Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

A TMA De-Arraying Method for High Throughput Biomarker Discovery in Tissue Research

Background: Tissue MicroArrays (TMAs) represent a potential high-throughput platform for the analysis and discovery of tissue biomarkers. As TMA slides are produced manually and subject to processing and sectioning artefacts, the layout of TMA cores on the final slide and subsequent digital scan (TMA digital slide) is often disturbed making it difficult to associate cores with their original position in the planned TMA map. Additionally, the individual cores can be greatly altered and contain numerous irregularities such as missing cores, grid rotation and stretching. These factors demand the development of a robust method for de-arraying TMAs which identifies each TMA core, and assigns them to their appropriate coordinates on the constructed TMA slide.

Methodology: This study presents a robust TMA de-arraying method consisting of three functional phases: TMA core segmentation, gridding and mapping. The segmentation of TMA cores uses a set of morphological operations to identify each TMA core. Gridding then utilises a Delaunay Triangulation based method to find the row and column indices of each TMA core. Finally, mapping correlates each TMA core from a high resolution TMA whole slide image with its name within a TMAMap.

Conclusion: This study describes a genuine robust TMA de-arraying algorithm for the rapid identification of TMA cores from digital slides. The result of this de-arraying algorithm allows the easy partition of each TMA core for further processing. Based on a test group of 19 TMA slides (3129 cores), 99.84% of cores were segmented successfully, 99.81% of cores were gridded correctly and 99.96% of cores were mapped with their correct names via TMAMaps. The gridding of TMA cores were also extensively tested using a set of 113 pseudo slide (13,536 cores) with a variety of irregular grid layouts including missing cores, rotation and stretching. 100% of the cores were gridded correctly.

An all-in-one dry chemistry immunoassay for the screening of coccidiostat nicarbazin in poultry eggs and liver

An automated immunoassay for the detection of nicarbazin residues in poultry eggs and liver was developed. The assay was based on a novel all-in-one dry chemistry concept and time-resolved fluorometry. The analyte specific antibody was immobilized into a single microtiter well and covered with an insulation layer, on top of which the label was dried in a small volume. The extracted sample was added automatically to the dry microtiter well, and the result was available within 18 min. Due to the rapidity and simplicity, the quantitative immunoassay could also be used as a high throughput screening method. The analytical limit of detection for the assay was calculated as 0.1 ng mL(-1) (n = 12) and the functional limit of detection as 3.2 ng g(-1) for egg (n = 6) and 11.3 ng g(-1) for liver (n = 6) samples. The sample recovery varied from 97.3 to 115.6%. Typically, the intra-assay variations were less than 10%, and interassay variations ranged between 8.1 and 13.6%.

Assessment of a multiplexing high throughput immunochemical SPR biosensor in measuring multiple proteins on a single biosensor chip

Multiplexed immunochemical detection platforms offer the potential to decrease labour demands, increase sample throughput and decrease overall time to result. A prototype four channel multiplexed high throughput surface plasmon resonance biosensor was previously developed, for the detection of food related contaminants. A study focused on determining the instruments performance characteristics was undertaken. This was followed by the development of a multiplexed assay for four high molecular weight proteins. The protein levels were simultaneously evaluated in serum samples of 10-week-old veal calves (n = 24) using multiple sample preparation methods. Each of the biosensor's four channels were shown to be independent of one another and produced multiplexed within run repeatability (n = 6) ranging from 2.0 to 6.7%CV, for the four tested proteins, whilst between run reproducibility (n = 4) ranged from 1.5 to 8.9%CV. Four calibration curves were successfully constructed before serum sample preparation was optimised for each protein. Multiplexed concentration analysis was successfully performed on four channels revealing that each proteins concentration was consistent across the twenty-four tested animals. Signal reproducibility (n > 19) on a further long term study revealed coefficient of variation ranging from 1.1% to 7.3% and showed that the multiplexed assay was stable for at least 480 cycles. These findings indicate that the performance characteristics fall within the range of previously published data for singleplex optical biosensors and that the multiplexing biosensor is fit-for-purpose for simultaneous concentration analysis in many different types of applications such as the multiplexed detection of markers of growth-promoter abuse and multiplexed detection of residues of concern in food safety. © 2013 Elsevier B.V.

Beaufort trout MicroPlex:A high-throughput multiplex platform comprising 38 informative microsatellite loci for use in resident and anadromous (sea trout) brown trout Salmo trutta genetic studies

A flexible panel consisting of 38 informative microsatellite markers for Salmo trutta is described. These markers were selected from a pool of over 150 candidate loci that can be readily amplified in four multiplex PCR groups but other permutations are also possible. The basic properties of each markers were assessed in six population samples from both the Burrishoole catchment, in the west of Ireland, and Lough Neagh, in Northern Ireland. A method to assess the relative utility of individual markers for the detection of population genetic structuring is also described. Given its flexibility, technical reliability and high degree of informativeness, the use of this panel of markers is advocated as a standard for S. trutta genetic studies. © 2013 The Authors. Journal of Fish Biology © 2013 The Fisheries Society of the British Isles.