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Journal articles on the topic 'Genome-wide analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Wu, Weihuai, Kexian Yi, Xing Huang, Thomas Gbokie Jr, and Baohui Liu. "Genome-wide analysis of defensin-like genes in Coffea arabica." SDRP Journal of Plant Science 3, no. 1 (2019): 1–6. http://dx.doi.org/10.25177/jps.3.1.ra.499.

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2

Weerasekara, Vajira Samanthi. "Genome-wide haplotype analysis." Sri Lanka Journal of Bio-Medical Informatics 3, no. 1 (January 8, 2013): 20. http://dx.doi.org/10.4038/sljbmi.v3i1.2564.

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3

Zhang, Jianzhi. "Epistasis Analysis Goes Genome-Wide." PLOS Genetics 13, no. 2 (February 16, 2017): e1006558. http://dx.doi.org/10.1371/journal.pgen.1006558.

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4

Krapohl, E., J. Euesden, D. Zabaneh, J.-B. Pingault, K. Rimfeld, S. von Stumm, P. S. Dale, G. Breen, P. F. O'Reilly, and R. Plomin. "Phenome-wide analysis of genome-wide polygenic scores." Molecular Psychiatry 21, no. 9 (August 25, 2015): 1188–93. http://dx.doi.org/10.1038/mp.2015.126.

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5

Lee, Young Ho, and Gwan Gyu Song. "Genome-wide pathway analysis of a genome-wide association study on Alzheimer’s disease." Neurological Sciences 36, no. 1 (July 19, 2014): 53–59. http://dx.doi.org/10.1007/s10072-014-1885-3.

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6

Song, Gwan Gyu, Sung Jae Choi, Jong Dae Ji, and Young Ho Lee. "Genome-wide pathway analysis of a genome-wide association study on multiple sclerosis." Molecular Biology Reports 40, no. 3 (December 14, 2012): 2557–64. http://dx.doi.org/10.1007/s11033-012-2341-1.

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7

Zhu, Lei, Yanman Li, Jintao Li, Yong Wang, Zhenli Zhang, Yanjiao Wang, Zanlin Wang, Jianbin Hu, Luming Yang, and Shouru Sun. "Genome-wide identification and analysis of the MLO gene families in three Cucurbita species." Czech Journal of Genetics and Plant Breeding 57, No. 3 (July 14, 2021): 119–23. http://dx.doi.org/10.17221/99/2020-cjgpb.

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Powdery mildew (PM) is a major fungal disease in the Cucurbita species in the world, which can cause significant yield loss. The Mildew Locus O (MLO) family genes play important roles in the PM stress response. In this paper, twenty, twenty-one, and eighteen candidate MLO genes in Cucurbita moschata, Cucurbita maxima and Cucurbita pepo, respectively, were identified and designated as CmoMLO, CmaMLO and CpeMLO, respectively. The phylogenetic analysis indicated that these MLOs were divided into five clades and the number of MLOs belonging to clade V in the Cucurbita species was more than that in other crops. Furthermore, the expression analysis in the susceptibility (S) and resistance (R) lines showed that CpeMLO1, CpeMLO2 and CpeMLO5 might be involved in the susceptibility response. CpeMLO4 and CpeMLO6 showing opposite expression patterns in the R/S lines might be involved in the resistance response. All these data would be beneficial for future functional analysis of MLOs in the Cucurbita species.

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8

Anusha.B.N, Anusha B. N., Shambu M. G. Shambu.M.G, and Kusum Paul. "Genome Wide Transcriptional Analysis of Gene Expression Signatures and Pathways on Neoplastic Pancreatic Cancer." International Journal of Scientific Research 2, no. 8 (June 1, 2012): 43–44. http://dx.doi.org/10.15373/22778179/aug2013/15.

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9

Tang, Lin. "Genome-wide analysis of structural variation." Nature Methods 18, no. 5 (May 2021): 448. http://dx.doi.org/10.1038/s41592-021-01161-z.

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10

Sonoyama, M. "Genome-wide analysis of membrane proteins." Seibutsu Butsuri 41, supplement (2001): S9. http://dx.doi.org/10.2142/biophys.41.s9_1.

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11

Zabeau, Marc, Peter Breyne, and Paul Van Hummelen. "Genome-wide expression analysis in Arabidopsis." Biochemical Society Transactions 28, no. 5 (October 1, 2000): A112. http://dx.doi.org/10.1042/bst028a112c.

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12

Stein, Murray B., Michael J. McCarthy, Chia-Yen Chen, Sonia Jain, Joel Gelernter, Feng He, Steven G. Heeringa, et al. "Genome-wide analysis of insomnia disorder." Molecular Psychiatry 23, no. 11 (March 8, 2018): 2238–50. http://dx.doi.org/10.1038/s41380-018-0033-5.

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13

Bazak, L., E. Y. Levanon, and E. Eisenberg. "Genome-wide analysis of Alu editability." Nucleic Acids Research 42, no. 11 (May 14, 2014): 6876–84. http://dx.doi.org/10.1093/nar/gku414.

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14

Huang, Chao, Paul Thompson, Yalin Wang, Yang Yu, Jingwen Zhang, Dehan Kong, Rivka R. Colen, Rebecca C. Knickmeyer, and Hongtu Zhu. "FGWAS: Functional genome wide association analysis." NeuroImage 159 (October 2017): 107–21. http://dx.doi.org/10.1016/j.neuroimage.2017.07.030.

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15

Motaung, Thabiso E., Ruan Ells, Carolina H. Pohl, Jacobus Albertyn, and Toi J. Tsilo. "Genome-wide functional analysis inCandida albicans." Virulence 8, no. 8 (March 13, 2017): 1563–79. http://dx.doi.org/10.1080/21505594.2017.1292198.

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16

Lee, Young Ho, Jae-Hoon Kim, and Gwan Gyu Song. "Genome-wide pathway analysis in neuroblastoma." Tumor Biology 35, no. 4 (November 30, 2013): 3471–85. http://dx.doi.org/10.1007/s13277-013-1459-7.

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17

Luo, Li, Gang Peng, Yun Zhu, Hua Dong, Christopher I. Amos, and Momiao Xiong. "Genome-wide gene and pathway analysis." European Journal of Human Genetics 18, no. 9 (May 5, 2010): 1045–53. http://dx.doi.org/10.1038/ejhg.2010.62.

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18

LEE, Y. H., and G. G. SONG. "Genome-wide pathway analysis in glioma." Neoplasma 62, no. 02 (2015): 230–38. http://dx.doi.org/10.4149/neo_2015_028.

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19

Diede, Scott J., Hisashi Tanaka, Donald A. Bergstrom, Meng-Chao Yao, and Stephen J. Tapscott. "Genome-wide analysis of palindrome formation." Nature Genetics 42, no. 4 (April 2010): 279. http://dx.doi.org/10.1038/ng0410-279.

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20

Sanada, Masashi, and Seishi Ogawa. "Genome-wide Analysis of Myelodysplastic Syndromes." Current Pharmaceutical Design 18, no. 22 (May 1, 2012): 3163–69. http://dx.doi.org/10.2174/1381612811209023163.

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21

Liang, Shoudan. "Genome-Wide Analysis of Epigenetic Modifications." Journal of Computer Science and Technology 25, no. 1 (January 2010): 35–41. http://dx.doi.org/10.1007/s11390-010-9303-7.

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22

Ekwall, Karl. "Genome-wide analysis of HDAC function." Trends in Genetics 21, no. 11 (November 2005): 608–15. http://dx.doi.org/10.1016/j.tig.2005.08.009.

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23

Ge, Tian, Thomas E. Nichols, Phil H. Lee, Avram J. Holmes, Joshua L. Roffman, Randy L. Buckner, Mert R. Sabuncu, and Jordan W. Smoller. "Massively expedited genome-wide heritability analysis (MEGHA)." Proceedings of the National Academy of Sciences 112, no. 8 (February 9, 2015): 2479–84. http://dx.doi.org/10.1073/pnas.1415603112.

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The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale heritability-based screening and high-dimensional heritability profile construction.

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24

Umate, Pavan, Narendra Tuteja, and Renu Tuteja. "Genome-wide comprehensive analysis of human helicases." Communicative & Integrative Biology 4, no. 1 (January 2011): 118–37. http://dx.doi.org/10.4161/cib.13844.

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25

Zeggini, Eleftheria, and John PA Ioannidis. "Meta-analysis in genome-wide association studies." Pharmacogenomics 10, no. 2 (February 2009): 191–201. http://dx.doi.org/10.2217/14622416.10.2.191.

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26

Bucknor, Brianna, and Jaime Derringer. "TU17. GENOME-WIDE ANALYSIS OF PSYCHOLOGICAL RESILIENCE." European Neuropsychopharmacology 51 (October 2021): e103. http://dx.doi.org/10.1016/j.euroneuro.2021.08.020.

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27

Gupta*, Romi, Arvindhan Nagarajan*, and Narendra Wajapeyee. "Advances in genome-wide DNA methylation analysis." BioTechniques 49, no. 4 (October 2010): iii—xi. http://dx.doi.org/10.2144/000113493.

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28

Bushman, Frederic, Mary Lewinski, Angela Ciuffi, Stephen Barr, Jeremy Leipzig, Sridhar Hannenhalli, and Christian Hoffmann. "Genome-wide analysis of retroviral DNA integration." Nature Reviews Microbiology 3, no. 11 (September 19, 2005): 848–58. http://dx.doi.org/10.1038/nrmicro1263.

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29

Liu, Qing, Ryan Weiss, Philipp Spahn, Austin Chiang, Jing Li, Kristina Hamill, Yitzhak Tor, Nathan Lewis, and Jeffrey Esko. "Genome‐wide Analysis of Heparan Sulfate Biosynthesis." FASEB Journal 34, S1 (April 2020): 1. http://dx.doi.org/10.1096/fasebj.2020.34.s1.03734.

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30

Kim, Jin Il, Sehee Park, Ilseob Lee, Kwang Sook Park, Eun Jung Kwak, Kwang Mee Moon, Chang Kyu Lee, Joon-Yong Bae, Man-Seong Park, and Ki-Joon Song. "Genome-Wide Analysis of Human Metapneumovirus Evolution." PLOS ONE 11, no. 4 (April 5, 2016): e0152962. http://dx.doi.org/10.1371/journal.pone.0152962.

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31

Mahmudi, Owais, Joel Sjöstrand, Bengt Sennblad, and Jens Lagergren. "Genome-wide probabilistic reconciliation analysis across vertebrates." BMC Bioinformatics 14, Suppl 15 (2013): S10. http://dx.doi.org/10.1186/1471-2105-14-s15-s10.

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32

Esko, Jeffrey D., Ryan J. Weiss, Philipp Spahn, Alejandro Gomez Toledo, Austin W. T. Chiang, Benjamin P. Kellman, Jing Li, et al. "Genome wide analysis of heparan sulfate assembly." FASEB Journal 34, S1 (April 2020): 1. http://dx.doi.org/10.1096/fasebj.2020.34.s1.00177.

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33

Sapra, Aparna K., Yoav Arava, Piyush Khandelia, and Usha Vijayraghavan. "Genome-wide Analysis of Pre-mRNA Splicing." Journal of Biological Chemistry 279, no. 50 (September 27, 2004): 52437–46. http://dx.doi.org/10.1074/jbc.m408815200.

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Removal of pre-mRNA introns is an essential step in eukaryotic genome interpretation. The spliceosome, a ribonucleoprotein performs this critical function; however, precise roles for many of its proteins remain unknown. Genome-wide consequences triggered by the loss of a specific factor can elucidate its function in splicing and its impact on other cellular processes. We have employed splicing-sensitive DNA microarrays, with yeast open reading frames and intron sequences, to detect changes in splicing efficiency and global expression. Comparison of expression profiles, for intron-containing transcripts, among mutants of two second-step factors, Prp17 and Prp22, reveals their unique and shared effects on global splicing. This analysis enabled the identification of substrates dependent on Prp17. We find a significant Prp17 role in splicing of introns which are longer than 200nts and note its dispensability when introns have a ≤13-nucleotide spacing between their branch point nucleotide and 3 ′ splice site.In vitrosplicing of substrates with varying branch nucleotide to 3 ′ splice site distances supports the differential Prp17 dependencies inferred from thein vivoanalysis. Furthermore, we tested the predicted dispensability of Prp17 for splicing short introns in the evolutionarily distant yeast,Schizosaccharomyces pombe, where the genome contains predominantly short introns. SpPrp17 was non-essential at all growth temperatures implying that functional evolution of splicing factors is integrated with genome evolution. Together our studies point to a role for budding yeast Prp17 in splicing of subsets of introns and have predictive value for deciphering the functions of splicing factors in gene expression and regulation in other eukaryotes.

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34

Ueki, Masao, and Heather J. Cordell. "Improved Statistics for Genome-Wide Interaction Analysis." PLoS Genetics 8, no. 4 (April 5, 2012): e1002625. http://dx.doi.org/10.1371/journal.pgen.1002625.

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35

LEE, MAXWELL P. "Genome-Wide Analysis of Epigenetics in Cancer." Annals of the New York Academy of Sciences 983, no. 1 (March 2003): 101–9. http://dx.doi.org/10.1111/j.1749-6632.2003.tb05965.x.

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36

Li, Yang, Zhixin Li, Shixin Zhou, Jinhua Wen, Bin Geng, Jichun Yang, and Qinghua Cui. "Genome-Wide Analysis of Human MicroRNA Stability." BioMed Research International 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/368975.

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Increasing studies have shown that microRNA (miRNA) stability plays important roles in physiology. However, the global picture of miRNA stability remains largely unknown. Here, we had analyzed genome-wide miRNA stability across 10 diverse cell types using miRNA arrays. We found that miRNA stability shows high dynamics and diversity both within individual cells and across cell types. Strikingly, we observed a negative correlation between miRNA stability and miRNA expression level, which is different from current findings on other biological molecules such as proteins and mRNAs that show positive and not negative correlations between stability and expression level. This finding indicates that miRNA has a distinct action mode, which we called “rapid production, rapid turnover; slow production, slow turnover.” This mode further suggests that high expression miRNAs normally degrade fast and may endow the cell with special properties that facilitate cellular status-transition. Moreover, we revealed that the stability of miRNAs is affected by cohorts of factors that include miRNA targets, transcription factors, nucleotide content, evolution, associated disease, and environmental factors. Together, our results provided an extensive description of the global landscape, dynamics, and distinct mode of human miRNA stability, which provide help in investigating their functions in physiology and pathophysiology.

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37

Cvijovic, M., R. Olivares-Hernandez, R. Agren, N. Dahr, W. Vongsangnak, I. Nookaew, K. R. Patil, and J. Nielsen. "BioMet Toolbox: genome-wide analysis of metabolism." Nucleic Acids Research 38, Web Server (May 18, 2010): W144—W149. http://dx.doi.org/10.1093/nar/gkq404.

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38

Rouhier, N. "Genome-wide analysis of plant glutaredoxin systems." Journal of Experimental Botany 57, no. 8 (May 19, 2006): 1685–96. http://dx.doi.org/10.1093/jxb/erl001.

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39

Yu, Jong W., and Mark A. Lemmon. "Genome-wide analysis of signaling domain function." Current Opinion in Chemical Biology 7, no. 1 (February 2003): 103–9. http://dx.doi.org/10.1016/s1367-5931(02)00008-x.

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40

de Bakker, P. I. W., B. M. Neale, and M. J. Daly. "Meta-Analysis of Genome-Wide Association Studies." Cold Spring Harbor Protocols 2010, no. 6 (June 1, 2010): pdb.top81. http://dx.doi.org/10.1101/pdb.top81.

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41

Zilberman, D., and S. Henikoff. "Genome-wide analysis of DNA methylation patterns." Development 134, no. 22 (October 17, 2007): 3959–65. http://dx.doi.org/10.1242/dev.001131.

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42

Treff, Nathan. "Genome-Wide Analysis of Human Preimplantation Aneuploidy." Seminars in Reproductive Medicine 30, no. 04 (June 21, 2012): 283–88. http://dx.doi.org/10.1055/s-0032-1313907.

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43

Lee, Young Ho, Jae-Hoon Kim, and Gwan Gyu Song. "Genome-wide pathway analysis of breast cancer." Tumor Biology 35, no. 8 (May 8, 2014): 7699–705. http://dx.doi.org/10.1007/s13277-014-2027-5.

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44

Bevilacqua, Philip C., Laura E. Ritchey, Zhao Su, and Sarah M. Assmann. "Genome-Wide Analysis of RNA Secondary Structure." Annual Review of Genetics 50, no. 1 (November 23, 2016): 235–66. http://dx.doi.org/10.1146/annurev-genet-120215-035034.

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45

Kim, Tae Hoon, and Bing Ren. "Genome-Wide Analysis of Protein-DNA Interactions." Annual Review of Genomics and Human Genetics 7, no. 1 (September 2006): 81–102. http://dx.doi.org/10.1146/annurev.genom.7.080505.115634.

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46

Heller, Ruth, and Daniel Yekutieli. "Replicability analysis for genome-wide association studies." Annals of Applied Statistics 8, no. 1 (March 2014): 481–98. http://dx.doi.org/10.1214/13-aoas697.

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47

Jost, Daniel, and Ralf Everaers. "Genome wide application of DNA melting analysis." Journal of Physics: Condensed Matter 21, no. 3 (December 17, 2008): 034108. http://dx.doi.org/10.1088/0953-8984/21/3/034108.

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48

Etzel, Carol J., Wei V. Chen, Neil Shepard, Damini Jawaheer, Francois Cornelis, Michael F. Seldin, Peter K. Gregersen, and Christopher I. Amos. "Genome-wide meta-analysis for rheumatoid arthritis." Human Genetics 119, no. 6 (April 13, 2006): 634–41. http://dx.doi.org/10.1007/s00439-006-0171-8.

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49

Lee, Y. H. "THU0019 Genome-wide pathway analysis of genome-wide association studies on psoriasis and behcet’s disease." Annals of the Rheumatic Diseases 71, Suppl 3 (June 2013): 160.1–160. http://dx.doi.org/10.1136/annrheumdis-2012-eular.1984.

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50

Lee, Young Ho, Sung Jae Choi, Jong Dae Ji, and Gwan Gyu Song. "Genome-wide pathway analysis of a genome-wide association study on psoriasis and Behcet’s disease." Molecular Biology Reports 39, no. 5 (December 27, 2011): 5953–59. http://dx.doi.org/10.1007/s11033-011-1407-9.

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