| Popis: |
Following advancements in the "omics" fields of molecular biology and genetics, much attention has been focused on categorising and annotating the large volume of data that has been produced since the sequencing of human and model genomes. With high-throughput data generated from these "omics" experiments and the increasing deposition of information from genetics experiments in biological databases, our understanding of the mechanisms that bridge the gap from genotype to phenotype can be explored in a holistic context. This is one of the aims of the relatively new field of systems biology, which aims to understand the complexity of biological systems in a holistic manner by studying the system as an ensemble of interacting parts. With increased volume and comprehensiveness of biological data, prediction of gene function and automatic identification of potential models for human diseases have become important aspects of systems-level analysis for wet-lab geneticists and clinicians. Here, I describe an integrated analysis of mouse phenotype data with high-throughput experiments to give genome-wide information about gene relationships and their function in a systems biology context. I show a functional dissection of mouse gene and phenotype networks and investigate the potential that ontology-compliant phenotype annotations can offer for functional classification of genes. The mouse genome and phenome show modularity at higher levels of cellular, physiological and organismal function. Using high-throughput protein-protein interaction data, the mouse proteome was dissected and computationally extracted communities were used to predict phenotypes of mouse gene ablation. Precision and recall curves show comparable performance for higher levels of the MP ontology to those undertaken by comprehensive mouse gene function prediction such as the Mouse Function Project which predicted Gene Ontology terms. I also developed and tested an automatic procedure that relates mouse phenotypes to human diseases and demonstrate its application to the use cases of identifying mouse models given a query consisting of a set of mouse phenotypes and breaking down human diseases into mouse phenotypes. Taken together, my results may be useful as a map for candidate gene discovery, finding how mouse networks relate to human networks and investigating the evolutionary origins of their components at higher levels of gene function. |
