Investigating normal human gene expression in tissues with high-throughput transcriptomic and proteomic data.
type: CreativeWork
description: With the improvement of high-throughput technologies during the last decade, several studies exploring the normal gene expression in human tissues have been published. Many studies examine the transcriptome with RNA sequencing (RNA-Seq), and others probe the proteome with unlabelled bottom-up Mass Spectrometry. As the sampling of undiseased tissues is difficult, the community often refers to expression atlases, which are collating these studies, to support or validate new findings. Despite many overlapping tissues between the studies, few atlases attempt to integrate all the data. In this thesis, I investigate the consistency of gene expression across tissues and studies in human with the help of transcriptomics captured with high-throughput sequencing (RNA-Seq) and proteomics generated with label-free bottom-up Mass Spectrometry (MS). After describing the transcriptomic and proteomic data and their state-of-art processing (Chapter 2), I review several identified sources of biases and my approaches to limit their effects (Chapter 3). The integration of the various transcriptomic datasets (Chapter 4) shows that the biological signal dominates the technical noise for RNA-Seq data. Tissue samples display higher levels of correlation for identical tissues in other studies than for other tissues in the same datasets. In other words, interstudy correlations for identical tissues are higher than correlations between different tissues within the same study. Globally, genes show similar expression profiles across studies for a given set of tissues. All genes categories are involved, including the tissue-specific genes and the ubiquitously expressed ones. After briefly discussing comparisons of proteomic data, I introduce a new proteomic quantification method, PPKM (Chapter 5). The PPKM method allows me to quantify about twice as many proteins compared to usual methods. Limited numbers of previous studies have shown various correlation levels between the expression of protein…
abstract: With the improvement of high-throughput technologies during the last decade, several studies exploring the normal gene expression in human tissues have been published. Many studies examine the transcriptome with RNA sequencing (RNA-Seq), and others probe the proteome with unlabelled bottom-up Mass Spectrometry. As the sampling of undiseased tissues is difficult, the community often refers to expression atlases, which are collating these studies, to support or validate new findings. Despite many overlapping tissues between the studies, few atlases attempt to integrate all the data. In this thesis, I investigate the consistency of gene expression across tissues and studies in human with the help of transcriptomics captured with high-throughput sequencing (RNA-Seq) and proteomics generated with label-free bottom-up Mass Spectrometry (MS). After describing the transcriptomic and proteomic data and their state-of-art processing (Chapter 2), I review several identified sources of biases and my approaches to limit their effects (Chapter 3). The integration of the various transcriptomic datasets (Chapter 4) shows that the biological signal dominates the technical noise for RNA-Seq data. Tissue samples display higher levels of correlation for identical tissues in other studies than for other tissues in the same datasets. In other words, interstudy correlations for identical tissues are higher than correlations between different tissues within the same study. Globally, genes show similar expression profiles across studies for a given set of tissues. All genes categories are involved, including the tissue-specific genes and the ubiquitously expressed ones. After briefly discussing comparisons of proteomic data, I introduce a new proteomic quantification method, PPKM (Chapter 5). The PPKM method allows me to quantify about twice as many proteins compared to usual methods. Limited numbers of previous studies have shown various correlation levels between the expression of protein…
author: Barzine, Mitra
language: eng
keywords: transcriptomics, RNA-seq, gene expression, proteomics, bottom-up label free, human tissues
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/bd5bbbda-a053-424e-b831-624edc92805f/download
license: https://creativecommons.org/licenses/by/4.0/
date published: 2020-07-01
date modified: 2026-07-22T22:24:14Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.69592