Bioinformatics framework for genotyping microarray data analysis

Functional genomics is a flourishing science enabled by recent technological breakthroughs in high-throughput instrumentation and microarray data analysis. Genotyping microarrays establish the genotypes of DNA sequences containing single nucleotide polymorphisms (SNPs), and can help biologists probe the functions of different genes and/or construct complex gene interaction networks. The enormous amount of data from these experiments makes it infeasible to perform manual processing to obtain accurate and reliable results in daily routines. Advanced algorithms as well as an integrated software toolkit are needed to help perform reliable and fast data analysis. The author developed a MatlabTM based software package, called TIMDA (a Toolkit for Integrated Genotyping Microarray Data Analysis), for fully automatic, accurate and reliable genotyping microarray data analysis. The author also developed new algorithms for image processing and genotype-calling. The modular design of TIMDA allows satisfactory extensibility and maintainability. TIMDA is open source (URL: http://timda.SF.net and can be easily customized by users to meet their particular needs. The quality and reproducibility of results in image processing and genotype-calling and the ease of customization indicate that TIMDA is a useful package for genomics research

Dual-spectral interferometric sensor for quantitative study of protein-DNA interactions

Thesis (Ph.D.)--Boston UniversityThe maintenance and functions of the genome are facilitated by DNA-binding proteins, whose specific binding mechanisms are not yet fully understood. Recently, it was discovered that the recognition and capture ofDNA conformational flexibility and deformation by DNA-binding proteins serve as an indirect readout mechanism for specific recognition and facilitate important cellular functions. Various biophysical techniques have been employed to elucidate this conformational specificity of protein-DNA interactions. These techniques are not sufficiently high-throughput to perform systematic investigation ofvarious protein-DNA complexes and their functions. Microarray-based high-throughput methods enable large-scale and comprehensive evaluation of the binding affmities of protein-DNA interactions, but do not provide conformational information. In this dissertation, we developed a tool that enables high-throughput quantification of both conformational specificity and binding affinity of protein-DNA interactions. Our approach is to combine quantitative detection of DNA conformational change and protein-DNA binding in a DNA microarray format. The DNA conformational change is measured by spectral self-interference fluorescence microscopy that determines surface-immobilized DNA conformation by measuring axial height offluorophores tagged to specific nucleotides. The amount of bound protein and DNA are measured by white light reflectance spectroscopy that quantifies molecular surface densities by measuring bioniolecule layer thicknesses. By implementing a dual-spectral imaging configuration, we can perform the two independent interferometric measurements in parallel using two separate spectral bandwidths. [TRUNCATED

Dual-spectral interferometric sensor for quantitative study of protein-DNA interactions

Thesis (Ph.D.)--Boston UniversityThe maintenance and functions of the genome are facilitated by DNA-binding proteins, whose specific binding mechanisms are not yet fully understood. Recently, it was discovered that the recognition and capture ofDNA conformational flexibility and deformation by DNA-binding proteins serve as an indirect readout mechanism for specific recognition and facilitate important cellular functions. Various biophysical techniques have been employed to elucidate this conformational specificity of protein-DNA interactions. These techniques are not sufficiently high-throughput to perform systematic investigation ofvarious protein-DNA complexes and their functions. Microarray-based high-throughput methods enable large-scale and comprehensive evaluation of the binding affmities of protein-DNA interactions, but do not provide conformational information. In this dissertation, we developed a tool that enables high-throughput quantification of both conformational specificity and binding affinity of protein-DNA interactions. Our approach is to combine quantitative detection of DNA conformational change and protein-DNA binding in a DNA microarray format. The DNA conformational change is measured by spectral self-interference fluorescence microscopy that determines surface-immobilized DNA conformation by measuring axial height offluorophores tagged to specific nucleotides. The amount of bound protein and DNA are measured by white light reflectance spectroscopy that quantifies molecular surface densities by measuring bioniolecule layer thicknesses. By implementing a dual-spectral imaging configuration, we can perform the two independent interferometric measurements in parallel using two separate spectral bandwidths. [TRUNCATED

Simulations in statistical physics and biology: some applications

One of the most active areas of physics in the last decades has been that of critical phenomena, and Monte Carlo simulations have played an important role as a guide for the validation and prediction of system properties close to the critical points. The kind of phase transitions occurring for the Betts lattice (lattice constructed removing 1/7 of the sites from the triangular lattice) have been studied before with the Potts model for the values q=3, ferromagnetic and antiferromagnetic regime. Here, we add up to this research line the ferromagnetic case for q=4 and 5. In the first case, the critical exponents are estimated for the second order transition, whereas for the latter case the histogram method is applied for the occurring first order transition. Additionally, Domany's Monte Carlo based clustering technique mainly used to group genes similar in their expression levels is reviewed. Finally, a control theory tool --an adaptive observer-- is applied to estimate the exponent parameter involved in the well-known Gompertz curve. By treating all these subjects our aim is to stress the importance of cooperation between distinct disciplines in addressing the complex problems arising in biology. Contents: Chapter 1 - Monte Carlo simulations in stat. physics; Chapter 2: MC simulations in biology; Chapter 3: Gompertz equationComment: 82 pages, 33 figures, 4 tables, somewhat reduced version of the M.Sc. thesis defended in Jan. 2006 at IPICyT, San Luis Potosi, Mx. (Supervisers: Drs. R. Lopez-Sandoval and H.C. Rosu). Last sections 3.3 and 3.4 can be found at http://lanl.arxiv.org/abs/physics/041108

Whole-transciptome analysis of [psi+] budding yeast via cDNA microarrays

Introduction: Prions of yeast present a novel analytical challenge in terms of both initial characterization and in vitro manipulation as models for human disease research. Presently, few robust analysis strategies have been successfully implemented which enable the efficient study of prion behavior in vivo. This study sought to evaluate the utilization of conventional dual-channel cDNA microarrays for the surveillance of transcriptomic regulation patterns by the [PSI+] yeast prion relative to an identical prion deficient yeast variant, [psi-]. Methods: A data analysis and normalization workflow strategy was developed and applied to cDNA array images, yielded quality-regulated expression ratios for a subset of genes exhibiting statistical congruence across multiple experimental repetitions and nested hybridization events. The significant gene list was analyzed using classical analytical approaches including several clustering-based methods and singular value decomposition. To add biological meaning to the differential expression data in hand, functional annotation using the Gene Ontology as well as several pathway-mapping approaches was conducted. Finally, the expression patterns observed were queried against all publicly curated microarray data performed using S. cerevisiae in order to discover similar expression behavior across a vast array of experimental conditions. Results: These data collectively implicate a low-level of overall genomic regulation as a result of the [PSI+] state, where the maximum statistically significant degree of differential expression was less than ±1 Log2(FC) in all cases. Notwithstanding, the [PSI+] differential expression was localized to several specific classes of structural elements and cellular functions, implying under homeostatic conditions significant up or down regulation is likely unnecessary but possible in those specific systems if environmental conditions warranted. As a result of these findings additional work pertaining to this system should include controlled insult to both yeast variants of differing environmental properties to promote a potential [PSI+] regulatory response coupled with co-surveillance of these conditions using transcriptomic and proteomic analysis methodologies

Tools for single cell proteomics

Despite recent advances that offer control of single cells, in terms of manipulation and sorting and the ability to measure gene expression, the need to measure protein copy number remains unmet. Measuring protein copy number in single cells and related quantities such as levels of phosphorylation and protein-protein interaction is the basis of single cell proteomics. A technology platform to undertake the analysis of protein copy number from single cells has been developed. The approach described is ‘all-optical’ whereby single cells are manipulated into separate analysis chambers using an optical trap; single cells are lysed by mechanical shearing caused by laser-induced microcavitation; and the protein released from a single cell is measured by total internal reflection microscopy as it is bound to micro-printed antibody spots within the device. The platform was tested using GFP transfected cells and the relative precision of the measurement method was determined to be 88%. Single cell measurements were also made on a breast cancer cell line to measure the relative levels of unlabelled human tumour suppressor protein p53 using a chip incorporating an antibody sandwich assay format. This demonstrates the ability count protein copy number from single cells in a manner which could be applied in principle to any set of proteins and for any cell type without the need for genetic engineering. Metabolism can undergo alteration in diseases such as cancer and heart failure and also as cells differentiate during development. In order to assess how it may inform a proteomic measurement, multidimensional two-photon fluorescence metabolic imaging is conducted on a cultured cancer cell line, primary adult rat cardiomyocytes and human embryonic stem cells. By measuring the parameters of fluorescence such as intensity and lifetime of the autofluorescent metabolic co-factors NADH and FAD, it was found to be possible to contrast cells under various conditions and metabolic stimuli. In particular, human embryonic stem cells were able to be contrasted at 3 stages of development as they underwent differentiation into embryonic stem cell derived cardiomyocytes. Metabolic imaging provides a non-destructive method to monitor cellular metabolic activity with high resolution. This is complimentary to the single cell proteomic platform and the convergence of both techniques holds promise in future investigations into how metabolism influences cell function and the proteome in development and disease

Multi-scale approaches for the statistical analysis of microarray data (with an application to 3D vesicle tracking)

The recent developments in experimental methods for gene data analysis, called microarrays, provide the possibility of interrogating changes in the expression of a vast number of genes in cell or tissue cultures and thus in depth exploration of disease conditions. As part of an ongoing program of research in Guy A. Rutter (G.A.R.) laboratory, Department of Biochemistry, University of Bristol, UK, with support from the Welcome Trust, we study the impact of established and of potentially new methods to the statistical analysis of gene expression data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Time-series clustering of gene expression in irradiated and bystander fibroblasts: an application of FBPA clustering

<p>Abstract</p> <p>Background</p> <p>The radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation, but the signaling mechanisms between irradiated and non-irradiated bystander cells are not fully understood. In this study, we measured a time-series of gene expression after α-particle irradiation and applied the Feature Based Partitioning around medoids Algorithm (FBPA), a new clustering method suitable for sparse time series, to identify signaling modules that act in concert in the response to direct irradiation and bystander signaling. We compared our results with those of an alternate clustering method, Short Time series Expression Miner (STEM).</p> <p>Results</p> <p>While computational evaluations of both clustering results were similar, FBPA provided more biological insight. After irradiation, gene clusters were enriched for signal transduction, cell cycle/cell death and inflammation/immunity processes; but only FBPA separated clusters by function. In bystanders, gene clusters were enriched for cell communication/motility, signal transduction and inflammation processes; but biological functions did not separate as clearly with either clustering method as they did in irradiated samples. Network analysis confirmed p53 and NF-κB transcription factor-regulated gene clusters in irradiated and bystander cells and suggested novel regulators, such as KDM5B/JARID1B (lysine (K)-specific demethylase 5B) and HDACs (histone deacetylases), which could epigenetically coordinate gene expression after irradiation.</p> <p>Conclusions</p> <p>In this study, we have shown that a new time series clustering method, FBPA, can provide new leads to the mechanisms regulating the dynamic cellular response to radiation. The findings implicate epigenetic control of gene expression in addition to transcription factor networks.</p