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Thematische Bibliographien / Gene expression / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Gene expression“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 29. Juli 2024

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1

Ashwin, S. S., und 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 und Anbupalam Thalamuthu. „Consensus Clustering for Microarray Gene Expression Data“. Bonfring International Journal of Data Mining 4, Nr. 4 (15.11.2014): 26–33. http://dx.doi.org/10.9756/bijdm.6140.

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3

Nesvadbová, M., und 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 (30.05.2011): 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, Nr. 4 (30.07.2000): 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 und Baizhong Zhang. „Selection and evaluation of potential reference genes for gene expression analysis in Avena fatua Linn“. Plant Protection Science 55, No. 1 (20.11.2018): 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 und 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 (11.09.2017): 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, Nr. 5 (25.05.2020): 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 und Knoll Aleš. „Genomic structure and expression of the porcine ACTC1 gene“. Czech Journal of Animal Science 63, No. 9 (31.08.2018): 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 und Mohamad Abadi M.R. „Expression of CD44 Gene in Goat’s Oocytes and Embryos“. Greener Journal of Biological Sciences 4, Nr. 5 (16.06.2014): 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, Nr. 2 (Dezember 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, Nr. 7135 (März 2007): 496. http://dx.doi.org/10.1038/446496a.

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12

Lamond, Angus I. „Gene expression“. Trends in Biochemical Sciences 18, Nr. 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 und A. A. Khan. „Gene expression profile of pulpitis“. Genes & Immunity 17, Nr. 4 (07.04.2016): 239–43. http://dx.doi.org/10.1038/gene.2016.14.

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14

Ruan, Xiaogang, Yingxin Li, Jiangeng Li, Daoxiong Gong und Jinlian Wang. „Tumor-specific gene expression patterns with gene expression profiles“. Science in China Series C 49, Nr. 3 (Juni 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 (31.03.2020): 490–500. http://dx.doi.org/10.5373/jardcs/v12sp4/20201514.

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16

Venkataravanappa, J. T., K. C. Prasad und S. Balakrishna. „LR4 gene expression in patients with chronic suppurative otitis media“. Ukrainian Biochemical Journal 93, Nr. 6 (20.12.2021): 87–92. http://dx.doi.org/10.15407/ubj93.06.087.

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17

Anitha, S., und Dr C. P. Chandran. „Review on Analysis of Gene Expression Data Using Biclustering Approaches“. Bonfring International Journal of Data Mining 6, Nr. 2 (30.04.2016): 16–23. http://dx.doi.org/10.9756/bijdm.8135.

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18

K, Sathishkumar, Balamurugan Dr., Dr Akpojaro Jackson und Ramalingam M. „Efficient Clustering Methods and Statistical Approaches for Gene Expression Data“. Journal of Advanced Research in Dynamical and Control Systems 11, Nr. 11-SPECIAL ISSUE (20.02.2019): 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, Nr. 4 (28.04.2024): 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, Nr. 03 (28.08.2019): 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, Nr. 03 (28.08.2019): 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, Nr. 6550 (01.07.2021): 70.2–70. http://dx.doi.org/10.1126/science.373.6550.70-b.

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24

YASUE, Hiroshi, Koji DOI und Hideki HIRAIWA. „Gene Expression Analysis“. Journal of Animal Genetics 48, Nr. 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, Nr. 8 (August 1999): 739. http://dx.doi.org/10.1038/11655.

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26

Stevens, Katherine. „Insulating gene expression“. Nature Methods 5, Nr. 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 und DESMOND J. SMITH. „Gene expression tomography“. Physiological Genomics 8, Nr. 2 (28.02.2002): 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, Nr. 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, Nr. 11 (26.04.2007): 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, Nr. 362 (14.11.2006): 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 (31.12.1995): 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 und Nikolaus Rajewsky. „Gene expression cartography“. Nature 576, Nr. 7785 (20.11.2019): 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, Nr. 4 (27.07.2015): 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 und Cynthia P. Paul. „Luteovirus Gene Expression“. Critical Reviews in Plant Sciences 14, Nr. 3 (Januar 1995): 179–211. http://dx.doi.org/10.1080/07352689509701926.

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37

Graydon, Oliver. „Gene expression control“. Nature Photonics 7, Nr. 11 (30.10.2013): 848. http://dx.doi.org/10.1038/nphoton.2013.294.

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38

Dundr, M., und T. Misteli. „Gene expression dynamics“. Biomedicine & Pharmacotherapy 57, Nr. 3-4 (Mai 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 und C. P. Paul. „Luteovirus Gene Expression“. Critical Reviews in Plant Sciences 14, Nr. 3 (1995): 179. http://dx.doi.org/10.1080/713608119.

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40

Jasny, B. R. „Solving Gene Expression“. Science 306, Nr. 5696 (22.10.2004): 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, Nr. 6 (12.02.2008): ec59-ec59. http://dx.doi.org/10.1126/stke.16ec59.

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42

Arnosti, David. „Modeling gene expression“. Methods 62, Nr. 1 (Juli 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 und William L. Carroll. „GENE EXPRESSION PROFILING“. Hematology/Oncology Clinics of North America 15, Nr. 5 (Oktober 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, Nr. 4 (April 2010): 408. http://dx.doi.org/10.1021/ed800130v.

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45

Sotiriou, C., und P. Dinh. „Gene expression profiling“. European Journal of Cancer Supplements 6, Nr. 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, Nr. 2 (Februar 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, Nr. 6 (Juni 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, Nr. 6 (Juni 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, Nr. 2 (Januar 2013): 534a. http://dx.doi.org/10.1016/j.bpj.2012.11.2954.

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50

Kher, Rajesh, und Robert L. Bacallao. „Imaging Gene Expression“. Nephron Experimental Nephrology 103, Nr. 2 (2006): e75-e80. http://dx.doi.org/10.1159/000090620.

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