Current Study Designs, Methods, and Future Directions of Genetic Association Mapping

Rapid progress in genotyping technologies, including the scaling up of assay technologies to genome-wide levels and next generation sequencing, has motivated a burst in methods development and application to detect genotype-phenotype associations in a wide array of diseases and other phenotypes. In this chapter, the authors review the study design and genotyping options that are used in association mapping, along with the appropriate methods to perform mapping within these study designs. The authors discuss both candidate gene and genome-wide studies, focused on DNA level variation. Quality control, genotyping technologies, and single-SNP and multiple-SNP analyses have facilitated the successes in identifying numerous loci influence disease risk. However, variants identified have generally explained only a small fraction of the heritable component of disease risk. The authors discuss emerging trends and future directions in performing analysis for rare variants to detect these variants that predict these traits with more complex etiologies.

Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics

Feature Extraction (FE) is a difficult task when the number of features is much larger than the number of samples, although that is a typical situation when biological (big) data is analyzed. This is especially true when FE is stable, independent of the samples considered (stable FE), and is often required. However, the stability of FE has not been considered seriously. In this chapter, the authors demonstrate that Principal Component Analysis (PCA)-based unsupervised FE functions as stable FE. Three bioinformatics applications of PCA-based unsupervised FE—detection of aberrant DNA methylation associated with diseases, biomarker identification using circulating microRNA, and proteomic analysis of bacterial culturing processes—are discussed.

The Benefits of Big Data Analytics in the Healthcare Sector

The benefits of big data analytics in the healthcare sector are assumed to be substantial, and early proponents have been very enthusiastic (Chen, Chiang, & Storey, 2012), but little research has been carried out to confirm just what those benefits are, and to whom they accrue (Bollier, 2010). This chapter presents an overview of existing literature that demonstrates quantifiable, measurable benefits of big data analytics, confirmed by researchers across a variety of healthcare disciplines. The chapter examines aspects of clinical operations in healthcare including Cost Effectiveness Research (CER), Clinical Decision Support Systems (CDS), Remote Patient Monitoring (RPM), Personalized Medicine (PM), as well as several public health initiatives. This examination is in the context of searching for the benefits described resulting from the deployment of big data analytics. Results indicate the principle benefits are delivered in terms of improved outcomes for patients and lower costs for healthcare providers.

Personalized Disease Phenotypes from Massive OMICs Data

Application of new high-throughput technologies in molecular medicine collects massive data for hundreds to thousands of persons in large cohort studies by characterizing the phenotype of each individual on a personalized basis. The chapter aims at increasing our understanding of disease genesis and progression and to improve diagnosis and treatment. New methods are needed to handle such “big data.” Machine learning enables one to recognize and to visualize complex data patterns and to make decisions potentially relevant for diagnosis and treatment. The authors address these tasks by applying the method of self-organizing maps and present worked examples from different disease entities of the colon ranging from inflammation to cancer.

Bioinformatics-Driven Big Data Analytics in Microbial Research

With the advent of sophisticated and high-end molecular biological technologies, microbial research has observed tremendous boom. It has now become one of the most prominent sources for the generation of “big data.” This is made possible due to huge data coming from the experimental platforms like whole genome sequencing projects, microarray technologies, mapping of Single Nucleotide Polymorphisms (SNP), proteomics, metabolomics, and phenomics programs. For analysis, interpretation, comparison, storage, archival, and utilization of this wealth of information, bioinformatics has emerged as a massive platform to solve the problems of data management in microbial research. In present chapter, the authors present an account of “big data” resources spread across the microbial domain of research, the efforts that are being made to generate “big data,” computational resources facilitating analysis and interpretation, and future needs for huge biological data storage, interpretation, and management.

Analysis of Genomic Data in a Cloud Computing Environment

A new trend for data analysis in the life sciences is Cloud computing, enabling the analysis of large datasets in short time. This chapter introduces Big Data challenges in the genomic era and how Cloud computing can be one feasible approach for solving them. Technical and security issues are discussed and a case study where Clouds are successfully applied to resolve computational bottlenecks in the analysis of genomic data is presented. It is an intentional outcome of this chapter that Cloud computing is not essential for analyzing Big Data. Rather, it is argued that for the optimized utilization of IT, it is required to choose the best architecture for each use case, either by security requirements, financial goals, optimized runtime through parallelization, or the ability for easier collaboration and data sharing with business partners on shared resources.

Detection and Employment of Biological Sequence Motifs

Biological sequence motifs are short nucleotide or amino acid sequences that are biologically significant and are attractive to scientists because they are usually highly conserved and result in structural and regulatory implications. In this chapter, the authors show practical applications of these data, followed by a review of the algorithms, techniques, and tools. They address the nature of motifs and elucidate on several methods for de novo motif discovery, covering the algorithms based on Gibbs sampling, expectation maximization, Bayesian inference, covariance models, and discriminative learning. The authors present the tools and their requirements to weigh their individual benefits and challenges. Since interpretation of a large set of results can pose significant challenges, they discuss several methods for handling data that span from visualization to integration into pipelines and curated databases. Additionally, the authors show practical applications of these data with examples.

Intellectual Property Protection for Synthetic Biology, Including Bioinformatics and Computational Intelligence

Since the completion of the Human Genome Project, biologists have shifted their efforts from understanding biology to modifying it. Synthetic biology is a rapidly growing interdisciplinary field that includes developing and manufacturing synthetic nucleotide sequences, systems, genomes, and medical devices. Gaining patent protection represents an imperative and significant tool for business development in synthetic biology. Without IP protection, investors most likely will not commit necessary resources for progress. While there have been many important breakthroughs in biotechnology, recent case law rulings and legislative statutes have created obstacles for inventors to gain patent protection of novel synthetic biology inventions. These issues cause hesitation in license agreements and postpone creation of synthetic biology start-up companies. Nevertheless, inventors still can gain patent protection in many branches of synthetic biology. This chapter examines the issues, controversies, and problems associated with patent protection in synthetic biology. It then gives solutions, recommendations, and future directions for the field.

Perspectives on Data Integration in Human Complex Disease Analysis

The identification of causal or predictive variants/genes/mechanisms for disease-associated traits is characterized by “complex” networks of molecular phenotypes. Present technology and computer power allow building and processing large collections of these data types. However, the super-rapid data generation is counterweighted by a slow-pace for data integration methods development. Most currently available integrative analytic tools pertain to pairing omics data and focus on between-data source relationships, making strong assumptions about within-data source architectures. A limited number of initiatives exist aiming to find the most optimal ways to analyze multiple, possibly related, omics databases, and fully acknowledge the specific characteristics of each data type. A thorough understanding of the underlying assumptions of integrative methods is needed to draw sound conclusions afterwards. In this chapter, the authors discuss how the field of “integromics” has evolved and give pointers towards essential research developments in this context.

Large-Scale Regulatory Network Analysis from Microarray Data

The inference of gene networks from gene expression data is known as “reverse engineering.” Elucidating genetic networks from high-throughput microarray data in seed maturation and embryo formation in plants is crucial for storage and production of cereals for human beings. Delayed seed maturation and abnormal embryo formation during storage of cereal crops degrade the quality and quantity of food grains. In this chapter, the authors perform comparative gene analysis of results of different microarray experiments in different stages of embryogenesis in Arabidopsis thaliana, and to reconstruct Gene Networks (GNs) related to various stages of plant seed maturation using reverse engineering technique. They also biologically validate the results for developing embryogenesis network on Arabidopsis thaliana with GO and pathway enrichment analysis. The biological analysis shows that different genes are over-expressed during embryogenesis related with several KEGG metabolic pathways. The large-scale microarray datasets of Arabidopsis thaliana for these genes involved in embryogenesis have been analysed in seed biology. The chapter also reveals new insight into the gene functional modules obtained from the Arabidopsis gene correlation networks in this dataset.