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Search Results: 1 - 10 of 211523 matches for " Ann L. Oberg "
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Statistical methods for quantitative mass spectrometry proteomic experiments with labeling
Oberg Ann L,Mahoney Douglas W
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-s16-s7
Abstract: Mass Spectrometry utilizing labeling allows multiple specimens to be subjected to mass spectrometry simultaneously. As a result, between-experiment variability is reduced. Here we describe use of fundamental concepts of statistical experimental design in the labeling framework in order to minimize variability and avoid biases. We demonstrate how to export data in the format that is most efficient for statistical analysis. We demonstrate how to assess the need for normalization, perform normalization, and check whether it worked. We describe how to build a model explaining the observed values and test for differential protein abundance along with descriptive statistics and measures of reliability of the findings. Concepts are illustrated through the use of three case studies utilizing the iTRAQ 4-plex labeling protocol.
Technical and biological variance structure in mRNA-Seq data: life in the real world
Ann L Oberg, Brian M Bot, Diane E Grill, Gregory A Poland, Terry M Therneau
BMC Genomics , 2012, DOI: 10.1186/1471-2164-13-304
Abstract: In mRNA-Seq data from 25 subjects, we found technical variation to generally follow a Poisson distribution as has been reported previously and biological variability was over-dispersed relative to the Poisson model. The mean-variance relationship across all genes was quadratic, in keeping with a Negative Binomial (NB) distribution. Over-dispersed Poisson and NB distributional assumptions demonstrated marked improvements in goodness-of-fit (GOF) over the standard Poisson model assumptions, but with evidence of over-fitting in some genes. Modeling of experimental effects improved GOF for high variance genes but increased the over-fitting problem.These conclusions will guide development of analytical strategies for accurate modeling of variance structure in these data and sample size determination which in turn will aid in the identification of true biological signals that inform our understanding of biological systems.
RNA-seq: technical variability and sampling
Lauren M McIntyre, Kenneth K Lopiano, Alison M Morse, Victor Amin, Ann L Oberg, Linda J Young, Sergey V Nuzhdin
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-293
Abstract: In this study three independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage.Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.RNA-seq (high throughput sequencing of the transcriptome) has the potential to transform the way we study gene structure and expression [1]. The ability to identify novel exons and splice sites [2-6] is just the beginning. Although there have been claims that RNA-seq is more sensitive [7] and has a larger dynamic range [3] than a microarray, these claims are now being challenged [8]. For all the optimism surrounding RNA-seq, there is growing evidence that estimating gene expression in an RNA-seq environment is not straightforward. In addition, technical variance, sequencing bias and mapping bias have been reported [9-11].The power and promise of RNA-seq technology demand our attention. New papers on experimental design [12] will lead the way to more thoughtful experimentation. One important component in careful study planning is an understanding of the different sources of variability so they can be accounted for in t
The Ratios of CD8+ T Cells to CD4+CD25+ FOXP3+ and FOXP3- T Cells Correlate with Poor Clinical Outcome in Human Serous Ovarian Cancer
Claudia C. Preston, Matthew J. Maurer, Ann L. Oberg, Daniel W. Visscher, Kimberly R. Kalli, Lynn C. Hartmann, Ellen L. Goode, Keith L. Knutson
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0080063
Abstract: Ovarian cancer is an immune reactive malignancy with a complex immune suppressive network that blunts successful immune eradication. This suppressive microenvironment may be mediated by recruitment or induction of CD4+ regulatory T cells (Tregs). Our study sought to investigate the association of tumor-infiltrating CD4+CD25+FOXP3+ Tregs, and other immune factors, with clinical outcome in serous ovarian cancer patients. We performed immunofluorescence and quantification of intraepithelial tumor-infiltrating triple positive Tregs (CD4+CD25+FOXP3+), as well as CD4+CD25+FOXP3-, CD3+ and CD8+ T cells in tumor specimens from 52 patients with high stage serous ovarian carcinoma. Thirty-one of the patients had good survival (i.e. > 60 months) and 21 had poor survival of < 18 months. Total cell counts as well as cell ratios were compared among these two outcome groups. The total numbers of CD4+CD25+FOXP3+ Tregs, CD4+CD25+FOXP3-, CD3+ and CD8+ cells were not significantly different between the groups. However, higher ratios of CD8+/CD4+CD25+FOXP3+ Treg, CD8+/CD4+ and CD8/CD4+CD25+FOXP3- cells were seen in the good outcome group when compared to the patients with poor outcome. These data show for the first time that the ratios of CD8+ to both CD4+CD25+FOXP3+ Tregs and CD4+CD25+FOXP3- T cells are associated with disease outcome in ovarian cancer. The association being apparent in ratios rather than absolute count of T cells suggests that the effector/suppressor ratio may be a more important indicator of outcome than individual cell count. Thus, immunotherapy strategies that modify the ratio of CD4+CD25+FOXP3+ Tregs or CD4+CD25+FOXP3- T cells to CD8+ effector cells may be useful in improving outcomes in ovarian cancer.
Leukocyte DNA Methylation Signature Differentiates Pancreatic Cancer Patients from Healthy Controls
Katrina S. Pedersen,William R. Bamlet,Ann L. Oberg,Mariza de Andrade,Martha E. Matsumoto,Hui Tang,Stephen N. Thibodeau,Gloria M. Petersen,Liang Wang
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0018223
Abstract: Pancreatic adenocarcinoma (PaC) is one of most difficult tumors to treat. Much of this is attributed to the late diagnosis. To identify biomarkers for early detection, we examined DNA methylation differences in leukocyte DNA between PaC cases and controls in a two-phase study. In phase I, we measured methylation levels at 1,505 CpG sites in treatment-na?ve leukocyte DNA from 132 never-smoker PaC patients and 60 never-smoker healthy controls. We found significant differences in 110 CpG sites (false discovery rate <0.05). In phase II, we tested and validated 88 of 96 phase I selected CpG sites in 240 PaC cases and 240 matched controls (p≤0.05). Using penalized logistic regression, we built a prediction model consisting of five CpG sites (IL10_P348, LCN2_P86, ZAP70_P220, AIM2_P624, TAL1_P817) that discriminated PaC patients from controls (C-statistic = 0.85 in phase I; 0.76 in phase II). Interestingly, one CpG site (LCN2_P86) alone could discriminate resectable patients from controls (C-statistic = 0.78 in phase I; 0.74 in phase II). We also performed methylation quantitative trait loci (methQTL) analysis and identified three CpG sites (AGXT_P180_F, ALOX12_E85_R, JAK3_P1075_R) where the methylation levels were significantly associated with single nucleotide polymorphisms (SNPs) (false discovery rate <0.05). Our results demonstrate that epigenetic variation in easily obtainable leukocyte DNA, manifested by reproducible methylation differences, may be used to detect PaC patients. The methylation differences at certain CpG sites are partially attributable to genetic variation. This study strongly supports future epigenome-wide association study using leukocyte DNA for biomarker discovery in human diseases.
ReliefSeq: A Gene-Wise Adaptive-K Nearest-Neighbor Feature Selection Tool for Finding Gene-Gene Interactions and Main Effects in mRNA-Seq Gene Expression Data
Brett A. McKinney, Bill C. White, Diane E. Grill, Peter W. Li, Richard B. Kennedy, Gregory A. Poland, Ann L. Oberg
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0081527
Abstract: Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main effects and interaction effects. Software Availability: http://insilico.utulsa.edu/ReliefSeq.php.
miRNA Expression in Colon Polyps Provides Evidence for a Multihit Model of Colon Cancer
Ann L. Oberg,Amy J. French,Aaron L. Sarver,Subbaya Subramanian,Bruce W. Morlan,Shaun M. Riska,Pedro M. Borralho,Julie M. Cunningham,Lisa A. Boardman,Liang Wang,Thomas C. Smyrk,Yan Asmann,Clifford J. Steer,Stephen N. Thibodeau
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0020465
Abstract: Changes in miRNA expression are a common feature in colon cancer. Those changes occurring in the transition from normal to adenoma and from adenoma to carcinoma, however, have not been well defined. Additionally, miRNA changes among tumor subgroups of colon cancer have also not been adequately evaluated. In this study, we examined the global miRNA expression in 315 samples that included 52 normal colonic mucosa, 41 tubulovillous adenomas, 158 adenocarcinomas with proficient DNA mismatch repair (pMMR) selected for stage and age of onset, and 64 adenocarcinomas with defective DNA mismatch repair (dMMR) selected for sporadic (n = 53) and inherited colon cancer (n = 11). Sporadic dMMR tumors all had MLH1 inactivation due to promoter hypermethylation. Unsupervised PCA and cluster analysis demonstrated that normal colon tissue, adenomas, pMMR carcinomas and dMMR carcinomas were all clearly discernable. The majority of miRNAs that were differentially expressed between normal and polyp were also differentially expressed with a similar magnitude in the comparison of normal to both the pMMR and dMMR tumor groups, suggesting a stepwise progression for transformation from normal colon to carcinoma. Among the miRNAs demonstrating the largest fold up- or down-regulated changes (≥4), four novel (miR-31, miR-1, miR-9 and miR-99a) and two previously reported (miR-137 and miR-135b) miRNAs were identified in the normal/adenoma comparison. All but one of these (miR-99a) demonstrated similar expression differences in the two normal/carcinoma comparisons, suggesting that these early tumor changes are important in both the pMMR- and dMMR-derived cancers. The comparison between pMMR and dMMR tumors identified four miRNAs (miR-31, miR-552, miR-592 and miR-224) with statistically significant expression differences (≥2-fold change).
Genome-Wide Transcriptional Profiling Reveals MicroRNA-Correlated Genes and Biological Processes in Human Lymphoblastoid Cell Lines
Liang Wang, Ann L. Oberg, Yan W. Asmann, Hugues Sicotte, Shannon K. McDonnell, Shaun M. Riska, Wanguo Liu, Clifford J. Steer, Subbaya Subramanian, Julie M. Cunningham, James R. Cerhan, Stephen N. Thibodeau
PLOS ONE , 2009, DOI: 10.1371/journal.pone.0005878
Abstract: Background Expression level of many genes shows abundant natural variation in human populations. The variations in gene expression are believed to contribute to phenotypic differences. Emerging evidence has shown that microRNAs (miRNAs) are one of the key regulators of gene expression. However, past studies have focused on the miRNA target genes and used loss- or gain-of-function approach that may not reflect natural association between miRNA and mRNAs. Methodology/Principal Findings To examine miRNA regulatory effect on global gene expression under endogenous condition, we performed pair-wise correlation coefficient analysis on expression levels of 366 miRNAs and 14,174 messenger RNAs (mRNAs) in 90 immortalized lymphoblastoid cell lines, and observed significant correlations between the two species of RNA transcripts. We identified a total of 7,207 significantly correlated miRNA-mRNA pairs (false discovery rate q<0.01). Of those, 4,085 pairs showed positive correlations while 3,122 pairs showed negative correlations. Gene ontology analyses on the miRNA-correlated genes revealed significant enrichments in several biological processes related to cell cycle, cell communication and signal transduction. Individually, each of three miRNAs (miR-331, -98 and -33b) demonstrated significant correlation with the genes in cell cycle-related biological processes, which is consistent with important role of miRNAs in cell cycle regulation. Conclusions/Significance This study demonstrates feasibility of using naturally expressed transcript profiles to identify endogenous correlation between miRNA and miRNA. By applying this genome-wide approach, we have identified thousands of miRNA-correlated genes and revealed potential role of miRNAs in several important cellular functions. The study results along with accompanying data sets will provide a wealth of high-throughput data to further evaluate the miRNA-regulated genes and eventually in phenotypic variations of human populations.
3' tag digital gene expression profiling of human brain and universal reference RNA using Illumina Genome Analyzer
Yan W Asmann, Eric W Klee, E Aubrey Thompson, Edith A Perez, Sumit Middha, Ann L Oberg, Terry M Therneau, David I Smith, Gregory A Poland, Eric D Wieben, Jean-Pierre A Kocher
BMC Genomics , 2009, DOI: 10.1186/1471-2164-10-531
Abstract: Using Brain RNA sample from multiple runs, we demonstrated that the transcript profiles from 3' DGE were highly reproducible between technical and biological replicates from libraries constructed by the same lab and even by different labs, and between two generations of Illumina's Genome Analyzers. Approximately 65% of all sequence reads mapped to mitochondrial genes, ribosomal RNAs, and canonical transcripts. The expression profiles of brain RNA and universal human reference RNA were compared which demonstrated that DGE was also highly quantitative with excellent correlation of differential expression with quantitative real-time PCR. Furthermore, one lane of 3' DGE sequencing, using the current sequencing chemistry and image processing software, had wider dynamic range for transcriptome profiling and was able to detect lower expressed genes which are normally below the detection threshold of microarrays.3' tag DGE profiling with massive parallel sequencing achieved high sensitivity and reproducibility for transcriptome profiling. Although it lacks the ability of detecting alternative splicing events compared to RNA-SEQ, it is much more affordable and clearly out-performed microarrays (Affymetrix) in detecting lower abundant transcripts.The transcriptome can be profiled by high throughput techniques including SAGE [1], microarray [2,3], and sequencing of clones from cDNA libraries [4,5]. For more than a decade, oligo-nucleotide microarrays have been the method of choice providing high throughput and affordable costs. However, microarray technology suffers from well-known limitations including insufficient sensitivity for quantifying lower abundant transcripts, narrow dynamic range and non-specific hybridizations. Additionally, microarrays are limited to only measuring known/annotated transcripts and often suffer from inaccurate annotations [6]. Sequencing-based methods such as SAGE rely upon cloning and sequencing cDNA fragments. This approach allows quantification
Genome-Wide Characterization of Transcriptional Patterns in High and Low Antibody Responders to Rubella Vaccination
Iana H. Haralambieva, Ann L. Oberg, Inna G. Ovsyannikova, Richard B. Kennedy, Diane E. Grill, Sumit Middha, Brian M. Bot, Vivian W. Wang, David I. Smith, Robert M. Jacobson, Gregory A. Poland
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0062149
Abstract: Immune responses to current rubella vaccines demonstrate significant inter-individual variability. We performed mRNA-Seq profiling on PBMCs from high and low antibody responders to rubella vaccination to delineate transcriptional differences upon viral stimulation. Generalized linear models were used to assess the per gene fold change (FC) for stimulated versus unstimulated samples or the interaction between outcome and stimulation. Model results were evaluated by both FC and p-value. Pathway analysis and self-contained gene set tests were performed for assessment of gene group effects. Of 17,566 detected genes, we identified 1,080 highly significant differentially expressed genes upon viral stimulation (p<1.00E?15, FDR<1.00E?14), including various immune function and inflammation-related genes, genes involved in cell signaling, cell regulation and transcription, and genes with unknown function. Analysis by immune outcome and stimulation status identified 27 genes (p≤0.0006 and FDR≤0.30) that responded differently to viral stimulation in high vs. low antibody responders, including major histocompatibility complex (MHC) class I genes (HLA-A, HLA-B and B2M with p = 0.0001, p = 0.0005 and p = 0.0002, respectively), and two genes related to innate immunity and inflammation (EMR3 and MEFV with p = 1.46E?08 and p = 0.0004, respectively). Pathway and gene set analysis also revealed transcriptional differences in antigen presentation and innate/inflammatory gene sets and pathways between high and low responders. Using mRNA-Seq genome-wide transcriptional profiling, we identified antigen presentation and innate/inflammatory genes that may assist in explaining rubella vaccine-induced immune response variations. Such information may provide new scientific insights into vaccine-induced immunity useful in rational vaccine development and immune response monitoring.
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