708 research outputs found
GOstat: find statistically overrepresented Gene Ontologies within a group of genes
No full textModern experimental techniques, as for example DNA microarrays, as a result usually produce a long list of genes, which are potentially interesting in the analyzed process. In order to gain biological understanding from this type of data, it is necessary to analyze the functional annotations of all genes in this list. The Gene-Ontology (GO) database provides a useful tool to annotate and analyze the functions of a large number of genes. Here, we introduce a tool that utilizes this information to obtain an understanding of which annotations are typical for the analyzed list of genes. This program automatically obtains the GO annotations from a database and generates statistics of which annotations are overrepresented in the analyzed list of genes. This results in a list of GO terms sorted by their specificity
Corrigendum to “Assessing the capacity of three production efficiency models in simulating gross carbon uptake across multiple biomes in conterminous USA” [Agric. Forest Meterol. 174–175 (2013) 158–169]
No full textThe authors regret that the printed version of the above article missed out the third author's name. The correct and final version follows. The authors would like to apologise for any inconvenience caused.We wish to add: Terence P. Dawson, School of the Environment, University of Dundee, Dundee, DD1 4HN, United Kingdom as a co-author in the article
Normalization of RNA-seq data using factor analysis of control genes or samples
No full textNormalization of RNA-sequencing (RNA-seq) data has proven essential to ensure accurate inference of expression levels. Here, we show that usual normalization approaches mostly account for sequencing depth and fail to correct for library preparation and other more complex unwanted technical effects. We evaluate the performance of the External RNA Control Consortium (ERCC) spike-in controls and investigate the possibility of using them directly for normalization. We show that the spike-ins are not reliable enough to be used in standard global-scaling or regression-based normalization procedures. We propose a normalization strategy, called remove unwanted variation (RUV), that adjusts for nuisance technical effects by performing factor analysis on suitable sets of control genes (e.g., ERCC spike-ins) or samples (e.g., replicate libraries). Our approach leads to more accurate estimates of expression fold-changes and tests of differential expression compared to state-of-the-art normalization methods. In particular, RUV promises to be valuable for large collaborative projects involving multiple laboratories, technicians, and/or sequencing platforms
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Statistical models for longitudinal analysis of single and mixed species infections
No full textAbstractStatistical models for longitudinal analysis of single and mixed species infectionsByKathryn Louise ColbornDoctor of Philosophy in BiostatisticsUniversity of California, BerkeleyProfessor Terence P. Speed, ChairThere are numerous examples of infectious diseases that are caused by various species ofthe same pathogen. Some examples include Lyme disease, malaria, Leishmaniasis, Denguefever, and Ehrlichiosis. The advancement of laboratory methods has facilitated more sensitivedetection of mixed species infections in humans, which has resulted in a surge of research focussingon the eects of mixed infections on clinical outcomes. Cross-sectional blood samplescompared with clinical outcome measures provide a limited scope of the interactions betweenspecies. It is important to study these infections in humans longitudinally, and within their naturalenvironments, in order to develop an understanding of the complex relationships betweenhosts, pathogens and vectors of transmission.Papua New Guinea is a country with high prevalence of both Plasmodium falciparum and P.vivax, two species of parasites that can cause malaria. It is well known that these two parasitescan cause severe morbidity and mortality independently, but there has not been conclusiveevidence of the eect of mixed P. falciparum and P. vivax infections on clinical symptoms.Children under age five are at highest risk of experiencing adverse outcomes from Plasmodiuminfections. In 2006, a cohort study was implemented to conduct an investigation of the eectsof mixed P. falciparum and P. vivax infections on clinical episodes of malaria in children livingin a rural area of Papua New Guinea. The data collected from this study are used throughoutthis dissertation to address both the epidemiological questions of the study investigators and topresent statistical models for analyzing longitudinal malaria data and mixed species infections
A systematic approach for comprehensive T-cell epitope discovery using peptide libraries
No full textT-cell response to peptides bound on MHC Class I or Class II molecules is essential for immune recognition of pathogens. T-cells are activated by specific peptide epitopes that are determined within the antigen processing pathways and presented on the surface of other cells bound to MHC molecules. To determine which part of allergenic or pathogenic proteins can stimulate T-cells is important for the treatment of diseases. We sought to take advantage of the falling cost of synthetic, screening grade peptides, and devise a comprehensive, non-hypothesis-driven screen for T-cell epitopes. We were interested in the study of celiac disease (CD) and used the ELISPOT technique to perform a large number of T-cell assays. We therefore needed to compensate for the lack of statistical data analysis methods for ELISPOT assays
Silencing of Odorant Receptor Genes by G Protein βγ Signaling Ensures the Expression of One Odorant Receptor per Olfactory Sensory Neuron
No full textSummaryOlfactory sensory neurons express just one out of a possible ∼1,000 odorant receptor genes, reflecting an exquisite mode of gene regulation. In one model, once an odorant receptor is chosen for expression, other receptor genes are suppressed by a negative feedback mechanism, ensuring a stable functional identity of the sensory neuron for the lifetime of the cell. The signal transduction mechanism subserving odorant receptor gene silencing remains obscure, however. Here, we demonstrate in the zebrafish that odorant receptor gene silencing is dependent on receptor activity. Moreover, we show that signaling through G protein βγ subunits is both necessary and sufficient to suppress the expression of odorant receptor genes and likely acts through histone methylation to maintain the silenced odorant receptor genes in transcriptionally inactive heterochromatin. These results link receptor activity with the epigenetic mechanisms responsible for ensuring the expression of one odorant receptor per olfactory sensory neuron
Reproductive failure and the major histocompatibility complex
No full text The association between HLA sharing and recurrent spontaneous abortion (RSA) was tested in 123 couples and the association between HLA sharing, and the outcome of treatment for unexplained infertility by in vitro fertilization (IVF) was tested in 76 couples, by using a new shared- allele test in order to identify more precisely the region of the major histocompatibility complex (MHC ) infleuencing these reproductive defects. The shared-allele test circumvents the problem of rare alleles at HLA loci and at the same time provides a substantial gain in power over the simple χ2 test. Two statistical methods, a corrected homogeneity test and a bootstrap approach, were developed to compare the allele frequencies at each of the HLA-A, HLA-B, HLA-DR, and HLA-DQ loci; they were not stastically different among three patient groups and the control group. there was a significant excess of HLA-DR sharing in couples with RSA and a excess sharing of HLA- DQ sharing in coules with unexplained infertility who failed treatment by IVF. These findings indicate that genes located in different parts of the class II region of the MHC affect different aspects of reproduction and strongly suggest that the sharing of HLA antigens per se is not the mechanism involved in the reproductive defects. The segment of the MHC that has genes affecting reproduction also has genes associated with different autoimmune diseases, and this juxtaposition may explain the association between reproductive defects and autoimmune diseases.#1845
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Statistical problems in DNA microarray data analysis
No full textDNA microarrays are powerful tools for functional genomics studies. Each array contains thousands of microscopic spots of DNA oligonucleotides with specific sequences, which can hybridize with their complementary DNA sequences. Thus each microarray experiment consists of parallel assays about thousands of genomic fragments. This thesis concerns some statistical issues in the analysis of DNA microarray data. One common usage of DNA microarrays is to monitor the dynamic levels of gene expression in response to a stimulus. This is often achieved through a time course experiment, in which RNA samples are extracted at various time points after exposing the organism to the stimulus. A particularly interesting type of time course experiments involve replicated series of longitudinal samples. In 2006, Tai and Speed proposed a multivariate empirical Bayes model for analyzing this type of data. The MB-statistic derived from this model was shown useful for ranking the genes according to changes in their temporal expression profiles. In the first part of this thesis, we propose an empirical Bayes false discovery rate (FDR)-controlling procedure for multiple hypothesis testing using the MB-statistic. A null distribution is obtained using the parametric bootstrap. Critical values are determined according to the empirical Bayes FDR procedure. This method was compared, through simulations, to the frequentist FDR procedure, which requires a theoretical null distribution for calculating the nominal p-values. Although our method is slightly anti-conservative, it is more robust to the variability in the estimates of the hyperparameters, when the degree of moderation is small. Another common usage of DNA microarrays is to detect genomic locations that are associated with DNA-binding proteins. This is often achieved through ChIP-chip experiments that combine chromatin immunoprecipitation with the microarray technology. Traditional DNA microarrays designed for gene expression studies contain only a few probes for each gene. A special type of DNA microarrays, called tiling arrays, are often used in ChIP-chip experiments. They typically contain probes that are placed densely along the chromosomes to cover either the entire genome or contigs of the genome. A couple of challenges in the analysis of ChIP-chip tiling array data have not been met satisfactorily in the literature. When large scale genomic studies are carried over a long period of time, tiling arrays with different probe designs are often used for practical reasons. The first challenge is the integration of replicate experiments performed using different tiling array designs. When the biological process of interest involves a large protein complex, the investigators often perform ChIP-chip experiments on each component DNA-binding protein individually. DNA targets that are shared by the individual proteins are thought to be the localization sites of the protein complex. The second challenge is the joint analysis of multiple DNA-binding proteins, aimed at identifying their shared targets. In the second part of this thesis, we propose a nonhomogeneous hidden Markov model (HMM) for addressing these two challenges. The nonhomogeneous time axis represents the genomic positions of the probes. The hidden states represent the binding statuses of the proteins. The state-conditional emission distributions of the tiling array data are protein-specific and design-specific. We derived a modified Baum-Welch algorithm for fitting the model parameters. We also developed a procedure that converts the probe level summaries into peaks, which represent the putative binding sites, based on both signal strength and peak shape. To compare our method with existing methods, we curated a set of positive and negative genomic regions from a C. elegans dataset, and performed some receiver operating characteristics (ROC) analyses. When applied to each experiment separately, our method performs similarly as the three best existing methods. When applied to the combined data set, which consists of tiling arrays with different probe designs, our method shows a drastic improvement in performance. A generalization of the nonhomogeneous HMM enables the joint analysis of the ChIP-chip data of multiple proteins. We present an application of this method to identify the shared localization sites of two DNA-binding proteins, under two different conditions
Poor prognosis in familial acute myeloid leukaemia with combined biallelic CEBPA mutations and downstream events affecting the ATM, FLT3 and CDX2 genes
No full textCatherine L. Carmichael, Ella J. Wilkins, Henrik Bengtsson, Marshall S. Horwitz, Terence P. Speed, Paul C. Vincent, Graham Young, Christopher N. Hahn, Robert Escher, Hamish S. Scot
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