Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further

Requirements of Modern Genome Browsers

Genome browsers are widely used tools for the visualization of a genome and related data. The demands placed on genome browsers due to the size, variety, and complexity of the data produced by modern biotechnology is increasing. These demands are poorly understood, and are not documented. Our study is establishing and documenting a clear set of requirements for genome browsers. Our study reviewed all widely used genome browsers, as well as notable research prototypes of genome browsers. This involved a review of the literature, executing typical uses of the genome browsers, program comprehension, reverse engineering, and code analysis. The key outcome of the study is a clear set of requirements in the form of a requirement document which conforms to the IEEE Std 830-1998 Standard of a Software Requirement Specification. This contains a domain model of concepts, the functional requirements as use cases, a definition of visualizations as metaphors, glyphs, or icons, formal specification of the system in Z notation and a specification of all widely used file formats. Genome browsers share a set of basic features like display, scroll, zoom, and search. However, they differ in their performance, maturity level and the implementation technologies. Our requirements also document the major non-functional requirements. The outcome of our study can be used in several ways: it can be used as a guide for future developers of Genome Browsers; it can form the basis of future enhancements of features in existing genome browsers; and it can motivate the invention of new algorithms, data structures, or file formats for implementations

Quantitative analysis of ChIP-seq signals and transcriptomes

Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) is commonly used to analyze the in vivo interactions between proteins and DNA across the genome. Analysis of ChIP-seq data has largely focused on detection of presence of peaks that represent DNA regions enriched by chromatin immunoprecipitation, i.e., the DNA loci bound by the immunoprecipitated proteins. To properly interpret ChIP-seq data, capturing its quantitative features is imperative. In this dissertation, we develop a statistically robust pipeline, named as ChIP-seq Signal Quantifier (CSSQ), that provides normalized ChIP-seq data, enabling detection and quantification of differential binding (DBs) across the genome, allowing calculable comparisons among multiple ChIP-seq datasets on predefined regions. Using both experimental datasets and computational simulations, we demonstrate the superior performance of CSSQ against existing tools as evidenced by its high sensitivity and specificity, and low false discovery rate. CSSQ is applicable to ChIP-seq datasets with varied signal to noise ratio, significantly improving the accuracy of comparison of ChIP-seq datasets from different experiments, serving as a powerful pipeline suited to garner quantitative information from ChIP-seq datasets for deciphering epigenomes. RNA-seq has become the leading choice for transcriptome analysis. Using RNA-seq and bioinformatics analysis, we characterize gene expression profiles and key cellular processes during stem cell differentiation and cell responses upon nanoparticle exposure. Collectively, these studies show that transcriptome analysis is a powerful tool for characterization and understanding cellular mechanisms.Ph.D