Thesis advisor: Gabor T. Marth / During the last few years, numerous new sequencing technologies have emerged that require tools that can process large amounts of read data quickly and accurately. Regardless of the downstream methods used, reference-guided aligners are at the heart of all next-generation analysis studies. I have developed a general reference-guided aligner, MOSAIK, to support all current sequencing technologies (Roche 454, Illumina, Applied Biosystems SOLiD, Helicos, and Sanger capillary). The calibrated alignment qualities calculated by MOSAIK allow the user to fine-tune the alignment accuracy for a given study. MOSAIK is a highly configurable and easy-to-use suite of alignment tools that is used in hundreds of labs worldwide. MOSAIK is an integral part of our genetic variant discovery pipeline. From SNP and short-INDEL discovery to structural variation discovery, alignment accuracy is an essential requirement and enables our downstream analyses to provide accurate calls. In this thesis, I present three major studies that were formative during the development of MOSAIK and our analysis pipeline. In addition, I present a novel algorithm that identifies mobile element insertions (non-LTR retrotransposons) in the human genome using split-read alignments in MOSAIK. This algorithm has a low false discovery rate (4.4 %) and enabled our group to be the first to determine the number of mobile elements that differentially occur between any two individuals. / Thesis (PhD) — Boston College, 2010. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

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A cell, the building block of all life, stores a plethora of information in its genome, epigenome, and transcriptome which needs to be analyzed via various Omic studies. The heterogeneity in a seemingly similar group of cells is an important factor to consider and it could lead us to better understand processes such as cancer development and resistance to treatment, fetal development, and immune response. There is an ever growing demand to be able to develop more sensitive, accurate and robust ways to study Omic information and to analyze subtle biological variation between samples even with limited starting material obtained from a single cell. Microfluidics has opened up new and exciting possibilities that have revolutionized how we study and manipulate the contents of the cell like the DNA, RNA, proteins, etc. Microfluidics in conjunction with Next Gen Sequencing has provided ground-breaking capabilities for handling small sample volumes and has also provided scope for automation and multiplexing. In this thesis, we discuss a number of platforms for developing low-input or single cell Omic technologies. The first part talks about the development of a novel microfluidic platform to carry out single-cell RNA-sequencing in a one-pot method with a diffusion-based reagent swapping scheme. This platform helps to overcome the limitations of conventional microfluidic RNA seq methods reported in literature that use complicated multiple-chambered devices. It also provides good quality data that is comparable to state-of-the-art scRNA-seq methods while implementing a simpler device design that permits multiplexing. The second part talks about studying the transcriptome of innate leukocytes treated with varying levels of LPS and using RNA-seq to observe how innate immune cells undergo epigenetic reprogramming to develop phenotypes of memory cells. The third part discusses a low-cost alternative to produce tn5 enzyme which low-cost NGS studies. And finally, we discuss a microfluidic approach to carrying out low-input epigenomic studies for studying transcription factors. Today, single-cell or low-input Omic studies are rapidly moving into the clinical setting to enable studies of patient samples for personalized medicine. Our approaches and platforms will no doubt be important for transcriptomic and epigenomic studies of scarce cell samples under such settings. / Doctor of Philosophy / This is the era of personalized medicine which means that we are no longer looking at one-size-fits-all therapies. We are rather focused on finding therapies that are tailormade to every individual’s personal needs. This has become more and more essential in the context of serious diseases like cancer where therapies have a lot of side-effects. To provide tailor-made therapy to patients, it is important to know how each patient is different from another. This difference can be found from studying how the individual is unique or different at the cellular level i.e. by looking into the contents of the cell like DNA, RNA, and chromatin. In this thesis, we discussed a number of projects which we can contribute to advancement in this field of personalized medicine. Our first project, MID-RNA-seq offers a new platform for studying the information contained in the RNA of a single cell. This platform has enough potential to be scaled up and automated into an excellent platform for studying the RNA of rare or limited patient samples. The second project discussed in this thesis involves studying the RNA of innate immune cells which defend our bodies against pathogens. The RNA data that we have unearthed in this project provides an immense scope for understanding innate immunity. This data provides our biologist collaborators the scope to test various pathways in innate immune cells and their roles in innate immune modulation. Our third project discusses a method to produce an enzyme called ‘Tn5’ which is necessary for studying the sequence of DNA. This enzyme which is commercially available has a very high cost associated with it but because we produced it in the lab, we were able to greatly reduce costs. The fourth project discussed involves the study of chromatin structure in cells and enables us to understand how our lifestyle choices change the expression or repression of genes in the cell, a study called epigenetics. The findings of this study would enable us to study epigenomic profiles from limited patient samples. Overall, our projects have enabled us to understand the information from cells especially when we have limited cell numbers. Once we have all this information we can compare how each patient is different from others. The future brings us closer to putting this into clinical practice and assigning different therapies to patients based on such data.

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There has been tremendous advancement in sequencing technologies; the rate at which sequencing data can be generated has increased multifold while the cost of sequencing continues on a downward descent. Sequencing data provide novel insights into the ecological environment of microbes as well as human health and disease status but challenge investigators with a variety of computational issues. This thesis focuses on three common problems in the analysis of high-throughput data. The goals of the first project are to (1) develop a statistical framework and a complete software pipeline for metagenomics that identifies microbes to the strain level and thus facilitating a personalized drug treatment targeting the strain; and (2) estimate the relative content of microbes in a sample as accurately and as quickly as possible. The second project focuses on the analysis of the microbiome variation across multiple samples. Studying the variation of microbiomes under different conditions within an organism or environment is the key to diagnosing diseases and providing personalized treatments. The goals are to (1) identify various statistical diversity measures; (2) develop confidence regions for the relative abundance estimates; (3) perform multi-dimensional and differential expression analysis; and (4) develop a complete pipeline for multi-sample microbiome analysis. The third project is focused on batch effect analysis. When analyzing high dimensional data, non-biological experimental variation or “batch effects” confound the true associations between the conditions of interest and the outcome variable. Batch effects exist even after normalization. Hence, unless the batch effects are identified and corrected, any attempts for downstream analyses, will likely be error prone and may lead to false positive results. The goals are to (1) analyze the effect of correlation of the batch adjusted data and develop new techniques to account for correlation in two step hypothesis testing approach; (2) develop a software pipeline to identify whether batch effects are present in the data and adjust for batch effects in a suitable way. In summary, we developed software pipelines called PathoScope, PathoStat and BatchQC as part of these projects and validated our techniques using simulation and real data sets.

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Understanding others is a deeply human urge basic in our existential quest. It requires knowing where someone has come from and where they sit amongst peers. Phylogenetic analysis and genome wide association studies seek to tell us where we’ve come from and where we are relative to one another through evolutionary history and genetic makeup. Current methods do not address the computational complexity caused by new forms of genomic data, namely long-read DNA sequencing and increased abundances of assembled genomes, that are becoming evermore abundant. To address this, we explore specialized data structures for storing and comparing genomic information. This work resulted in the creation of novel data structures for storing multiple genomes that can be used for identifying structural variations and other types of polymorphisms. Using these methods we illuminate the genetic history of organisms in our efforts to understand the world around us.