Complex diseases are caused by a combination of genetic and environmental factors. Unraveling the molecular pathways from the genetic factors that affect a phenotype is always difficult, but in the case of complex diseases, this is further complicated since genetic factors in affected individuals might be different. Polycystic ovarian syndrome (PCOS) is an example of a complex disease with limited molecular information. Recently, PCOS molecular omics data have increasingly appeared in many publications. We conduct extensive bioinformatics analyses on the data and perform strong integration of experimental and computational biology to understand its complex biological systems in examining multiple interacting genes and their products. PCOS involves networks of genes, and to understand them, those networks must be mapped. This approach has emerged as powerful tools for studying complex diseases and been coined as network biology. Network biology encompasses wide range of network types including those based on physical interactions between and among cellular components and those baised on similarity among patients or diseases. Each of these offers distinct biological clues that may help scientists transform their cellular parts list into insights about complex diseases. This chapter will discuss some computational analysis aspects on the omics studies that have been conducted in PCOS.
Part of the book: Polycystic Ovarian Syndrome
This chapter describes the computational approach used in analyzing rice transcriptomics and genomics data to identify and annotate potential single nucleotide polymorphism (SNPs) as potential biomarker in the production of flavonoid. SNPs play a role in the accumulation of nutritional components (e.g. antioxidants), and flavonoid is one of them. However, the number of identified SNPs associated with flavonoid nutritional trait is still limited. We develop a knowledge-based bioinformatic workflow to search for specific SNPs and integration analysis on the SNPs and their co-expressed genes to investigate their influence on the gain/loss of functional genes that are involved in the production of flavonoids. Raw files obtained from the functional genomics studies can be analyzed in details to obtain a useful biological insight. Different tools, algorithms and databases are available to analyze the ontology, metabolic and pathway at the molecular level in order to observe the effects of gene and protein expression. The usage of different tools, algorithms and databases allows the integration, interpretation and the inference of analysis to provide better understanding of the biological meaning of the resutls. This chapter illustrates how to select and bring together several software to develop a specific bioinformatic workflow that processes and analyses omics data. The implementation of this bioinformatic workflow revealed the identification of potential flavonoid biosynthetic genes that can be used as guided-gene to screen the single nucleotide polymorphisms (SNPs) in the flavonoid biosynthetic genes from genome and transcriptomics data.
Part of the book: Recent Advances in Rice Research