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Improved tag-count approaches for label-free quantitation of proteome differences in bottom-up proteomic experiments

Branson, Owen E
http://orcid.org/0000-0003-4611-0937
http://rave.ohiolink.edu/etdc/view?acc_num=osu1471553685

Abstract Details

2016, Doctor of Philosophy, Ohio State University, Biochemistry Program, Ohio State.
This dissertation describes the research that was conducted on the development of label-free quantitation procedures for the identification and quantitation of proteome differences determined from shotgun proteomics experiments. Chapter 1 introduces common approaches of which their basic understanding of is imperative for all proteomic scientists. This introductory chapter also describes label-free quantitation approaches, which is built upon in following chapters. Chapter 2 outlines a novel approach to perform label-free spectral count quantitation from shotgun proteomic experiments. This approach, termed MultiSpec, utilizes open-source statistical platforms; namely edgeR, DESeq and baySeq, to statistically select protein candidates for further investigation. The results from these three statistical approaches are combined to provide a single ranked list of differentially expressed proteins. The statistical results from multiple proteomic pipelines are integrated and cross-validated by means of collapsing protein groups. Chapter 3 highlights the efficient application of negative binomial based tag-count analysis of large-scale proteomics. This chapter illustrates the efficacy of edgeR to perform spectral count quantitation across a large number of samples. Chapter 4 demonstrates the use of precursor abundance (MS1) quantitation, an alternative to spectral count quantitation, to quantitate proteome differences in chromatin-bound androgen receptor protein complexes pivotal in directing proper gene expression in the context of localized human prostate cancer. Also presented in chapter 4, precursor intensities were used to determine proteome differences between the prostate proteomes of a transgenic mouse model of prostatic intraepithelial neoplasia (PIN). In a collaborative effort, these data were overlaid with RNA sequencing and Chromatin-Immunoprecipitation sequencing data to identify a proteome set of putative androgen receptor regulated proteins.
Michael Freitas (Advisor)
184 p.
Biochemistry
Proteomics; Shotgun Proteomics; label-free; spectral counting; spectral count; edgeR; DESeq; baySeq; MultiSpec

Recommended Citations

Citations

  • Branson, O. E. (2016). Improved tag-count approaches for label-free quantitation of proteome differences in bottom-up proteomic experiments [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471553685

    APA Style (7th edition)

  • Branson, Owen. Improved tag-count approaches for label-free quantitation of proteome differences in bottom-up proteomic experiments. 2016. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1471553685.

    MLA Style (8th edition)

  • Branson, Owen. "Improved tag-count approaches for label-free quantitation of proteome differences in bottom-up proteomic experiments." Doctoral dissertation, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471553685

    Chicago Manual of Style (17th edition)

osu1471553685
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© 2016, some rights reserved.Creative Commons License Improved tag-count approaches for label-free quantitation of proteome differences in bottom-up proteomic experiments by Owen E Branson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.