doi.org/10.1089/gen.31.7.13.

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11

Kozian, D. "Comparative gene-expression analysis". Trends in Biotechnology 17, n.º 2 (1 de febrero de 1999): 73–78. http://dx.doi.org/10.1016/s0167-7799(98)01292-x.

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12

Brazma, Alvis y Jaak Vilo. "Gene expression data analysis". FEBS Letters 480, n.º 1 (24 de agosto de 2000): 17–24. http://dx.doi.org/10.1016/s0014-5793(00)01772-5.

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13

Kriete, Andres. "Gene expression analysis enriched". Drug Discovery Today 9, n.º 21 (noviembre de 2004): 913–14. http://dx.doi.org/10.1016/s1359-6446(04)03255-6.

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14

Zhao, Weiguo, Rongfang Li, Dandan Chen, Dominic Kwame Kotoka, Renjie Sun, Yuanliang Deng, Feng Li, Jiao Qian, Rongjun fang y Long Li. "Cloning and expression pattern analysis of MmPOD12 gene in mulberry under abiotic stresses". Journal of Experimental Biology and Agricultural Sciences 4, VIS (2 de enero de 2017): 698–705. http://dx.doi.org/10.18006/2016.4(vis).698.705.

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15

Shi, T., Y. Xu, M. J. Yang, Y. Zhou, M. Liu, X. Y. Lan, C. Z. Lei et al. "Genetic variation, association analysis, and expression pattern of SMAD3 gene in Chinese cattle". Czech Journal of Animal Science 61, No. 5 (15 de julio de 2016): 209–16. http://dx.doi.org/10.17221/34/2015-cjas.

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16

Tejashwini. N, Tejashwini N., Tanushree Chaudhuri y Kusum Paul. "Regulation of Nuclear Gene Expression Data Analysis in Diabetic Nephropathy and Data Mining". International Journal of Scientific Research 2, n.º 8 (1 de junio de 2012): 48–50. http://dx.doi.org/10.15373/22778179/aug2013/17.

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17

Halpert,, Richard L. "Improving Gene-Expression Data Analysis". Genetic Engineering & Biotechnology News 32, n.º 5 (marzo de 2012): 38–39. http://dx.doi.org/10.1089/gen.32.5.16.

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18

Daniels, David. "Gene Expression Analysis Making Inroads". Genetic Engineering & Biotechnology News 33, n.º 6 (15 de marzo de 2013): 20, 22–23. http://dx.doi.org/10.1089/gen.33.6.10.

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19

Burian, Dennis. "Exon-Level Gene Expression Analysis". Aviation, Space, and Environmental Medicine 80, n.º 6 (1 de junio de 2009): 577–78. http://dx.doi.org/10.3357/asem.21004.2009.

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20

Velculescu, Victor E. y Kenneth W. Kinzler. "Gene expression analysis goes digital". Nature Biotechnology 25, n.º 8 (agosto de 2007): 878–80. http://dx.doi.org/10.1038/nbt0807-878.

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21

Curtis, R. Keira y Martin D. Brand. "Control analysis of gene expression". Biochemical Society Transactions 30, n.º 1 (1 de febrero de 2002): A8. http://dx.doi.org/10.1042/bst030a008.

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22

Curtis, R. Keira y Martin D. Brand. "Control analysis of gene expression". Biochemical Society Transactions 30, n.º 1 (1 de febrero de 2002): A32. http://dx.doi.org/10.1042/bst030a032.

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23

Yoshida, Tetsuo, Takehisa Suzuki, Hironori Sato, Hiroshi Nishina y Hideo Iba. "Analysis offra-2 gene expression". Nucleic Acids Research 21, n.º 11 (1993): 2715–21. http://dx.doi.org/10.1093/nar/21.11.2715.

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24

Velculescu, V. E., L. Zhang, B. Vogelstein y K. W. Kinzler. "Serial Analysis of Gene Expression". Science 270, n.º 5235 (20 de octubre de 1995): 484–87. http://dx.doi.org/10.1126/science.270.5235.484.

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25

Dunn, C. W., X. Luo y Z. Wu. "Phylogenetic Analysis of Gene Expression". Integrative and Comparative Biology 53, n.º 5 (7 de junio de 2013): 847–56. http://dx.doi.org/10.1093/icb/ict068.

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26

Patino, Willmar D., Omar Y. Mian y Paul M. Hwang. "Serial Analysis of Gene Expression". Circulation Research 91, n.º 7 (4 de octubre de 2002): 565–69. http://dx.doi.org/10.1161/01.res.0000036018.76903.18.

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27

Carson, Monica J., J. Cameron Thrash y David Lo. "Analysis of Microglial Gene Expression". American Journal of PharmacoGenomics 4, n.º 5 (2004): 321–30. http://dx.doi.org/10.2165/00129785-200404050-00005.

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28

Hu, Min y Kornelia Polyak. "Serial analysis of gene expression". Nature Protocols 1, n.º 4 (noviembre de 2006): 1743–60. http://dx.doi.org/10.1038/nprot.2006.269.

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29

Lovén, Jakob, David A. Orlando, Alla A. Sigova, Charles Y. Lin, Peter B. Rahl, Christopher B. Burge, David L. Levens, Tong Ihn Lee y Richard A. Young. "Revisiting Global Gene Expression Analysis". Cell 151, n.º 3 (octubre de 2012): 476–82. http://dx.doi.org/10.1016/j.cell.2012.10.012.

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30

Ma, Fang, Yali Zou, Ruilin Ma, Xin Chen y Lanfang Ma. "Evolution, characterization and expression analysis of Sox gene family in rainbow trout (Oncorhynchus mykiss)". Czech Journal of Animal Science 67, No. 4 (30 de abril de 2022): 157–66. http://dx.doi.org/10.17221/4/2022-cjas.

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The Sox transcription factor family plays an important role in various biological processes such as animal sex determination and multiple organ development. We used online databases to analyze the gene structure, chemical characteristics, and evolutionary relationship of Sox family genes through bioinformatics, and we studied the expression profiles and regulatory mechanisms of Sox family genes. A total of 29 rainbow trout Sox genes were identified. The phylogenetic analysis found that Sox genes of rainbow trout were clustered in seven subfamilies (B1, B2, C, D, E, F and H), and the gene structure of each subfamily was relatively conserved. Furthermore, Sox1, Sox4, Sox6, Sox8, Sox9, Sox11, Sox17, Sox18, and Sox19 developed into two copies, which might be the result of teleost fish-specific genome replication. Multiple HMG box domain alignments indicated that the motifs for all Sox sequences are conserved. Gene expression studies reveal that Sox expression is tissue-specific and that multiple Sox genes are involved in rainbow trout gonad and central nervous system development. Our study provides valuable information on the evolution of teleosts, and will also help to further research the functional characteristics of Sox genes.

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31

Anusha.B.N, Anusha B. N., Shambu M. G. Shambu.M.G y Kusum Paul. "Genome Wide Transcriptional Analysis of Gene Expression Signatures and Pathways on Neoplastic Pancreatic Cancer". International Journal of Scientific Research 2, n.º 8 (1 de junio de 2012): 43–44. http://dx.doi.org/10.15373/22778179/aug2013/15.

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32

Lykhenko, O. "СONSECUTIVE INTEGRATION OF AVAILABLE MICROARRAY DATA FOR ANALYSIS OF DIFFERENTIAL GENE EXPRESSION IN HUMAN PLACENTA". Biotechnologia Acta 14, n.º 1 (febrero de 2021): 38–45. http://dx.doi.org/10.15407/biotech14.01.38.

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The purpose of the study was to provide the pipeline for processing of publicly available unprocessed data on gene expression via integration and differential gene expression analysis. Data collection from open gene expression databases, normalization and integration into a single expression matrix in accordance with metadata and determination of differentially expressed genes were fulfilled. To demonstrate all stages of data processing and integrative analysis, there were used the data from gene expression in the human placenta from the first and second trimesters of normal pregnancy. The source code for the integrative analysis was written in the R programming language and publicly available as a repository on GitHub. Four clusters of functionally enriched differentially expressed genes were identified for the human placenta in the interval between the first and second trimester of pregnancy. Immune processes, developmental processes, vasculogenesis and angiogenesis, signaling and the processes associated with zinc ions varied in the considered interval between the first and second trimester of placental development. The proposed sequence of actions for integrative analysis could be applied to any data obtained by microarray technology.

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33

Bao, W. B., L. Ye, Z. Y. Pan, J. Zhu, G. Q. Zhu, X. G. Huang y S. L. Wu. "Analysis of polymorphism in the porcine TLR4 gene and its expression related to Escherichia coliF18 infection". Czech Journal of Animal Science 56, No. 11 (22 de noviembre de 2011): 475–82. http://dx.doi.org/10.17221/3836-cjas.

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The genetic variation in exon 1 of the TLR4 gene was detected among a total of 893 animals, including Asian wild boars, 3 imported commercial and 10 Chinese indigenous pig breeds. The expression of TLR4 was assayed by RT-PCR and different expression between resistant and sensitive resource populations to ETEC F18 was analysed to discuss the role that the TLR4 gene plays in resistance. In this study, new alleles were detected in exon 1 of the TLR4 gene. These polymorphisms are significantly different between Chinese indigenous breeds and imported breeds. Based on the published TLR4 gene sequence (AB232527) in GenBank, a 93G > C mutation was found in 5’UTR and only a 194G > A synonymous mutation was found in the coding sequence of exon 1. In addition, TLR4 gene was broadly expressed in 11 tissues with the highest level in lung. The expression was relatively high in the lymph nodes, kidney and spleen. Generally, the expression of TLR4 gene in sensitive individuals was higher than that in resistant individuals. The results indicated that the downregulation of the mRNA expression of TLR4 gene had reduced the transmembrane signal transduction of LPS and then led to the responsive ability of the host to ETEC F18 in piglets.

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34

Park, Young-Kyu, Jeffrey L. Franklin, Stephen H. Settle, Shawn E. Levy, Eunkyung Chung, Loice H. Jeyakumar, Yu Shyr et al. "Gene expression profile analysis of mouse colon embryonic development". genesis 41, n.º 1 (enero de 2005): 1–12. http://dx.doi.org/10.1002/gene.20088.

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35

Yan, Shankai y Ka-Chun Wong. "GESgnExt: Gene Expression Signature Extraction and Meta-Analysis on Gene Expression Omnibus". IEEE Journal of Biomedical and Health Informatics 24, n.º 1 (enero de 2020): 311–18. http://dx.doi.org/10.1109/jbhi.2019.2896144.

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36

Ye, Shui Qing, David C. Usher y Li Q. Zhang. "Gene Expression Profiling of Human Diseases by Serial Analysis of Gene Expression". Journal of Biomedical Science 9, n.º 5 (2002): 384–94. http://dx.doi.org/10.1159/000064547.

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37

Qing Ye, Shui, David C. Usher y Li Q. Zhang. "Gene expression profiling of human diseases by serial analysis of gene expression". Journal of Biomedical Science 9, n.º 5 (septiembre de 2002): 384–94. http://dx.doi.org/10.1007/bf02256531.

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38

Stein, Richard A. "Gene Expression Analysis Reshapes Biomedical Research". Genetic Engineering & Biotechnology News 32, n.º 17 (octubre de 2012): 34–39. http://dx.doi.org/10.1089/gen.32.17.15.

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39

Weldon, Don y Grace Johnston. "Gene Expression Analysis in Living Cells". Genetic Engineering & Biotechnology News 33, n.º 9 (mayo de 2013): 20–21. http://dx.doi.org/10.1089/gen.33.9.10.

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40

Tanabata, Takanari, Fumiaki Hirose, Hidenobu Hashikami y Hajime Nobuhara. "Interactive Data Mining Tool for Microarray Data Analysis Using Formal Concept Analysis". Journal of Advanced Computational Intelligence and Intelligent Informatics 16, n.º 2 (20 de marzo de 2012): 273–81. http://dx.doi.org/10.20965/jaciii.2012.p0273.

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The DNA microarray analysis can explain gene functions by measuring tens of thousands of gene expressions at once and analyzing gene expression profiles that are obtained from the measurement. However, gene expression profiles have such a vast amount of information and therefore most analyses work are done on the data narrowed down by statistical methods, there remains a possibility ofmissing out on genes that consist the factors of phenomena from their evaluations. This study propose a method based on a formal concept analysis to visualize all gene expression profiles and characteristic information that can be obtained from annotation information of each gene so that the user can overview them. In the formal concept analysis, a lattice structure that allows genes to be hierarchically classified and made viewable is built based on the inclusion relations of attributes from a context table in which gene is the object and the attributes are expression profiles and binarized characteristic information. With the proposed method, the user can change the overview state by adjusting the expression ratio and the binary state of characteristic information, understand the relational structure of gene expressions, and carry out analyses of gene functions. We develop software to practice the proposed method, and then ask a biologist to evaluate effectiveness of proposed method applied to a function analysis of genes related to blue light signaling of rice seedlings.

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41

Nishida, N., K. Kurata y A. Suyama. "Gene expression analysis by DNA computing". Seibutsu Butsuri 40, supplement (2000): S152. http://dx.doi.org/10.2142/biophys.40.s152_4.

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42

Pham, Tuan, Christine Wells y Denis Crane. "Analysis of Microarray Gene Expression Data". Current Bioinformatics 1, n.º 1 (1 de enero de 2006): 37–53. http://dx.doi.org/10.2174/157489306775330642.

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43

Chan, W. C. y J. Z. Huang. "Gene expression analysis in aggressive NHL". Annals of Hematology 80, S3 (noviembre de 2001): B38—B41. http://dx.doi.org/10.1007/pl00022786.

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44

Prasad, Tangirala Venkateswara, Ravindra Pentela Babu y Syed Ismail Ahson. "GEDAS – Gene Expression Data Analysis Suite". Bioinformation 1, n.º 1 (1 de enero de 2006): 83–85. http://dx.doi.org/10.6026/97320630001083.

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45

TABUCHI, Yoshiaki. "Part 17. Global gene expression analysis". Choonpa Igaku 46, n.º 2 (2019): 181–84. http://dx.doi.org/10.3179/jjmu.jjmu.t.17.

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46

SESE, Jun y Shinichi MORISHITA. "Gene Expression Analysis with Data Mining." Seibutsu Butsuri 41, n.º 3 (2001): 132–36. http://dx.doi.org/10.2142/biophys.41.132.

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47

Nishida, N., M. Wakui, K. Tokunaga y A. Suyama. "Gene expression analysis by DNA computing". Seibutsu Butsuri 41, supplement (2001): S88. http://dx.doi.org/10.2142/biophys.41.s88_2.

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48

Kodzius, Rimantas, Miki Kojima, Hiromi Nishiyori, Mari Nakamura, Shiro Fukuda, Michihira Tagami, Daisuke Sasaki et al. "CAGE: cap analysis of gene expression". Nature Methods 3, n.º 3 (marzo de 2006): 211–22. http://dx.doi.org/10.1038/nmeth0306-211.

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49

Schmid, H., C. D. Cohen, A. Henger, D. Schlondorff y M. Kretzler. "Gene expression analysis in renal biopsies". Nephrology Dialysis Transplantation 19, n.º 6 (19 de marzo de 2004): 1347–51. http://dx.doi.org/10.1093/ndt/gfh181.

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50

Roth, S. M., R. E. Ferrell, D. G. Peters, E. J. Metter, G. F. Martel, B. F. Hurley y M. A. Rogers. "MICROARRAY ANALYSIS OF MUSCLE GENE EXPRESSION". Medicine & Science in Sports & Exercise 34, n.º 5 (mayo de 2002): S189. http://dx.doi.org/10.1097/00005768-200205001-01059.

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