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Articles 31 - 41 of 41
Full-Text Articles in Life Sciences
Microbial Nad Metabolism: Lessons From Comparative Genomics, Francesca Gazzaniga, Rebecca Stebbins, Sheila Z. Chang, Mark A. Mcpeek, Charles Brenner
Microbial Nad Metabolism: Lessons From Comparative Genomics, Francesca Gazzaniga, Rebecca Stebbins, Sheila Z. Chang, Mark A. Mcpeek, Charles Brenner
Dartmouth Scholarship
NAD is a coenzyme for redox reactions and a substrate of NAD-consuming enzymes, including ADP-ribose transferases, Sir2-related protein lysine deacetylases, and bacterial DNA ligases. Microorganisms that synthesize NAD from as few as one to as many as five of the six identified biosynthetic precursors have been identified. De novo NAD synthesis from aspartate or tryptophan is neither universal nor strictly aerobic. Salvage NAD synthesis from nicotinamide, nicotinic acid, nicotinamide riboside, and nicotinic acid riboside occurs via modules of different genes. Nicotinamide salvage genes nadV and pncA, found in distinct bacteria, appear to have spread throughout the tree of life …
Lipid Uptake And Metabolism In The Parasitic Protozoan Giardia Lamblia., Mayte Yichoy
Lipid Uptake And Metabolism In The Parasitic Protozoan Giardia Lamblia., Mayte Yichoy
Open Access Theses & Dissertations
Giardia lamblia is a protozoan parasite that causes various intestinal syndromes, and it is a common cause of water-borne illness worldwide, both in developed and developing countries. Giardia attaches to the mucosal epithelia of the duodenum below the bile duct, where it is exposed to bile salts and dietary lipids. G. lamblia is unable to synthesize lipids de novo and must therefore scavenge necessary lipids from its extracellular environment and remodel them as needed. However, the current lipidomic analysis (presented in this Dissertation) has revealed that while the Giardia lipidome is rich in phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylglycerol (PG), …
A Locus-Based Paradigm For Generating Systems Biological Inferences From Large Scale Functional Genomics Datasets, Ajish Dominic George
A Locus-Based Paradigm For Generating Systems Biological Inferences From Large Scale Functional Genomics Datasets, Ajish Dominic George
Legacy Theses & Dissertations (2009 - 2024)
Genomics data is growing at a exponential rate. The ability to integrate new results with existing knowledge about genomic biology is rapidly becoming the limiting factor as there no universal language with which to describe genomic functional elements. To integrate and compare new and existing genomic data, we define our basic functional unit of a genome to be a locus -- a set of positional coordinates along any genome with an arbitrary amount of functional annotations attached. The locus concept enables addressing genomic elements and annotations at any level of granularity from entire swaths of chromosomes to single base-positions. We …
Alternative Probeset Definitions For Combining Microarray Data Across Studies Using Different Versions Of Affymetrix Oligonucleotide Arrays, Jeffrey S. Morris, Chunlei Wu, Kevin R. Coombes, Keith A. Baggerly, Jing Wang, Li Zhang
Alternative Probeset Definitions For Combining Microarray Data Across Studies Using Different Versions Of Affymetrix Oligonucleotide Arrays, Jeffrey S. Morris, Chunlei Wu, Kevin R. Coombes, Keith A. Baggerly, Jing Wang, Li Zhang
Jeffrey S. Morris
Many published microarray studies have small to moderate sample sizes, and thus have low statistical power to detect significant relationships between gene expression levels and outcomes of interest. By pooling data across multiple studies, however, we can gain power, enabling us to detect new relationships. This type of pooling is complicated by the fact that gene expression measurements from different microarray platforms are not directly comparable. In this chapter, we discuss two methods for combining information across different versions of Affymetrix oligonucleotide arrays. Each involves a new approach for combining probes on the array into probesets. The first approach involves …
Some Statistical Issues In Microarray Gene Expression Data, Matthew S. Mayo, Byron J. Gajewski, Jeffrey S. Morris
Some Statistical Issues In Microarray Gene Expression Data, Matthew S. Mayo, Byron J. Gajewski, Jeffrey S. Morris
Jeffrey S. Morris
In this paper we discuss some of the statistical issues that should be considered when conducting experiments involving microarray gene expression data. We discuss statistical issues related to preprocessing the data as well as the analysis of the data. Analysis of the data is discussed in three contexts: class comparison, class prediction and class discovery. We also review the methods used in two studies that are using microarray gene expression to assess the effect of exposure to radiofrequency (RF) fields on gene expression. Our intent is to provide a guide for radiation researchers when conducting studies involving microarray gene expression …
Shrinkage Estimation For Sage Data Using A Mixture Dirichlet Prior, Jeffrey S. Morris, Keith A. Baggerly, Kevin R. Coombes
Shrinkage Estimation For Sage Data Using A Mixture Dirichlet Prior, Jeffrey S. Morris, Keith A. Baggerly, Kevin R. Coombes
Jeffrey S. Morris
Serial Analysis of Gene Expression (SAGE) is a technique for estimating the gene expression profile of a biological sample. Any efficient inference in SAGE must be based upon efficient estimates of these gene expression profiles, which consist of the estimated relative abundances for each mRNA species present in the sample. The data from SAGE experiments are counts for each observed mRNA species, and can be modeled using a multinomial distribution with two characteristics: skewness in the distribution of relative abundances and small sample size relative to the dimension. As a result of these characteristics, a given SAGE sample will fail …
An Introduction To High-Throughput Bioinformatics Data, Keith A. Baggerly, Kevin R. Coombes, Jeffrey S. Morris
An Introduction To High-Throughput Bioinformatics Data, Keith A. Baggerly, Kevin R. Coombes, Jeffrey S. Morris
Jeffrey S. Morris
High throughput biological assays supply thousands of measurements per sample, and the sheer amount of related data increases the need for better models to enhance inference. Such models, however, are more effective if they take into account the idiosyncracies associated with the specific methods of measurement: where the numbers come from. We illustrate this point by describing three different measurement platforms: microarrays, serial analysis of gene expression (SAGE), and proteomic mass spectrometry.
Bayesian Mixture Models For Gene Expression And Protein Profiles, Michele Guindani, Kim-Anh Do, Peter Mueller, Jeffrey S. Morris
Bayesian Mixture Models For Gene Expression And Protein Profiles, Michele Guindani, Kim-Anh Do, Peter Mueller, Jeffrey S. Morris
Jeffrey S. Morris
We review the use of semi-parametric mixture models for Bayesian inference in high throughput genomic data. We discuss three specific approaches for microarray data, for protein mass spectrometry experiments, and for SAGE data. For the microarray data and the protein mass spectrometry we assume group comparison experiments, i.e., experiments that seek to identify genes and proteins that are differentially expressed across two biologic conditions of interest. For the SAGE data example we consider inference for a single biologic sample.
Pooling Information Across Different Studies And Oligonucleotide Microarray Chip Types To Identify Prognostic Genes For Lung Cancer., Jeffrey S. Morris, Guosheng Yin, Keith A. Baggerly, Chunlei Wu, Li Zhang
Pooling Information Across Different Studies And Oligonucleotide Microarray Chip Types To Identify Prognostic Genes For Lung Cancer., Jeffrey S. Morris, Guosheng Yin, Keith A. Baggerly, Chunlei Wu, Li Zhang
Jeffrey S. Morris
Our goal in this work is to pool information across microarray studies conducted at different institutions using two different versions of Affymetrix chips to identify genes whose expression levels offer information on lung cancer patients’ survival above and beyond the information provided by readily available clinical covariates. We combine information across chip types by identifying “matching probes” present on both chips, and then assembling them into new probesets based on Unigene clusters. This method yields comparable expression level quantifications across chips without sacrificing much precision or significantly altering the relative ordering of the samples. We fit a series of multivariable …
The Importance Of Experimental Design In Proteomic Mass Spectrometry Experiments: Some Cautionary Tales, Jeffrey S. Morris, Jianhua Hu, Kevin R. Coombes, Keith A. Baggerly
The Importance Of Experimental Design In Proteomic Mass Spectrometry Experiments: Some Cautionary Tales, Jeffrey S. Morris, Jianhua Hu, Kevin R. Coombes, Keith A. Baggerly
Jeffrey S. Morris
Proteomic expression patterns derived from mass spectrometry have been put forward as potential biomarkers for the early diagnosis of cancer and other diseases. This approach has generated much excitement and has led to a large number of new experiments and vast amounts of new data. The data, derived at great expense, can have very little value if careful attention is not paid to the experimental design and analysis. Using examples from surfaceenhanced laser desorption/ionisation time-of-flight (SELDI-TOF) and matrix-assisted laser desorption–ionisation/time-of-flight (MALDI-TOF) experiments, we describe several experimental design issues that can corrupt a dataset. Fortunately, the problems we identify can be …
Bayesian Shrinkage Estimation Of The Relative Abundance Of Mrna Transcripts Using Sage, Jeffrey S. Morris, Keith A. Baggerly, Kevin R. Coombes
Bayesian Shrinkage Estimation Of The Relative Abundance Of Mrna Transcripts Using Sage, Jeffrey S. Morris, Keith A. Baggerly, Kevin R. Coombes
Jeffrey S. Morris
Serial analysis of gene expression (SAGE) is a technology for quantifying gene expression in biological tissue that yields count data that can be modeled by a multinomial distribution with two characteristics: skewness in the relative frequencies and small sample size relative to the dimension. As a result of these characteristics, a given SAGE sample may fail to capture a large number of expressed mRNA species present in the tissue. Empirical estimators of mRNA species’ relative abundance effectively ignore these missing species, and as a result tend to overestimate the abundance of the scarce observed species comprising a vast majority of …