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Relevant bibliographies by topics / Genomics and transcriptomics / Books

Books on the topic 'Genomics and transcriptomics'

To see the other types of publications on this topic, follow the link: Genomics and transcriptomics.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

The dictionary of gene technology: Genomics, transcriptomics, proteomics. 2nd ed. Weinheim: Wiley-VCH, 2001.

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2

The dictionary of genomics, transcriptomics and proteomics: L - Q. 4th ed. Weinheim: Wiley-VCH, 2009.

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3

Westerfield, Monte, Detrich III H. William, and Leonard Zon. Zebrafish : Genetics, Genomics, and Transcriptomics: Genetics, Genomics, and Transcriptomics. Elsevier Science & Technology Books, 2016.

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4

Munro, Carol A., and Duncan Wilson. Fungal genomics and transcriptomics. Edited by Christopher C. Kibbler, Richard Barton, Neil A. R. Gow, Susan Howell, Donna M. MacCallum, and Rohini J. Manuel. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198755388.003.0006.

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The advent of whole-genome sequencing has resulted in a range of platforms for large-scale analysis of the DNA (genomics), RNA (transcriptomics), protein (proteomics), and metabolite (metabolomics) content of cells. These inclusive ‘omics’ approaches have allowed for unparalleled insights into fungal biology. In this chapter we will discuss how genomics and transcriptomics have been used to broaden our understanding of the biology of human pathogenic fungi and their interactions with their hosts.

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5

Westerfield, Monte, Detrich III H. William, and Leonard Zon. Zebrafish: Genetics, Genomics, and Transcriptomics. Elsevier Science & Technology Books, 2016.

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6

Kahl, Guenter. Dictionary of Genomics, Transcriptomics and Proteomics. Wiley & Sons, Limited, John, 2015.

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7

Plant Genomics and Transcriptomics [Working Title]. IntechOpen, 2019. http://dx.doi.org/10.5772/intechopen.82964.

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8

The Zebrafish - Genetics, Genomics, and Transcriptomics. Elsevier, 2016. http://dx.doi.org/10.1016/s0091-679x(16)x0005-2.

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9

Kahl, Guenter. Dictionary of Genomics, Transcriptomics and Proteomics. Wiley & Sons, Incorporated, John, 2015.

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10

The Dictionary Of Genomics Transcriptomics And Proteomics. Wiley-VCH Verlag GmbH, 2014.

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11

Passos, Geraldo A. Transcriptomics in Health and Disease. Springer International Publishing AG, 2021.

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12

Passos, Geraldo A. Transcriptomics in Health and Disease. Springer International Publishing AG, 2016.

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13

Passos, Geraldo A. Transcriptomics in Health and Disease. Springer, 2015.

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14

Passos, Geraldo A. Transcriptomics in Health and Disease. Springer, 2015.

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15

Husen, Azamal, and Altaf Ahmad. Genomics, Transcriptomics, Proteomics and Metabolomics of Crop Plants. Elsevier Science & Technology Books, 2023.

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16

Günter Kahl. The Dictionary of Gene Technology: Genomics, Transcriptomics, Proteomics. 2nd ed. Wiley-VCH, 2001.

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17

Kahl, Guenter. Dictionary of Genomics, Transcriptomics and Proteomics, 4 Volume Set. Wiley & Sons, Incorporated, John, 2015.

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18

Kahl, Guenter. Dictionary of Genomics, Transcriptomics and Proteomics, 4 Volume Set. Wiley & Sons, Incorporated, John, 2015.

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19

The Dictionary of Gene Technology: Genomics, Transcriptomics, Proteomics (Life Sciences). 3rd ed. Wiley-VCH, 2004.

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20

Liu, Yu. Omics in Clinical Practice: Genomics, Pharmacogenomics, Proteomics, and Transcriptomics in Clinical Research. Apple Academic Press, Incorporated, 2014.

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21

Liu, Yu. Omics in Clinical Practice: Genomics, Pharmacogenomics, Proteomics, and Transcriptomics in Clinical Research. Taylor & Francis Group, 2014.

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22

Cell Language Theory: A Molecular Theory of Genomics, Transcriptomics, and Proteomics. Imperial College Press, 2014.

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23

Bioinformatics and the Cell: Modern Computational Approaches in Genomics, Proteomics and Transcriptomics. Springer, 2007.

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24

Xia, Xuhua. Bioinformatics and the Cell: Modern Computational Approaches in Genomics, Proteomics and Transcriptomics. Springer, 2018.

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25

Xia, Xuhua. Bioinformatics and the Cell: Modern Computational Approaches in Genomics, Proteomics and Transcriptomics. Springer, 2010.

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26

Xia, Xuhua. Bioinformatics and the Cell: Modern Computational Approaches in Genomics, Proteomics and Transcriptomics. Springer, 2019.

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27

Xia, Xuhua. Bioinformatics and the Cell: Modern Computational Approaches in Genomics, Proteomics and Transcriptomics. Springer London, Limited, 2007.

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28

Macha, Muzafar A., Tariq A. masoodi, and Ajaz A. bhat. Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-Omic. Elsevier Science & Technology Books, 2024.

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29

Vermeulen, Roel, Douglas A. Bell, Dean P. Jones, Montserrat Garcia-Closas, Avrum Spira, Teresa W. Wang, Martyn T. Smith, Qing Lan, and Nathaniel Rothman. Application of Biomarkers in Cancer Epidemiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0006.

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Advancements in OMICs are now enabling investigators to explore comprehensively the biological consequences of exogenous and endogenous exposures by detecting molecular signatures of exposure, early signs of adverse biological effects, preclinical disease, and molecularly defined cancer subtypes. These new technologies have proven invaluable for assembling a comprehensive portrait of human exposure, health, and disease. This includes hypothesis-driven biomarkers, as well as platforms that can agnostically analyze entire biologic processes and “compartments,” including the measurement of small molecules (metabolomics), DNA polymorphisms and rarer inherited variants (genomics), methylation and microRNA (epigenomics), chromosome-wide alterations, mRNA (transcriptomics), proteins (proteomics), and the microbiome (microbiomics). Although the implementation of these technologies in epidemiologic studies has already shown great promise, some challenges of particular importance must be addressed. Non-genetic OMIC markers vary over time due to both random variation and physiologic changes. Therefore, there is an urgent need for cohorts to collect repeat biological samples over time.

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30

Xi, Jianing, and Zhenhua Yu, eds. Unsupervised Learning Models for Unlabeled Genomic, Transcriptomic & Proteomic Data. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88971-967-9.

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31

Analyse des genomes transcriptomes et proteomes troisième édition. Dunod, 2001.

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32

Renner, Tanya, Tianying Lan, Kimberly M. Farr, Enrique Ibarra-Laclette, Luis Herrera-Estrella, Stephan C. Schuster, Mitsuyasu Hasebe, Kenji Fukushima, and Victor A. Albert. Carnivorous plant genomes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779841.003.0011.

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Carnivorous plant genome research has focused on members of the Lamiales and Oxalidales; the most complete sequences are for Utricularia gibba and Cephalotus follicularis. The size-limited U. gibba genome highlights the importance of small-scale tandem duplications, which likely play roles in this species’ carnivorous adaptation. Sequencing of the C. follicularis genome detected adaptive changes that may explain the evolution of traits associated with attraction, trapping, digestion, and absorption. Functional consequences of genes putatively missing in the U. gibba genome, yet present in other angiosperms, may have influenced the evolution of polyploidy, physiology, and a rootless Bauplan. Additional draft nuclear genomes and transcriptomes are available for carnivorous Caryophyllales, Ericales, Lamiales, and Poales, but are limited in quantity and quality. Chloroplast genomes of carnivorous Lentibulariaceae have revealed interesting patterns of gene loss, alterations in the proportion of repeat DNA, and plastome-wide increases in substitution rates.

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33

Ellison, Aaron M., and Lubomír Adamec. The future of research with carnivorous plants. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779841.003.0029.

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The material presented in the chapters of Carnivorous Plants: Physiology, Ecology, and Evolution together provide a suite of common themes that could provide a framework for increasing progress in understanding carnivorous plants. All speciose genera would benefit from more robust, intra-generic classifications in a phylogenetic framework that uses a unified species concept. As more genomic, proteomic, and transcriptomic data accrue, new insights will emerge regarding trap biochemistry and regulation; interactions with commensals; and the importance of intraspecific variability on which natural selection works. Continued elaboration of field experiments will provide new insights into basic physiology; population biology; plant-animal and plant-microbe relationships; and evolutionary dynamics, all of which will aid conservation efforts and contribute to discussions of assisted migration as the climate continues to change.

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