Browsing by Subject "Biology, Bioinformatics (0715)"
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Item Developing a Bioinformatics Pipeline to Assess the Potential Functional Impact of Novel Protein Isoforms(2020-04-20) Klein, Alyssa; Darby, Miranda; Hood College Biology; Craig Laufer; Georgette Jones; Hood College Bioinformatics ProgramWhile we know the sequence of the nucleotides that make up the DNA of the human genome, the process of annotating those nucleotides according to the transcripts that originate from them remains incomplete. Novel transcripts continue to be identified, and so methods must be devised to characterize these novel transcripts and prioritize them for future study by assessing potential hallmarks of function. One of the potential hallmarks of function is presence of an open reading frame with potential to produce a protein isoform that is not yet annotated. Based on the sequence of the novel protein isoform, an initial assessment of the potential functional impact of expression of the novel protein can be made: 1) Identification of the loss and/or gain of protein domains in the novel isoform versus the current annotated form of the protein. 2) Analysis of proteins that have lost and/or gained functional domains by generating 3D models of the annotated proteins and novel protein isoforms to facilitate potential understanding of functional impact. 3) Differential expression analysis of the putative exons at the RNA level to investigate disease-specificity of expression and potential changes in expression of the novel isoform in disease.Item Developing a Library Collection in Bioinformatics: Support for an Evolving Profession(IGI Global, 2013) Martin, VictoriaThis chapter provides guidelines for developing a university library collection for bioinformatics programs. The chapter discusses current research and scholarly communication trends in bioinformatics and their impact on information needs and information seeking behavior of bioinformaticians and, consequently, on collection development. It also discusses the criteria for making collection development decisions that are largely influenced by the interdisciplinary nature of the field. The types of information resources most frequently used by bioinformaticians are described, specific resources are suggested, and creative options aimed at finding ways for a bioinformatics library collection to expand in the digital era are explored. The author draws on literature in bioinformatics and the library and information sciences as well as on her ten years of experience providing bioinformatics user services at George Mason University. The chapter is geared towards practicing librarians who are charged with developing a collection for bioinformatics academic programs as well as future librarians taking courses on collection development and academic librarianship.Item Empirical Bayes Methods for Proteomics(2008-02-05) Li, Feng; Seillier-Moiseiwitsch, Francoise; Rukhin, Andrew; Mathematics and Statistics; StatisticsProteomics is the science that deals with high-throughput analysis of proteins. The study of proteins relies on efficient protein separation technique. 2D PAGE is a powerful technique for separating complex mixtures of proteins, where thousands of proteins are separated and measured simultaneously. The analysis of 2D PAGE images needs efficient methods able to cope with large-scale dataset. Empirical Bayes methods have been shown to be very efficient at combining information across dimensions of high-dimensional data. In the first part of this dissertation, the construction of empirical Bayes confidence intervals under different model assumptions is studied. Numerical simulations are conducted to demonstrate the satisfactory performance of the proposed methods. In the second part, a new comprehensive procedure for statistical analysis of 2D PAGE images is proposed, including protein quantification, normalization and statistical analysis. It reduces the dimension of the data. It also bypasses the current bottleneck in the analysis of 2D PAGE images in that it does not require spot matching. A strategy for multiple hypothesis testing based on multivariate analysis combined with empirical Bayes methods is formulated and applied to the differential analysis of 2D PAGE images. The new methodologies are implemented in a custom software package.Item Why these 20 amino acids?(2008-02-05) Lu, Yi; Freeland, Stephen J.; Biological Sciences; Biological SciencesNearly all living organisms use the same set of 20 amino acids to make proteins. However, substantial evidence indicates that this standard alphabet of amino acids is a mere subset of what was available to life during early evolution. A common qualitative explanation claims that the diversity of encoded amino acids has increased during evolution, enabling diverse protein structures and functions to be made from them. Therefore, I proposed a testable baseline hypothesis that the current amino acid alphabet comprises a subset that maximizes the diversity of certain key properties, which is responsible for protein diversity. To build a quantitative framework, I first investigated the reliability of using computational programs in predicting three fundamental amino acid properties. These properties were size, charge and hydrophobicity, as measured by van der Waals volume, isoelectric point, and logP, respectively. My results demonstrated the plausibility of estimating amino acid physiochemical properties with fast and reliable computational approaches, which can thus supplement or even replace time-consuming and costly experimental determinations. I then tested quantitative formulations of qualitative explanations for the origin of the amino acid alphabet, such as the idea that biochemical diversity of the amino acid alphabet was somehow optimized by natural selection. My research first focused on investigating whether the group of eight standard amino acids (i.e., those used in genetic coding) that are routinely produced in abiotic syntheses is in some way a non-random sample of the 66 abiotic amino acids that have been found in the Murchison meteorite. I compared statistical variance (a diversity measure) of the eight ""standard"" amino acids to alternative samples of eight amino acids chosen at random from the larger set of those that are prebiotically plausible. My results showed that when factoring in amino acid abundance, the eight standard amino acids are more diverse than most of the random sets in terms of logP (hydrophobicity) and pI (charge). These results are consistent with, though not strongly supportive of, the idea that amino acids may have been non-randomly ""chosen"" during the earliest stage of amino acid alphabet evolution. Next, I investigated the change of amino acid diversity along the formation of the genetic code. Specifically, I evaluated two widely discussed models of genetic code development: ""sequential incorporation"" (by which the genetic code encoded proteinaceous amino acids one at a time, gradually leading to the standard alphabet of 20) and ""ambiguity reduction"" (by which all 20 proteinaceous amino acids were present from the start, but moved from a state of non-specific tRNA charging to later states of increasingly specific charging). Still using statistical variance as the diversity measure, I asked how each model relates to the idea that natural selection exerted a pressure to increase the biochemical diversity of amino acids during code evolution. My results show that the ""ambiguity reduction"" model is more straightforwardly consistent with the widespread idea that natural selection acted to produce a diverse alphabet of amino acids for genetic coding. To make the amino acid data that derived from my research freely available for the scientific community and the public, I have also developed a novel web-resource of information pertaining to 387 amino acids. The database includes general information about each amino acid, the sources from which this amino acid is known, the three fundamental biophysical properties, and several analysis and visualization tools. Additionally, to illustrate the types of exploration that our database can support, I also conducted two simple Quantitative Structure-Activity Relationship (QSAR) studies of peptides that include non-standard amino acids. Each demonstrated the utility of the three fundamental biophysical properties on which our database focuses and the speed and ease with which meaningful bioactivity results (specifically potentiating activity, bitterness of taste) can be explored. In summary, my dissertation work not only deepened our understanding of the formation of the amino acid alphabet but also provided quantitative framework for origin-of-life studies and protein engineering research.