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Books on the topic 'Transcriptomic analysi'

Author: Grafiati
Published: 10 March 2023

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1

Cellerino, Alessandro, and Michele Sanguanini. Transcriptome Analysis. Pisa: Scuola Normale Superiore, 2018. http://dx.doi.org/10.1007/978-88-7642-642-1.

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2

Wang, Yejun, and Ming-an Sun, eds. Transcriptome Data Analysis. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7710-9.

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3

Genome transcriptome and proteome analysis. Chichester: Wiley, 2004.

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4

Bernot, Alain. Genome Transcriptome and Proteome Analysis. New York: John Wiley & Sons, Ltd., 2005.

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5

Krizay, Daniel Kyle. Transcriptomic and Functional Analysis of Neuronal Activity and Disease. [New York, N.Y.?]: [publisher not identified], 2022.

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6

Lee, Albert Kim. Characterizing Immune Responses to Marburg Virus Infection in Animal Hosts Using Statistical Transcriptomic Analysis. [New York, N.Y.?]: [publisher not identified], 2018.

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7

Scanfeld, Daniel. Exploring the Plasmodium falciparum Transcriptome Using Hypergeometric Analysis of Time Series (HATS). [New York, N.Y.?]: [publisher not identified], 2013.

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8

author, Tuimala Jarno, Somervuo Panu author, Huss Mikael author, and Wong Garry author, eds. RNA-seq data analysis: A practical approach. Boca Raton: CRC Press, Taylor & Francis Group, 2015.

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9

Blumenberg, Miroslav, ed. Transcriptome Analysis. IntechOpen, 2019. http://dx.doi.org/10.5772/intechopen.77860.

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10

Transcriptome Analysis. IntechOpen, 2019.

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11

Transcriptomics: Expression Pattern Analysis. VDM Verlag, 2009.

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12

Bernot, Alain. Genome Transcriptome and Proteome Analysis. Wiley & Sons, Incorporated, John, 2005.

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13

Bernot, Alain. Genome Transcriptome and Proteome Analysis. Wiley & Sons, Incorporated, John, 2007.

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14

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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15

Transcriptome Data Analysis: Methods and Protocols. Humana, 2018.

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16

Wang, Yejun, and Ming-an Sun. Transcriptome Data Analysis: Methods and Protocols. Springer New York, 2019.

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17

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

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18

Cellerino, Alessandro, and Michele Sanguanini. Transcriptome Analysis: Introduction and Examples from the Neurosciences. Edizioni della Normale, 2019.

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19

Tuimala, Jarno, Eija Korpelainen, Panu Somervuo, Mikael Huss, and Garry Wong. RNA-Seq Data Analysis: A Practical Approach. Taylor & Francis Group, 2014.

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20

Tuimala, Jarno, Eija Korpelainen, Panu Somervuo, Mikael Huss, and Garry Wong. RNA-Seq Data Analysis: A Practical Approach. Taylor & Francis Group, 2014.

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21

Tuimala, Jarno, Eija Korpelainen, Panu Somervuo, Mikael Huss, and Garry Wong. RNA-Seq Data Analysis: A Practical Approach. Taylor & Francis Group, 2014.

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22

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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23

Iannuccilli, William. The analysis of the partial neural transcriptome of Aplysia californica by sequencing 6,034 ESTs generated from normalized cDNA library from the pleural-pedal ganglia. 2003.

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24

Lee, Junghyun. Laying the foundation for molecular studies of learning and memory using Aplysia californica: Sequencing neuronal soma- and process-specific cDNA libraries and transcriptome analysis. 2003.

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