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Computational analysis and interpretation of multi-omics data
In the last decade, with huge advances in high throughput sequencing (HT-seq) technologies and rapidly decreasing costs, cell and molecular biology are becoming increasingly a heavily "data-driven" science. HT-seq has transformed the scientific landscape in biology by allowing researchers to answer important biological questions in multiple biological layers with multi-omics data. In this thesis, I will introduce my work on computational analysis, interpretation and application of multi-omics data on bulk as well on single cell levels. RNA-seq, the next-generation sequencing of RNAs is a powerful method to characterize genome-wide differential gene expression between different conditions. ChIP-seq, the high throughput chromatin immuno-precipitation sequencing technology has been a powerful tool to identify genome-wide profiles of histone modifications which have been identified to be the key epigenetic mechanisms in the regulation of gene expression. More and more studies start analyzing simultaneously the combination of RNA-seq data and ChIP-seq data of different histone modifications across different conditions. The integrative analysis of these corresponding data sets, in principle, becomes a desirable option to study gene regulation in the complex and dynamic biological processes for example in organ development and disease progression. However, computational tools for such analyses are still technically in their infancy. In the first part of this thesis, I introduce intePareto, a novel method to prioritize genes with consistent changes in RNA-seq and ChIP-seq data of different histone modifications between different conditions using Pareto optimization. In addition to the rapid development and applications in bulk sequencing of pooled cell populations discussed above, the past decade has witnessed tremendous progress in single cell RNA sequencing (scRNA-seq) technologies which have further revolutionized our understanding of the fundamental biological and physiological phenomena at the single cell ...
Computational analysis and interpretation of multi-omics data
In the last decade, with huge advances in high throughput sequencing (HT-seq) technologies and rapidly decreasing costs, cell and molecular biology are becoming increasingly a heavily "data-driven" science. HT-seq has transformed the scientific landscape in biology by allowing researchers to answer important biological questions in multiple biological layers with multi-omics data. In this thesis, I will introduce my work on computational analysis, interpretation and application of multi-omics data on bulk as well on single cell levels. RNA-seq, the next-generation sequencing of RNAs is a powerful method to characterize genome-wide differential gene expression between different conditions. ChIP-seq, the high throughput chromatin immuno-precipitation sequencing technology has been a powerful tool to identify genome-wide profiles of histone modifications which have been identified to be the key epigenetic mechanisms in the regulation of gene expression. More and more studies start analyzing simultaneously the combination of RNA-seq data and ChIP-seq data of different histone modifications across different conditions. The integrative analysis of these corresponding data sets, in principle, becomes a desirable option to study gene regulation in the complex and dynamic biological processes for example in organ development and disease progression. However, computational tools for such analyses are still technically in their infancy. In the first part of this thesis, I introduce intePareto, a novel method to prioritize genes with consistent changes in RNA-seq and ChIP-seq data of different histone modifications between different conditions using Pareto optimization. In addition to the rapid development and applications in bulk sequencing of pooled cell populations discussed above, the past decade has witnessed tremendous progress in single cell RNA sequencing (scRNA-seq) technologies which have further revolutionized our understanding of the fundamental biological and physiological phenomena at the single cell ...
Computational analysis and interpretation of multi-omics data
Cao, Yingying (author) / Hoffmann, Daniel
2021-05-28
Theses
Electronic Resource
English
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