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Dive into the research topics where Kathleen ONeill is active.

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Featured researches published by Kathleen ONeill.


Nucleic Acids Research | 2016

Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation

Nuala A. O'Leary; Mathew W. Wright; J. Rodney Brister; Stacy Ciufo; Diana Haddad; Richard McVeigh; Bhanu Rajput; Barbara Robbertse; Brian Smith-White; Danso Ako-adjei; Alexander Astashyn; Azat Badretdin; Yiming Bao; Olga Blinkova; Vyacheslav Brover; Vyacheslav Chetvernin; Jinna Choi; Eric Cox; Olga Ermolaeva; Catherine M. Farrell; Tamara Goldfarb; Tripti Gupta; Daniel H. Haft; Eneida Hatcher; Wratko Hlavina; Vinita Joardar; Vamsi K. Kodali; Wenjun Li; Donna Maglott; Patrick Masterson

The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55 000 organisms (>4800 viruses, >40 000 prokaryotes and >10 000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.


Nucleic Acids Research | 2014

RefSeq microbial genomes database: new representation and annotation strategy

Tatiana Tatusova; Stacy Ciufo; Boris Fedorov; Kathleen ONeill; Igor Tolstoy

The source of the microbial genomic sequences in the RefSeq collection is the set of primary sequence records submitted to the International Nucleotide Sequence Database public archives. These can be accessed through the Entrez search and retrieval system at http://www.ncbi.nlm.nih.gov/genome. Next-generation sequencing has enabled researchers to perform genomic sequencing at rates that were unimaginable in the past. Microbial genomes can now be sequenced in a matter of hours, which has led to a significant increase in the number of assembled genomes deposited in the public archives. This huge increase in DNA sequence data presents new challenges for the annotation, analysis and visualization bioinformatics tools. New strategies have been developed for the annotation and representation of reference genomes and sequence variations derived from population studies and clinical outbreaks.


Nucleic Acids Research | 2015

Update on RefSeq microbial genomes resources

Tatiana Tatusova; Stacy Ciufo; Scott Federhen; Boris Fedorov; Richard McVeigh; Kathleen ONeill; Igor Tolstoy; Leonid Zaslavsky

NCBI RefSeq genome collection http://www.ncbi.nlm.nih.gov/genome represents all three major domains of life: Eukarya, Bacteria and Archaea as well as Viruses. Prokaryotic genome sequences are the most rapidly growing part of the collection. During the year of 2014 more than 10 000 microbial genome assemblies have been publicly released bringing the total number of prokaryotic genomes close to 30 000. We continue to improve the quality and usability of the microbial genome resources by providing easy access to the data and the results of the pre-computed analysis, and improving analysis and visualization tools. A number of improvements have been incorporated into the Prokaryotic Genome Annotation Pipeline. Several new features have been added to RefSeq prokaryotic genomes data processing pipeline including the calculation of genome groups (clades) and the optimization of protein clusters generation using pan-genome approach.


Pharmacogenomics | 2004

Rationale and study design of the CardioGene Study: genomics of in-stent restenosis

Santhi K. Ganesh; Kimberly A. Skelding; Laxmi S. Mehta; Kathleen ONeill; Jungnam Joo; Gang Zheng; James A. Goldstein; Robert D. Simari; Eric M. Billings; Nancy L. Geller; David R. Holmes; William W. O'Neill; Elizabeth G. Nabel

BACKGROUND AND AIMS in-stent restenosis is a major limitation of stent therapy for atherosclerosis coronary artery disease. The CardioGene Study is an ongoing study of restenosis in bare mental stents (BMS) for the treatment of coronary artery disease. The overall goal is to understand the genetic determinants of the responses to vascular injury that result in the development of restenosis in some patients but not in others. Gene expression profiling at transcriptional and translational levels provides global assessment of gene activity after vascular injury and mechanistic insight. Furthermore, the delineation of genetic biomarkers would be of value in the clinical setting of risk-stratify patients prior to stent therapy. Prospective risk stratification would allow for the rational selection of specialized treatments against the development of in-stent restenosis (ISR), such as drug-eluting stents. SETTING Patients are enrolled at two sites in the US with high-volume cardiac catheterization facilities: the William Beaumont Hospital in Royal Oak, MI, USA, and the Mayo Clinic in Rochester, MN, USA. STUDY DESIGN Two complementary study designs are used to understand the molecular mechanisms of restenosis and the genetic biomarkers predictive of restenosis. First, 350 patients are enrolled prospectively at the time of stent implantation. Blood is sampled prior to stent placement and afterwards at 2 weeks and 6 months. The clinical outcome of restenosis is determined 6 and 12 months after stent placement. The primary outcome is clinical restenosis at 6 months. The major secondary outcome is clinical restenosis at 12 months. Second, a corollary case-control analysis will be carried out with the enrollment of an additional 250 cases with a history of recurrent restenosis after treatment with BMS. Controls for this analysis are derived from the prospective cohort. PATIENTS AND METHODS Consecutive patients presenting to the cardiac catheterization laboratory are screened, informed about the study and enrolled after signing the consent form. Enrollment has been completed for the prospective cohort, and enrollment of the additional group is ongoing. A standardized questionnaire is used to collect clinical data primarily through direct patient interview to assess medical history, medication use, functional status, family history, environmental factors, and social history. Further data are abstracted from the medical charts and catheterization reports. A total of 276 clinical variables are collected per individual at baseline, and 49 variables are collected at each of the 6- and 12-month follow-up visits. A Clinical Events Committee adjudicates clinical outcomes. Blood samples are processed at each clinical enrollment site using standardized operating procedures. From each blood sample, several aliquots are prepared and stored of peripheral blood mononuclear cells, granulocytes, platelets, serum, and plasma. Additionally, a portion of each patients leukocytes is cryopreserved for future cell-line creation. Samples are frozen and shipped to the National Heart, Lung and Blood Institute (NHLBI). Additional materials generated in the analysis of the samples at the NHLBI are frozen and stored, including isolated genomic DNA, total RNA, reverse transcribed cDNA libraries and labeled RNA hybridization mixtures used in microarray analysis. Per individual in the prospective cohort, high-quality transcript profiles of peripheral blood mononuclear cells at each time of blood sampling are obtained using Affymetrix U133A microarrays (Affymetrix, Santa Clara, CA, USA). Per chip, this yields 495,930 features per individual per time of sampling. This represents expression levels for 22,283 genes per patients oer time of blood sampling, including 14,500 well-characterized human genes. Proteomics of plasma is performed with multidimensional liquid chromatography and tandem mass spectrometry. Protein expression is examined similarly to mRNA expression as a measure of gene expression. Genotyping is performed in two manners. First, those genes showing differential expression at the levels of mRNA and protein are investigated using a candidate gene approach. Specific variants in known gene regulatory regions, such as promoters, are sought initially, as those variants may explain differences in expression level. Second, a genome-wide scan is used to identify genetic loci that are associated with ISR. Those regions identified are further examined for genes that show differential expression in the mRNA microarray profiling or proteomics investigations. These genes are finely investigated for candidate SNPs and other gene variants. Complementary genomic and proteomic approaches are expected to be robust. Integration of data sets is accomplished using a variety of informatics tools, organization of gene expression into functional pathways, and investigation of physical maps of up- and downregulated sets of genes. CONCLUSIONS The CardioGene Study is designed to understand ISR. Global gene and protein expression profiling define molecular phenotypes of patients. Well-defined clinical phenotypes will be paired with genomic data to define analyses aimed to achieve several goals. These include determining blood gene and protein expression in patients with ISR, investigating the genetic basis of ISR, developing predictive gene and protein biomarkers, and the identification of new targets for treatment.


BMC Medical Genomics | 2011

Time course analysis of gene expression identifies multiple genes with differential expression in patients with in-stent restenosis

Santhi K. Ganesh; Jungnam Joo; Kimberly A. Skelding; Laxmi S. Mehta; Gang Zheng; Kathleen ONeill; Eric M. Billings; Anna Helgadottir; Karl Andersen; Gudmundur Thorgeirsson; Thorarinn Gudnason; Nancy L. Geller; Robert D. Simari; David R. Holmes; William W. O'Neill; Elizabeth G. Nabel

BackgroundThe vascular disease in-stent restenosis (ISR) is characterized by formation of neointima and adverse inward remodeling of the artery after injury by coronary stent implantation. We hypothesized that the analysis of gene expression in peripheral blood mononuclear cells (PBMCs) would demonstrate differences in transcript expression between individuals who develop ISR and those who do not.Methods and ResultsWe determined and investigated PBMC gene expression of 358 patients undergoing an index procedure to treat in de novo coronary artery lesions with bare metallic stents, using a novel time-varying intercept model to optimally assess the time course of gene expression across a time course of blood samples. Validation analyses were conducted in an independent sample of 97 patients with similar time-course blood sampling and gene expression data. We identified 47 probesets with differential expression, of which 36 were validated upon independent replication testing. The genes identified have varied functions, including some related to cellular growth and metabolism, such as the NAB2 and LAMP genes.ConclusionsIn a study of patients undergoing bare metallic stent implantation, we have identified and replicated differential gene expression in peripheral blood mononuclear cells, studied across a time series of blood samples. The genes identified suggest alterations in cellular growth and metabolism pathways, and these results provide the basis for further specific functional hypothesis generation and testing of the mechanisms of ISR.


Archive | 2010

Protein Clusters: A Collection of Proteins Grouped by Sequence Similarity and Function

Kathleen ONeill; William Klimke; Tatiana Tatusova


Archive | 2010

Figure 8. [Sequence similarity search against protein...].

Kathleen ONeill; William Klimke; Tatiana Tatusova


Archive | 2010

Figure 2. [Protein table. A protein table...].

Kathleen ONeill; William Klimke; Tatiana Tatusova


Archive | 2010

Figure 7. [Protein clusters limits page. For...].

Kathleen ONeill; William Klimke; Tatiana Tatusova


Archive | 2010

[Table, Protein Table].

Kathleen ONeill; William Klimke; Tatiana Tatusova

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Tatiana Tatusova

National Institutes of Health

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William Klimke

National Institutes of Health

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Elizabeth G. Nabel

National Institutes of Health

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Eric M. Billings

National Institutes of Health

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Gang Zheng

National Institutes of Health

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Jungnam Joo

National Institutes of Health

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Nancy L. Geller

National Institutes of Health

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