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Genome Landscapes of Disease: Strategies to Predict the Phenotypic Consequences of Human Germline and Somatic Variation

Year: 2016

Type: article

Abstract: Computational biology can marry disciplines to help solve some of the most pressing problems in medical research.When built on fundamental evolutionary, biological, and/or physics principles and provided with large quantities of diverse experimental data, computing power, and rigorous statistics tools, efficient and effective computational strategies can help unlock the secrets of the genome to cure disease.Computational biology has undertaken this challenge, spearheading efforts to decode the genetic blueprints encrypted in the human germline and in somatic variations to decipher complex phenotypic consequences.It devises new and creative approaches to sift through the immense-and rapidly growing-assembled human genetic material, its products, the proteome and the metabolome, and their possible clinical ramifications.The cell is the basic unit of life; the nucleus houses the genetic material that is believed to have provided a distinct advantage to the evolving cell.The organization of the genome varies; it depends on cell type, stage of development, differentiation, disease status, and more.The higher-order spatial and temporal organization of genomes-which itself is a function of conditions and environment-is a driver of biological function in differentiation, development, and disease.Studies of genomics, epigenetics, big data analysis, imaging, and clinical cell and molecular biology all benefit from rigorous computational biology analyses and modeling.They profit from the testable hypotheses that computational biology provides.These link the genetic material to the physiological cell state; however, in-depth understanding of-and predicting-the phenotypic relationship to genome landscapes also requires effective algorithms to unravel the outcomes of small-and large-scale alterations on DNA, RNA, and protein molecules.Diseases can be viewed as perturbed states of molecular systems.They can be of different types, including single-gene (monogenic) diseases and multifactorial diseases, such as cancers, immune system diseases, neurodegenerative diseases, cardiovascular diseases, and metabolic diseases.They may involve genetic alterations or be more complex.They can also be infectious diseases where interacting molecular networks of both pathogens and humans are involved, with the pathogen protein subverting the cell's machinery.The rapid development of next-generation methods for whole-genome, whole-exome, and targeted sequencing, complemented by earlier microarray technologies, including comparative

Source: PLoS Computational Biology

DOI: 10.1371/journal.pcbi.1005043

Authors: Rachel Karchin, Ruth Nussinov