"Since sequencing the human genome in the early 2000s, researchers have been determined to define the genetic pathways that regulate cellular activity or lead to disease. With the recent advent of Chromosome Conformation Capture (3C) technologies, the ability to observe chromatin’s three-dimensional (3D) structure became a possibility. It quickly became apparent that the genome is not regulated in one-dimension, but in 3D where chromatin loops are formed between an enhancer(s) and promoter to regulate a gene’s transcription. While 3C technology is quite useful, most protocols are limited in their resolution and availability across cell types and genomes. This limited resolution is a common concern for many technologies that study the regulation of genomes, such as Chromatin Immunoprecipitation (ChIP), and typically results from low-coverage sequencing. The objective of this thesis is to develop computational and biochemical methodologies that provide accurate, high-resolution genomic data for deciphering the organization and regulation of genomes. The first contribution in this thesis is Hi-C Interaction Frequency Inference (HIFI), a collection of density estimation algorithms for High-throughput 3C (Hi-C) data. Hi-C is a particularly useful 3C technology that identifies chromatin contacts genome-wide. HIFI allows Hi-C data to be analyzed at the highest possible resolution (restriction fragments) while providing the most accurate estimation of chromatin contact frequency when compared to other techniques in the field. The higher resolution afforded by HIFI has lead to the discovery of a potential role for active promoters and enhancers at the boundaries of Topologically Associating Domains (TADs). Next, we developed machine learning approaches to predict chromatin interaction frequencies from the reference genome sequence alone. While some machine learning work has been done to predict Hi-C data, all these models rely on biochemical input to make their predictions, which makes them impossible to use in cases where this data is unavailable (e.g., computationally inferred ancestral genomes). By limiting model input to features derived from sequence only, their predictions enable us to identify sequence determinants of 3D genome organization. Finally, we present a targeted and affordable ChIP methodology, called ‘Carbon Copy-ChIP’ (2C-ChIP), that continues our foray into high-resolution chromatin assays. 2C-ChIP provides quantifiable measures of bound protein across the genome at a cost that makes it very attractive for studies involving multiple experimental conditions (e.g., drug design). We also describe a computational tool for processing 2C-ChIP products called the Ligation-mediated Amplified, Multiplexed Paired-end Sequence (LAMPS) analysis pipeline.Taken together, the work in this thesis provides new ways to study genome function and organization affordably and at high resolution"--

This book sheds new light on the current state of knowledge concerning chromatin organization. Particular emphasis is given to the new imaging potential offered by super-resolution microscopy, which allows DNA imaging with a very high labeling density. From the early work on chromosomes by Walther Flemming in the nineteenth century to recent advances in genomics, the history of chromatin research now spans more than a century. The various milestones, such as the discovery of the double helix structure, the sequencing of the human genome, and the recent description of the genome in 3D space, show that understanding chromatin and chromosome function requires a clear understanding of its structure. Presenting cutting-edge data from super-resolution single molecule microscopy, the book demonstrates that chromatin manifests several levels of folding, from nucleosomes to chromosomes. Chromatin domains emerge as a new fundamental building block of chromatin architecture, with functions possibly related to gene regulation. A detailed description of chromatin folding in the pachytene stage of meiosis serves as a model for exploring this functionality, showing the apparent interplay between structure, function, and epigenetic regulation. Lastly, the book discusses possible new avenues of innovation to describe chromatin’s organization and functions. Gathering essential insights on chromatin architecture, the book offers students an introduction to microscopy and its application to chromatin organization, while also providing advanced readers with new ideas for future research.

This volume details a comprehensive set of methods and tools for Hi-C data processing, analysis, and interpretation. Chapters cover applications of Hi-C to address a variety of biological problems, with a specific focus on state-of-the-art computational procedures adopted for the data analysis. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Hi-C Data Analysis: Methods and Protocols aims to help computational and molecular biologists working in the field of chromatin 3D architecture and transcription regulation.

"The organization of DNA in the nucleus of a cell has long been known to play an important role in processes such as DNA replication and repair, and the regulation of gene expression. Recent advances in microarray and high-throughput sequencing technologies have enabled the creation of novel techniques for measuring certain aspects of the three-dimensional conformation of chromatin in vivo. The data generated by these methods contain both structural information and noise from the experimental procedures. Methods for modeling and analyzing these data to infer three-dimensional chromatin structure will constitute invaluable tools in the discovery of the mechanism by which chromatin structure is mediated. The overall objective of my thesis is to develop robust three-dimensional computational models of DNA and to analyze these data to gain biological insight into the role of chromatin structure on cellular processes. This thesis presents three main results, firstly, a novel computational modeling and analysis approach for the inference of three-dimensional structure from chromatin conformation capture carbon copy (5C) and Hi-C data. Our method named MCMC5C is based on Markov chain Monte Carlo sampling and can generate representative ensembles of three-dimensional models from noisy experimental data. Secondly, our investigation of the relationship between chromatin structure and gene expression during cellular differentiation shows that chromatin architecture is a dynamic structure which adopts an open conformation for actively transcribed genes and a condensed conformation for repressed genes. And thirdly, we developed a support vector machine classifier from 5C data and demonstrate a proof-of-concept that chromatin conformation signatures could be used to discriminate between human acute lymphoid and myeloid leukemias." --

The proliferation of massive data sets brings with it a series of special computational challenges. This "data avalanche" arises in a wide range of scientific and commercial applications. With advances in computer and information technologies, many of these challenges are beginning to be addressed by diverse inter-disciplinary groups, that indude computer scientists, mathematicians, statisticians and engineers, working in dose cooperation with application domain experts. High profile applications indude astrophysics, bio-technology, demographics, finance, geographi cal information systems, government, medicine, telecommunications, the environment and the internet. John R. Tucker of the Board on Mathe matical Seiences has stated: "My interest in this problern (Massive Data Sets) isthat I see it as the rnost irnportant cross-cutting problern for the rnathernatical sciences in practical problern solving for the next decade, because it is so pervasive. " The Handbook of Massive Data Sets is comprised of articles writ ten by experts on selected topics that deal with some major aspect of massive data sets. It contains chapters on information retrieval both in the internet and in the traditional sense, web crawlers, massive graphs, string processing, data compression, dustering methods, wavelets, op timization, external memory algorithms and data structures, the US national duster project, high performance computing, data warehouses, data cubes, semi-structured data, data squashing, data quality, billing in the large, fraud detection, and data processing in astrophysics, air pollution, biomolecular data, earth observation and the environment.

HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, performs quality controls and generates intra- and inter-chromosomal contact maps. It includes a fast implementation of the iterative correction method and is based on a memory-efficient data format for Hi-C contact maps. In addition, HiC-Pro can use phased genotype data to build allele-specific contact maps. We applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time. Source code and documentation are available at http://github.com/nservant/HiC-Pro.

One of the holy grails in biology is the ability to predict functional characteristics from an organism's genetic sequence. Despite decades of research since the first sequencing of an organism in 1995, scientists still do not understand exactly how the information in genes is converted into an organism's phenotype, its physical characteristics. Functional genomics attempts to make use of the vast wealth of data from "-omics" screens and projects to describe gene and protein functions and interactions. A February 2020 workshop was held to determine research needs to advance the field of functional genomics over the next 10-20 years. Speakers and participants discussed goals, strategies, and technical needs to allow functional genomics to contribute to the advancement of basic knowledge and its applications that would benefit society. This publication summarizes the presentations and discussions from the workshop.