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You searched for subject:(GRO seq). Showing records 1 – 6 of 6 total matches.

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University of Arizona

1. Morera, Andrés Alejandro. Reciprocal Regulation of Transcription for Protein-Coding and Non-Coding Alu Transcripts by TDP-43 .

Degree: 2019, University of Arizona

 TAR DNA binding protein of 43 kDa (TDP-43) is a ubiquitously expressed RNA and DNA binding protein with reported functions in transcription, processing, transport, and… (more)

Subjects/Keywords: Alu element; GRO-Seq; TDP-43; transcription

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APA (6th Edition):

Morera, A. A. (2019). Reciprocal Regulation of Transcription for Protein-Coding and Non-Coding Alu Transcripts by TDP-43 . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/631907

Chicago Manual of Style (16th Edition):

Morera, Andrés Alejandro. “Reciprocal Regulation of Transcription for Protein-Coding and Non-Coding Alu Transcripts by TDP-43 .” 2019. Doctoral Dissertation, University of Arizona. Accessed January 22, 2021. http://hdl.handle.net/10150/631907.

MLA Handbook (7th Edition):

Morera, Andrés Alejandro. “Reciprocal Regulation of Transcription for Protein-Coding and Non-Coding Alu Transcripts by TDP-43 .” 2019. Web. 22 Jan 2021.

Vancouver:

Morera AA. Reciprocal Regulation of Transcription for Protein-Coding and Non-Coding Alu Transcripts by TDP-43 . [Internet] [Doctoral dissertation]. University of Arizona; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10150/631907.

Council of Science Editors:

Morera AA. Reciprocal Regulation of Transcription for Protein-Coding and Non-Coding Alu Transcripts by TDP-43 . [Doctoral Dissertation]. University of Arizona; 2019. Available from: http://hdl.handle.net/10150/631907


University of California – San Diego

2. Hetzel, Jonathan. Characterization of Arabidopsis thaliana Transcription through an Analysis of Nascent Transcripts.

Degree: Biology, 2016, University of California – San Diego

 Plant transcription is simultaneously one of the most unique forms of transcription due to the presence of two additional RNA polymerases not found in other… (more)

Subjects/Keywords: Biology; Plant sciences; Molecular biology; GRO-seq; transcription

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APA (6th Edition):

Hetzel, J. (2016). Characterization of Arabidopsis thaliana Transcription through an Analysis of Nascent Transcripts. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/7mw1n7vg

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hetzel, Jonathan. “Characterization of Arabidopsis thaliana Transcription through an Analysis of Nascent Transcripts.” 2016. Thesis, University of California – San Diego. Accessed January 22, 2021. http://www.escholarship.org/uc/item/7mw1n7vg.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hetzel, Jonathan. “Characterization of Arabidopsis thaliana Transcription through an Analysis of Nascent Transcripts.” 2016. Web. 22 Jan 2021.

Vancouver:

Hetzel J. Characterization of Arabidopsis thaliana Transcription through an Analysis of Nascent Transcripts. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2021 Jan 22]. Available from: http://www.escholarship.org/uc/item/7mw1n7vg.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hetzel J. Characterization of Arabidopsis thaliana Transcription through an Analysis of Nascent Transcripts. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/7mw1n7vg

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Cornell University

3. Hah, Nasun. Signal Regulated Gene Expression: Defining The Effects Of Estrogen Signaling Through Genomic And Proteomic Analyses.

Degree: PhD, Biochemistry, 2011, Cornell University

 Estrogens play crucial roles in regulating gene expression in physiological and disease states. Estrogens acts through estrogen receptors (ERs) and their binding sites in genomic… (more)

Subjects/Keywords: estrogen; estrogen receptor; GRO-seq; swi/snf; baf57; baf180; silac; proteomic; enhancer; edc; estrogen signaling

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APA (6th Edition):

Hah, N. (2011). Signal Regulated Gene Expression: Defining The Effects Of Estrogen Signaling Through Genomic And Proteomic Analyses. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/33589

Chicago Manual of Style (16th Edition):

Hah, Nasun. “Signal Regulated Gene Expression: Defining The Effects Of Estrogen Signaling Through Genomic And Proteomic Analyses.” 2011. Doctoral Dissertation, Cornell University. Accessed January 22, 2021. http://hdl.handle.net/1813/33589.

MLA Handbook (7th Edition):

Hah, Nasun. “Signal Regulated Gene Expression: Defining The Effects Of Estrogen Signaling Through Genomic And Proteomic Analyses.” 2011. Web. 22 Jan 2021.

Vancouver:

Hah N. Signal Regulated Gene Expression: Defining The Effects Of Estrogen Signaling Through Genomic And Proteomic Analyses. [Internet] [Doctoral dissertation]. Cornell University; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/1813/33589.

Council of Science Editors:

Hah N. Signal Regulated Gene Expression: Defining The Effects Of Estrogen Signaling Through Genomic And Proteomic Analyses. [Doctoral Dissertation]. Cornell University; 2011. Available from: http://hdl.handle.net/1813/33589


Wayne State University

4. Al-Husini, Nadra. An Investigation Into The Role Of Cfia 3' End Processing Complex In The Termination And Initiation/reinitiation Of Transcription.

Degree: PhD, Biological Sciences, 2013, Wayne State University

  In budding yeast, as in higher eukaryotes, transcription of protein coding genes is executed by a highly specialized, conserved polymerase called RNA polymerase II… (more)

Subjects/Keywords: Antisense Transcription; GRO-Seq; Promoter Directionality; RNA Polymerase II; Termination; Transcription; Biology

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APA (6th Edition):

Al-Husini, N. (2013). An Investigation Into The Role Of Cfia 3' End Processing Complex In The Termination And Initiation/reinitiation Of Transcription. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/868

Chicago Manual of Style (16th Edition):

Al-Husini, Nadra. “An Investigation Into The Role Of Cfia 3' End Processing Complex In The Termination And Initiation/reinitiation Of Transcription.” 2013. Doctoral Dissertation, Wayne State University. Accessed January 22, 2021. https://digitalcommons.wayne.edu/oa_dissertations/868.

MLA Handbook (7th Edition):

Al-Husini, Nadra. “An Investigation Into The Role Of Cfia 3' End Processing Complex In The Termination And Initiation/reinitiation Of Transcription.” 2013. Web. 22 Jan 2021.

Vancouver:

Al-Husini N. An Investigation Into The Role Of Cfia 3' End Processing Complex In The Termination And Initiation/reinitiation Of Transcription. [Internet] [Doctoral dissertation]. Wayne State University; 2013. [cited 2021 Jan 22]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/868.

Council of Science Editors:

Al-Husini N. An Investigation Into The Role Of Cfia 3' End Processing Complex In The Termination And Initiation/reinitiation Of Transcription. [Doctoral Dissertation]. Wayne State University; 2013. Available from: https://digitalcommons.wayne.edu/oa_dissertations/868


University of Colorado

5. Azofeifa, Joseph Gaspare. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.

Degree: PhD, 2017, University of Colorado

  Seventy-six percent of disease associated variants occur in non-genic sites of open chromatin suggesting that the regulation of gene expression plays a crucial role… (more)

Subjects/Keywords: genetics; gro-seq; hidden markov models; high throughput sequencing; machine learning; mixture models; Bioinformatics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Azofeifa, J. G. (2017). Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/193

Chicago Manual of Style (16th Edition):

Azofeifa, Joseph Gaspare. “Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.” 2017. Doctoral Dissertation, University of Colorado. Accessed January 22, 2021. https://scholar.colorado.edu/csci_gradetds/193.

MLA Handbook (7th Edition):

Azofeifa, Joseph Gaspare. “Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.” 2017. Web. 22 Jan 2021.

Vancouver:

Azofeifa JG. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. [Internet] [Doctoral dissertation]. University of Colorado; 2017. [cited 2021 Jan 22]. Available from: https://scholar.colorado.edu/csci_gradetds/193.

Council of Science Editors:

Azofeifa JG. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. [Doctoral Dissertation]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/csci_gradetds/193


University of Colorado

6. Azofeifa, Joseph Gaspare. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.

Degree: PhD, 2017, University of Colorado

  Seventy-six percent of disease associated variants occur in non-genic sites of open chromatin suggesting that the regulation of gene expression plays a crucial role… (more)

Subjects/Keywords: genetics; GRO-seq; hidden Markov models; high throughput sequencing; machine learning; mixture models; Bioinformatics; Molecular Genetics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Azofeifa, J. G. (2017). Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/137

Chicago Manual of Style (16th Edition):

Azofeifa, Joseph Gaspare. “Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.” 2017. Doctoral Dissertation, University of Colorado. Accessed January 22, 2021. https://scholar.colorado.edu/csci_gradetds/137.

MLA Handbook (7th Edition):

Azofeifa, Joseph Gaspare. “Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity.” 2017. Web. 22 Jan 2021.

Vancouver:

Azofeifa JG. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. [Internet] [Doctoral dissertation]. University of Colorado; 2017. [cited 2021 Jan 22]. Available from: https://scholar.colorado.edu/csci_gradetds/137.

Council of Science Editors:

Azofeifa JG. Stochastic Modeling of RNA Polymerase Predicts Transcription Factor Activity. [Doctoral Dissertation]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/csci_gradetds/137

.