Probabilistic analysis of the human transcriptome with side information

TL;DR

This paper introduces novel probabilistic and statistical methods for analyzing the human transcriptome, integrating side information to uncover functional mechanisms and improve understanding of genome regulation.

Contribution

It develops new computational tools combining statistical evidence and background data to analyze high-dimensional genomic data, revealing insights into cell networks and cancer mechanisms.

Findings

New exploratory tools for high-throughput data analysis

Identification of functional mechanisms in genome data

Open source implementations for community use

Abstract

Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput measurement technologies and efficient sharing of research material through community databases have opened up new views to the study of living organisms and the structure of life. In this thesis, novel computational strategies have been developed to investigate a key functional layer of genetic information, the human transcriptome, which regulates the function of living cells through protein synthesis. The key contributions of the thesis are general exploratory tools for high-throughput data analysis that have provided new insights to cell-biological networks, cancer mechanisms and other aspects of genome function. A central challenge in functional genomics is that high-dimensional genomic observations are associated with high levels of complex and largely unknown sources of variation. By combining statistical evidence across multiple measurement sources and the wealth of background information in genomic data repositories it has been possible to solve some the uncertainties associated with individual observations and to identify functional mechanisms that could not be detected based on individual measurement sources. Statistical learning and probabilistic models provide a natural framework for such modeling tasks. Open source implementations of the key methodological contributions have been released to facilitate further adoption of the developed methods by the research community.