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Journal articles on the topic 'Gene expression'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Ashwin, S. S., and Masaki Sasai. "2P132 Dynamics of transcriptional apparatus in eukaryotic gene expression(08. Molecular genetics & Gene expression,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S180. http://dx.doi.org/10.2142/biophys.53.s180_6.

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2

Muthukalathi, Selvamani, Ravanan Ramanujam, and Anbupalam Thalamuthu. "Consensus Clustering for Microarray Gene Expression Data." Bonfring International Journal of Data Mining 4, no. 4 (November 15, 2014): 26–33. http://dx.doi.org/10.9756/bijdm.6140.

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3

Nesvadbová, M., and A. Knoll. "Evaluation of reference genes for gene expression studies in pig muscle tissue by real-time PCR." Czech Journal of Animal Science 56, No. 5 (May 30, 2011): 213–16. http://dx.doi.org/10.17221/1428-cjas.

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The selection of reference genes is essential for gene expression studies when using a real-time quantitative polymerase chain reaction (PCR). Reference gene selection should be performed for each experiment because the gene expression level may be changed in different experimental conditions. In this study, the stability of mRNA expression was determined for seven genes: HPRT1, RPS18, NACA, TBP, TAF4B, RPL32 and OAZ1. The stability of these reference genes was investigated in the skeletal muscle tissue of pig foetuses, piglets and adult pigs using real-time quantitative PCR and SYBR green chemistry. The expression of stability of the used reference genes was calculated using the geNorm application. Different gene expression profiles among the age categories of pigs were found out. RPS18 has been identified as the gene with the most stable expression in the muscle tissue of all pig age categories. HPRT1 and RPL32 were found to have the highest stability in piglets and adult pigs, and in foetuses and adults pigs, respectively. The newly used reference gene, TAF4B, reached the highest expression stability in piglets.

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4

Prima, V. I. "Proposals for the ISS: «Expression» Experiment Gene expression in plants in microgravity." Kosmìčna nauka ì tehnologìâ 6, no. 4 (July 30, 2000): 100. http://dx.doi.org/10.15407/knit2000.04.101.

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5

Liu, Junjie, Peng Li, Liuyang Lu, Lanfen Xie, Xiling Chen, and Baizhong Zhang. "Selection and evaluation of potential reference genes for gene expression analysis in Avena fatua Linn." Plant Protection Science 55, No. 1 (November 20, 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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6

Li, M., X. Wu, X. Guo, P. Bao, X. Ding, M. Chu, C. Liang, and P. Yan. "Identification of optimal reference genes for examination of gene expression in different tissues of fetal yaks." Czech Journal of Animal Science 62, No. 10 (September 11, 2017): 426–34. http://dx.doi.org/10.17221/75/2016-cjas.

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Reverse transcription quantitative real-time PCR (RT-qPCR) is widely used to study the relative abundance of mRNA transcripts because of its sensitivity and reliable quantification. However, the reliability of the interpretation of expression data is influenced by several complex factors, including RNA quality, transcription activity, and PCR efficiency, among others. To avoid experimental errors arising from potential variation, the selection of appropriate reference genes to normalize gene expression is essential. In this study, 10 commonly used reference genes – ACTB, B2M, HPRT1, GAPDH, 18SrRNA, 28SrRNA, PPIA, UBE2D2, SDHA, and TBP – were selected as candidate reference genes for six fetal tissues (heart, liver, spleen, lung, kidney, and forehead skin) of yak (Bos grunniens). The transcription stability of the candidate reference genes was evaluated using geNorm, NormFinder, and BestKeeper. The results showed that the combination of TBP and ACTB provided high-quality data for further study. In contrast, the commonly used reference genes 28SrRNA, SDHA, GAPDH, and B2M should not be used for endogenous controls because of their unstable expression in this study. The reference genes that could be used in future gene expression studies in yaks were indentified.

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7

R, Dr Prema. "Feature Selection for Gene Expression Data Analysis – A Review." International Journal of Psychosocial Rehabilitation 24, no. 5 (May 25, 2020): 6955–64. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020695.

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8

Antonín, Stratil, Horák Pavel, Nesvadbová Michaela, Poucke Mario Van, Dvořáková Věra, Stupka Roman, Čítek Jaroslav, Zadinová Kateřina, Peelman Luc J, and Knoll Aleš. "Genomic structure and expression of the porcine ACTC1 gene." Czech Journal of Animal Science 63, No. 9 (August 31, 2018): 371–78. http://dx.doi.org/10.17221/34/2018-cjas.

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A partial cDNA (~1200 bp) of the porcine ACTC1 gene was identified in the subtracted foetal hind limb muscle cDNA library (44 days of gestation; using m. biceps femoris cDNA as the driver). Using specific polymerase chain reaction (PCR) primers, a bacterial artificial chromosome (BAC) clone containing the genomic ACTC1 gene was identified and the gene was sequenced. Specific PCR primers designed from the BAC and cDNA sequences were used for amplification and comparative sequencing of ACTC1 of Pietrain and Meishan pigs. The gene is approximately 5.4 kb in length, is composed of 7 exons, and has a coding sequence containing 1134 bp. The gene was mapped using the INRA-Minnesota porcine radiation hybrid (IMpRH) panel to chromosome 1, with SW65 as the closest marker (41 cR; LOD = 7.73). Differences were observed in tissue-specific expression of ACTC1 that was studied by transcription profiling in 28 porcine tissues. Developmental differences in muscle and heart were analysed by real-time quantitative PCR (RT-qPCR). Two single nucleotide polymorphisms (SNPs) were found in intron 1. One adequately informative SNP (FM212567.1:g.901C>G) was genotyped by PCR-restriction fragment length polymorphism, and allele frequencies in eight pig breeds were calculated.

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9

M, Aminafshar, Bahrampour V, Bagizadeh A, Emam Jomeh Kashan N, and Mohamad Abadi M.R. "Expression of CD44 Gene in Goat’s Oocytes and Embryos." Greener Journal of Biological Sciences 4, no. 5 (June 16, 2014): 139–45. http://dx.doi.org/10.15580/gjbs.2014.5.050614223.

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10

Mora-Avilés, M. A. "EXPRESIÓN DE GENES RELACIONADOS CON LA PATOGENICIDAD EN PLANTAS DE BRÓCOLI EXPRESANDO EL GEN ENDOQUITINASA DE Trichoderma harzianum." Revista Chapingo Serie Horticultura X, no. 2 (December 2004): 141–46. http://dx.doi.org/10.5154/r.rchsh.2003.04.028.

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11

Kemp, Martin. "Gene expression." Nature 446, no. 7135 (March 2007): 496. http://dx.doi.org/10.1038/446496a.

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12

Lamond, Angus I. "Gene expression." Trends in Biochemical Sciences 18, no. 4 (April 1993): 149. http://dx.doi.org/10.1016/0968-0004(93)90027-k.

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13

Galicia, J. C., B. R. Henson, J. S. Parker, and A. A. Khan. "Gene expression profile of pulpitis." Genes & Immunity 17, no. 4 (April 7, 2016): 239–43. http://dx.doi.org/10.1038/gene.2016.14.

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14

Ruan, Xiaogang, Yingxin Li, Jiangeng Li, Daoxiong Gong, and Jinlian Wang. "Tumor-specific gene expression patterns with gene expression profiles." Science in China Series C 49, no. 3 (June 2006): 293–304. http://dx.doi.org/10.1007/s11427-006-0293-1.

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15

P., Avila Clemenshia. "A Research on Cancer Subtype Classification Using Gene Expression Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 490–500. http://dx.doi.org/10.5373/jardcs/v12sp4/20201514.

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16

Venkataravanappa, J. T., K. C. Prasad, and S. Balakrishna. "LR4 gene expression in patients with chronic suppurative otitis media." Ukrainian Biochemical Journal 93, no. 6 (December 20, 2021): 87–92. http://dx.doi.org/10.15407/ubj93.06.087.

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17

Anitha, S., and Dr C. P. Chandran. "Review on Analysis of Gene Expression Data Using Biclustering Approaches." Bonfring International Journal of Data Mining 6, no. 2 (April 30, 2016): 16–23. http://dx.doi.org/10.9756/bijdm.8135.

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18

K, Sathishkumar, Balamurugan Dr., Dr Akpojaro Jackson, and Ramalingam M. "Efficient Clustering Methods and Statistical Approaches for Gene Expression Data." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (February 20, 2019): 440–47. http://dx.doi.org/10.5373/jardcs/v11sp11/20193052.

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19

S, Kavya. "A Review on: Gene Expression Analysis Techniques and its Application." International Journal of Research Publication and Reviews 5, no. 4 (April 28, 2024): 9928–33. http://dx.doi.org/10.55248/gengpi.5.0424.1145.

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20

Purutçuoǧlu, Vilda. "Robust Gene Expression Index." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/182758.

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The frequentist gene expression index (FGX) was recently developed to measure expression on Affymetrix oligonucleotide DNA arrays. In this study, we extend FGX to cover nonnormal log expressions, specifically long-tailed symmetric densities and call our new index as robust gene expression index (RGX). In estimation, we implement the modified maximum likelihood method to unravel the elusive solutions of likelihood equations and utilize the Fisher information matrix for covariance terms. From the analysis via the bench-mark datasets and simulated data, it is shown that RGX has promising results and mostly outperforms FGX in terms of relative efficiency of the estimated signals, in particular, when the data are nonnormal.

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21

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, no. 03 (August 28, 2019): 194–205. http://dx.doi.org/10.29199/2639-4618/arms.202037.

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22

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, no. 03 (August 28, 2019): 194–205. http://dx.doi.org/10.29199/2639-4618/arms.203037.

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23

Zahn, Laura M. "Localizing gene expression." Science 373, no. 6550 (July 1, 2021): 70.2–70. http://dx.doi.org/10.1126/science.373.6550.70-b.

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24

YASUE, Hiroshi, Koji DOI, and Hideki HIRAIWA. "Gene Expression Analysis." Journal of Animal Genetics 48, no. 1 (2019): 9–18. http://dx.doi.org/10.5924/abgri.48.9.

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25

Frederickson, Robert. "Profiling gene expression." Nature Biotechnology 17, no. 8 (August 1999): 739. http://dx.doi.org/10.1038/11655.

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26

Stevens, Katherine. "Insulating gene expression." Nature Methods 5, no. 4 (April 2008): 284–85. http://dx.doi.org/10.1038/nmeth0408-284b.

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27

BROWN, VANESSA M., ALEX OSSADTCHI, ARSHAD H. KHAN, SANJIV S. GAMBHIR, SIMON R. CHERRY, RICHARD M. LEAHY, and DESMOND J. SMITH. "Gene expression tomography." Physiological Genomics 8, no. 2 (February 28, 2002): 159–67. http://dx.doi.org/10.1152/physiolgenomics.00090.2001.

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Gene expression tomography, or GET, is a new method to increase the speed of three-dimensional (3-D) gene expression analysis in the brain. The name is evocative of the method’s dual foundations in high-throughput gene expression analysis and computerized tomographic image reconstruction, familiar from techniques such as positron emission tomography (PET) and X-ray computerized tomography (CT). In GET, brain slices are taken using a cryostat in conjunction with axial rotation about independent axes to create a series of “views” of the brain. Gene expression information obtained from the axially rotated views can then be used to recreate 3-D gene expression patterns. GET was used to successfully reconstruct images of tyrosine hydroxylase gene expression in the mouse brain, using both RNase protection and real-time quantitative reverse transcription PCR (QRT-PCR). A Monte-Carlo analysis confirmed the good quality of the GET image reconstruction. By speeding acquisition of gene expression patterns, GET may help improve our understanding of the genomics of the brain in both health and disease.

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28

Whitchurch, A. K. "Gene expression microarrays." IEEE Potentials 21, no. 1 (2002): 30–34. http://dx.doi.org/10.1109/45.985325.

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29

Weitzman, Jonathan B. "Shaping gene expression." Genome Biology 3 (2002): spotlight—20020220–01. http://dx.doi.org/10.1186/gb-spotlight-20020220-01.

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30

Tsuchyia, Masa, Sum T. Wong, Zhen X. Yeo, Alfredo Colosimo, Maria C. Palumbo, Lorenzo Farina, Marco Crescenzi, et al. "Gene expression waves." FEBS Journal 274, no. 11 (April 26, 2007): 2878–86. http://dx.doi.org/10.1111/j.1742-4658.2007.05822.x.

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31

Adler, E. M. "Activating Gene Expression." Science's STKE 2006, no. 362 (November 14, 2006): tw392. http://dx.doi.org/10.1126/stke.3622006tw392.

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32

Orlowski, Michael. "Gene expression inMucordimorphism." Canadian Journal of Botany 73, S1 (December 31, 1995): 326–34. http://dx.doi.org/10.1139/b95-263.

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An ongoing dialectic has concerned the relative importance of differential gene expression versus the pattern of new wall deposition in Mucor dimorphism. Numerous physiological processes and enzyme activities have been observed in flux during morphogenesis, but a causal link to dimorphism has been infrequently demonstrated. Very few of the proteins that are conspicuous in two-dimensional polyacrylamide gel electrophoresis are specific to cell morphology or significantly change in amount during morphogenesis. Cyclic AMP, putrescine, S-adenosylmethionine, and enzymes governing their intracellular concentrations show patterns of change that consistently correlate with morphogenesis. The expression of RAS proteins and translation elongation factor-1α activity during morphogenesis are regulated at the level of transcription and post-translational methylation, respectively. Wall chemistry is very similar in both morphologies, but wall deposition is isodiametric in yeasts and vectorial in hyphae. Electron microscopy shows patterns of apparent exocytosis that are generalized in the former and apical in the latter. Research on other dimorphic fungi, including Saccharomyces cerevisiae, suggests an involvement of cytoskeletal proteins and a family of GTP-linked protein kinases in directing polar growth. Some of these elements, which may be controlled quite distal from the genes encoding them, have been demonstrated in Mucor spp., while others are the subject of ongoing investigations. Key words: Mucor, dimorphism, morphogenesis, gene expression, yeasts, hyphae.

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33

Nitzan, Mor, Nikos Karaiskos, Nir Friedman, and Nikolaus Rajewsky. "Gene expression cartography." Nature 576, no. 7785 (November 20, 2019): 132–37. http://dx.doi.org/10.1038/s41586-019-1773-3.

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34

Kuusisto, Finn. "Gene expression profiling." XRDS: Crossroads, The ACM Magazine for Students 21, no. 4 (July 27, 2015): 71. http://dx.doi.org/10.1145/2788538.

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35

Robinson, Richard. "Quantitative gene expression." Genome Biology 4 (2003): spotlight—20030320–01. http://dx.doi.org/10.1186/gb-spotlight-20030320-01.

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36

Miller, W. Allen, S. P. Dinesh-Kumar, and Cynthia P. Paul. "Luteovirus Gene Expression." Critical Reviews in Plant Sciences 14, no. 3 (January 1995): 179–211. http://dx.doi.org/10.1080/07352689509701926.

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37

Graydon, Oliver. "Gene expression control." Nature Photonics 7, no. 11 (October 30, 2013): 848. http://dx.doi.org/10.1038/nphoton.2013.294.

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38

Dundr, M., and T. Misteli. "Gene expression dynamics." Biomedicine & Pharmacotherapy 57, no. 3-4 (May 2003): 180. http://dx.doi.org/10.1016/s0753-3322(02)00347-5.

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39

Miller, W. A., S. P. Dinesh-Kumar, and C. P. Paul. "Luteovirus Gene Expression." Critical Reviews in Plant Sciences 14, no. 3 (1995): 179. http://dx.doi.org/10.1080/713608119.

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40

Jasny, B. R. "Solving Gene Expression." Science 306, no. 5696 (October 22, 2004): 629. http://dx.doi.org/10.1126/science.306.5696.629.

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41

Purnell, B. A. "Specifying Gene Expression." Science Signaling 1, no. 6 (February 12, 2008): ec59-ec59. http://dx.doi.org/10.1126/stke.16ec59.

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42

Arnosti, David. "Modeling gene expression." Methods 62, no. 1 (July 2013): 1–2. http://dx.doi.org/10.1016/j.ymeth.2013.08.001.

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43

Raetz, Elizabeth A., Philip J. Moos, Aniko Szabo, and William L. Carroll. "GENE EXPRESSION PROFILING." Hematology/Oncology Clinics of North America 15, no. 5 (October 2001): 911–30. http://dx.doi.org/10.1016/s0889-8588(05)70257-4.

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44

Helser, Terry L. "Gene Expression Wordsearch." Journal of Chemical Education 87, no. 4 (April 2010): 408. http://dx.doi.org/10.1021/ed800130v.

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45

Sotiriou, C., and P. Dinh. "Gene expression profiling." European Journal of Cancer Supplements 6, no. 7 (April 2008): 105. http://dx.doi.org/10.1016/s1359-6349(08)70508-1.

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46

Oliver, Stephen. "Maximizing gene expression." Trends in Genetics 4, no. 2 (February 1988): 57. http://dx.doi.org/10.1016/0168-9525(88)90070-4.

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47

Buratowski, Stephen. "Visualizing Gene Expression." Nature Structural & Molecular Biology 10, no. 6 (June 2003): 413. http://dx.doi.org/10.1038/nsb0603-413.

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48

Harmar, A. J. "Neuropeptide gene expression." Trends in Pharmacological Sciences 16, no. 6 (June 1995): 214. http://dx.doi.org/10.1016/s0165-6147(00)89025-2.

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49

Teichmann, Sarah A. "Gene Expression Genomics." Biophysical Journal 104, no. 2 (January 2013): 534a. http://dx.doi.org/10.1016/j.bpj.2012.11.2954.

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50

Kher, Rajesh, and Robert L. Bacallao. "Imaging Gene Expression." Nephron Experimental Nephrology 103, no. 2 (2006): e75-e80. http://dx.doi.org/10.1159/000090620.

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