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You searched for +publisher:"Penn State University" +contributor:("Yu Zhang, Outside Member"). Showing records 1 – 2 of 2 total matches.

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Penn State University

1. Kakumanu, Akshay. COMPUTATIONAL MODELLING OF SEQUENCE FEATURES DRIVING TRANSCRIPTION FACTOR BINDING IN NEURONAL REPROGRAMMING.

Degree: 2017, Penn State University

Transcription factors (TF) bind to highly selective regions of the genome. When ectopically expressed in a defined combination, they enable direct conversion of cellular identities. The thesis of this dissertation is that stratification of TF binding targets through meaningful genomic annotations, followed by the discovery of associated sequence signals, will greatly enhance our understanding of binding site selectivity. However, such analyses are often confounded by uneven overlaps of genomic annotations. As a principle approach to motif discovery, this dissertation introduces SeqUnwinder – a computational framework to deconvolve sequence features associated with overlapping genomic annotations at regulatory sites. Using the novel analyses abilities of SeqUnwinder, we make important contributions towards understanding TF binding in neuronal reprogramming systems. It was previously shown that overexpression of Ngn2, Isl1 and Lhx3 (NIL) effectively programs spinal motor neurons from mouse embryonic stem cells (mESC) within 48 hours. This dissertation unravels the mechanisms of TF binding in NIL reprogramming. Based on SeqUnwinder’s prediction, we show that Onecut factors act as facilitators and synergize with Isl1/Lhx3 to gain access to additional binding targets crucial for reprogramming. In another study, we provide insights into the mechanism through which two pro-neuronal basic helix loop helix factors, Ascl1 and Ngn2, program general neurons with preferential inclinations towards specific neuronal subtypes. Using SeqUnwinder’s approach, we show that the binding preferences of the DNA binding domains of Ascl1 and Ngn2 directly bestows them with distinct instructive abilities to program different neuronal subtypes. Advisors/Committee Members: Shaun A Mahony, Dissertation Advisor/Co-Advisor, Shaun A Mahony, Committee Chair/Co-Chair, Benjamin Franklin Pugh, Committee Member, Ross Cameron Hardison, Committee Member, Yu Zhang, Outside Member, Esteban O. Mazzoni, Committee Member.

Subjects/Keywords: discriminative features; transcription factor binding sites; DNaseI hypersensitive sites; motif discovery; cellular reprogramming; transcription factor binding; spinal motor neurons; Ngn2; Ascl1; Lhx3; Isl1

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

APA (6th Edition):

Kakumanu, A. (2017). COMPUTATIONAL MODELLING OF SEQUENCE FEATURES DRIVING TRANSCRIPTION FACTOR BINDING IN NEURONAL REPROGRAMMING. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14794auk262

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):

Kakumanu, Akshay. “COMPUTATIONAL MODELLING OF SEQUENCE FEATURES DRIVING TRANSCRIPTION FACTOR BINDING IN NEURONAL REPROGRAMMING.” 2017. Thesis, Penn State University. Accessed April 17, 2021. https://submit-etda.libraries.psu.edu/catalog/14794auk262.

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

MLA Handbook (7th Edition):

Kakumanu, Akshay. “COMPUTATIONAL MODELLING OF SEQUENCE FEATURES DRIVING TRANSCRIPTION FACTOR BINDING IN NEURONAL REPROGRAMMING.” 2017. Web. 17 Apr 2021.

Vancouver:

Kakumanu A. COMPUTATIONAL MODELLING OF SEQUENCE FEATURES DRIVING TRANSCRIPTION FACTOR BINDING IN NEURONAL REPROGRAMMING. [Internet] [Thesis]. Penn State University; 2017. [cited 2021 Apr 17]. Available from: https://submit-etda.libraries.psu.edu/catalog/14794auk262.

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

Council of Science Editors:

Kakumanu A. COMPUTATIONAL MODELLING OF SEQUENCE FEATURES DRIVING TRANSCRIPTION FACTOR BINDING IN NEURONAL REPROGRAMMING. [Thesis]. Penn State University; 2017. Available from: https://submit-etda.libraries.psu.edu/catalog/14794auk262

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


Penn State University

2. Verma, Anurag. PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA.

Degree: 2018, Penn State University

In an emerging approach called precision medicine, the primary focus is to utilize an individual’s clinical data along with genetic, environmental, and lifestyle information to tailor clinical care. The initial steps toward precision medicine involve enrolling individuals into studies to link their genotype and phenotype data. Patient data can be used to discover clinically relevant genetic associations. The most common methodology to identify genetic associations is called an genome-wide association study (GWAS), in which tests for associations are performed between single-nucleotide polymorphisms (SNPs) across the genome (usually over 500,000 SNPs) and a single disease outcome or trait. There is now a growing amount of evidence to demonstrate the success of some of these genetic associations. However, the impact of GWAS has been limited due to its focus on a single phenotype, and hence, the effect of a given SNP across multiple phenotypes cannot be explored. An alternative approach called a phenome-wide association study (PheWAS) has been successful in simultaneously scanning genome-wide significant variants over hundreds of phenotypes. Using this approach, we can identify genetic variants associated with a wide range of phenotypes, also referred to as cross-phenotype associations. Such findings have the potential to identify pleiotropy (where one variant is affecting two or more independent phenotypes with same underlying biological mechanism) or an underlying genetic architecture of comorbidities. The majority of PheWAS have used data from de-identified electronic health records (EHRs) linked to genotype data, and a few have been performed in large-scale epidemiologic studies and clinical trials. While existing studies have demonstrated the development of PheWAS methodology, the focus has remained on a small set of genome-wide significant SNPs or a genomic region of iv interest. After advances in genotyping and sequencing technologies, as well as in phenotype data collection, it is imperative to apply PheWAS on a genome-wide scale. It will allow us to investigate genetic associations across all SNPs and phenotypes in a study population. However, there can be many challenges with expanding the current PheWAS approach to investigate associations across the genome. In this dissertation, I aim to address following specific challenges regarding large-scale PheWAS analysis. 1) Evaluating heterogeneous groups simultaneously makes precision medicine impossible; stratifying samples based on context such as age, sex, or drugs can help to improve precision in identifying true genetic associations (Chapter 2). 2) The number of association tests raise the statistical threshold of significance in such a way that finding the significant associations is difficult. Also, the impact of factors such as sample size, casecontrol ratio, and minor allele frequency on the statistical power to identify associations have not been explored (Chapter 3). 3) Integrating results from independent PheWAS using different types of data sets (e.g.,… Advisors/Committee Members: Marylyn Ritchie, Dissertation Advisor/Co-Advisor, Moriah Louise Szpara, Committee Chair/Co-Chair, Ross Cameron Hardison, Committee Member, N/A, Committee Member, Yu Zhang, Outside Member.

Subjects/Keywords: PheWAS; EHR; GWAS; Genomics; Biobank

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

APA (6th Edition):

Verma, A. (2018). PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15007auv13

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):

Verma, Anurag. “PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA.” 2018. Thesis, Penn State University. Accessed April 17, 2021. https://submit-etda.libraries.psu.edu/catalog/15007auv13.

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

MLA Handbook (7th Edition):

Verma, Anurag. “PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA.” 2018. Web. 17 Apr 2021.

Vancouver:

Verma A. PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Apr 17]. Available from: https://submit-etda.libraries.psu.edu/catalog/15007auv13.

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

Council of Science Editors:

Verma A. PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15007auv13

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

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