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Dive into the research topics where Jennifer M. Lee is active.

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Featured researches published by Jennifer M. Lee.


Nucleic Acids Research | 2014

ClinVar: public archive of relationships among sequence variation and human phenotype

Melissa J. Landrum; Jennifer M. Lee; George Riley; Wonhee Jang; Wendy S. Rubinstein; Deanna M. Church; Donna Maglott

ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) provides a freely available archive of reports of relationships among medically important variants and phenotypes. ClinVar accessions submissions reporting human variation, interpretations of the relationship of that variation to human health and the evidence supporting each interpretation. The database is tightly coupled with dbSNP and dbVar, which maintain information about the location of variation on human assemblies. ClinVar is also based on the phenotypic descriptions maintained in MedGen (http://www.ncbi.nlm.nih.gov/medgen). Each ClinVar record represents the submitter, the variation and the phenotype, i.e. the unit that is assigned an accession of the format SCV000000000.0. The submitter can update the submission at any time, in which case a new version is assigned. To facilitate evaluation of the medical importance of each variant, ClinVar aggregates submissions with the same variation/phenotype combination, adds value from other NCBI databases, assigns a distinct accession of the format RCV000000000.0 and reports if there are conflicting clinical interpretations. Data in ClinVar are available in multiple formats, including html, download as XML, VCF or tab-delimited subsets. Data from ClinVar are provided as annotation tracks on genomic RefSeqs and are used in tools such as Variation Reporter (http://www.ncbi.nlm.nih.gov/variation/tools/reporter), which reports what is known about variation based on user-supplied locations.


Nucleic Acids Research | 2016

ClinVar: public archive of interpretations of clinically relevant variants

Melissa J. Landrum; Jennifer M. Lee; Mark Benson; Garth Brown; Chen Chao; Shanmuga Chitipiralla; Baoshan Gu; Jennifer Hart; Douglas W. Hoffman; Jeffrey Hoover; Wonhee Jang; Kenneth S. Katz; Michael Ovetsky; George Riley; Amanjeev Sethi; Ray E. Tully; Ricardo Villamarín-Salomón; Wendy S. Rubinstein; Donna Maglott

ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) at the National Center for Biotechnology Information (NCBI) is a freely available archive for interpretations of clinical significance of variants for reported conditions. The database includes germline and somatic variants of any size, type or genomic location. Interpretations are submitted by clinical testing laboratories, research laboratories, locus-specific databases, OMIM®, GeneReviews™, UniProt, expert panels and practice guidelines. In NCBIs Variation submission portal, submitters upload batch submissions or use the Submission Wizard for single submissions. Each submitted interpretation is assigned an accession number prefixed with SCV. ClinVar staff review validation reports with data types such as HGVS (Human Genome Variation Society) expressions; however, clinical significance is reported directly from submitters. Interpretations are aggregated by variant-condition combination and assigned an accession number prefixed with RCV. Clinical significance is calculated for the aggregate record, indicating consensus or conflict in the submitted interpretations. ClinVar uses data standards, such as HGVS nomenclature for variants and MedGen identifiers for conditions. The data are available on the web as variant-specific views; the entire data set can be downloaded via ftp. Programmatic access for ClinVar records is available through NCBIs E-utilities. Future development includes providing a variant-centric XML archive and a web page for details of SCV submissions.


Genome Research | 2009

The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes

Kim D. Pruitt; Jennifer Harrow; Rachel A. Harte; Craig Wallin; Mark Diekhans; Donna Maglott; Steve Searle; Catherine M. Farrell; Jane Loveland; Barbara J. Ruef; Elizabeth Hart; Marie-Marthe Suner; Melissa J. Landrum; Bronwen Aken; Sarah Ayling; Robert Baertsch; Julio Fernandez-Banet; Joshua L. Cherry; Val Curwen; Michael DiCuccio; Manolis Kellis; Jennifer M. Lee; Michael F. Lin; Michael Schuster; Andrew Shkeda; Clara Amid; Garth Brown; Oksana Dukhanina; Adam Frankish; Jennifer Hart

Effective use of the human and mouse genomes requires reliable identification of genes and their products. Although multiple public resources provide annotation, different methods are used that can result in similar but not identical representation of genes, transcripts, and proteins. The collaborative consensus coding sequence (CCDS) project tracks identical protein annotations on the reference mouse and human genomes with a stable identifier (CCDS ID), and ensures that they are consistently represented on the NCBI, Ensembl, and UCSC Genome Browsers. Importantly, the project coordinates on manually reviewing inconsistent protein annotations between sites, as well as annotations for which new evidence suggests a revision is needed, to progressively converge on a complete protein-coding set for the human and mouse reference genomes, while maintaining a high standard of reliability and biological accuracy. To date, the project has identified 20,159 human and 17,707 mouse consensus coding regions from 17,052 human and 16,893 mouse genes. Three evaluation methods indicate that the entries in the CCDS set are highly likely to represent real proteins, more so than annotations from contributing groups not included in CCDS. The CCDS database thus centralizes the function of identifying well-supported, identically-annotated, protein-coding regions.


Nucleic Acids Research | 2012

The NIH genetic testing registry: a new, centralized database of genetic tests to enable access to comprehensive information and improve transparency

Wendy S. Rubinstein; Donna Maglott; Jennifer M. Lee; Brandi L. Kattman; Adriana J. Malheiro; Michael Ovetsky; Vichet Hem; Viatcheslav Gorelenkov; Guangfeng Song; Craig Wallin; Nora Husain; Shanmuga Chitipiralla; Kenneth S. Katz; Douglas W. Hoffman; Wonhee Jang; Mark R. Johnson; Fedor Karmanov; Alexander Ukrainchik; Mikhail Denisenko; Cathy Fomous; Kathy L. Hudson; James Ostell

The National Institutes of Health Genetic Testing Registry (GTR; available online at http://www.ncbi.nlm.nih.gov/gtr/) maintains comprehensive information about testing offered worldwide for disorders with a genetic basis. Information is voluntarily submitted by test providers. The database provides details of each test (e.g. its purpose, target populations, methods, what it measures, analytical validity, clinical validity, clinical utility, ordering information) and laboratory (e.g. location, contact information, certifications and licenses). Each test is assigned a stable identifier of the format GTR000000000, which is versioned when the submitter updates information. Data submitted by test providers are integrated with basic information maintained in National Center for Biotechnology Information’s databases and presented on the web and through FTP (ftp.ncbi.nih.gov/pub/GTR/_README.html).


Nucleic Acids Research | 2018

ClinVar: improving access to variant interpretations and supporting evidence

Melissa J. Landrum; Jennifer M. Lee; Mark Benson; Garth Brown; Chen Chao; Shanmuga Chitipiralla; Baoshan Gu; Jennifer Hart; Douglas W. Hoffman; Wonhee Jang; Karen Karapetyan; Kenneth S. Katz; Chunlei Liu; Zenith Maddipatla; Adriana J. Malheiro; Kurt McDaniel; Michael Ovetsky; George Riley; George Zhou; J. Bradley Holmes; Brandi L. Kattman; Donna Maglott

Abstract ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) is a freely available, public archive of human genetic variants and interpretations of their significance to disease, maintained at the National Institutes of Health. Interpretations of the clinical significance of variants are submitted by clinical testing laboratories, research laboratories, expert panels and other groups. ClinVar aggregates data by variant-disease pairs, and by variant (or set of variants). Data aggregated by variant are accessible on the website, in an improved set of variant call format files and as a new comprehensive XML report. ClinVar recently started accepting submissions that are focused primarily on providing phenotypic information for individuals who have had genetic testing. Submissions may come from clinical providers providing their own interpretation of the variant (‘provider interpretation’) or from groups such as patient registries that primarily provide phenotypic information from patients (‘phenotyping only’). ClinVar continues to make improvements to its search and retrieval functions. Several new fields are now indexed for more precise searching, and filters allow the user to narrow down a large set of search results.


Current protocols in human genetics | 2016

Using ClinVar as a Resource to Support Variant Interpretation

Steven M. Harrison; Erin Rooney Riggs; Donna Maglott; Jennifer M. Lee; Danielle R. Azzariti; Annie Niehaus; Erin M. Ramos; Christa Lese Martin; Melissa J. Landrum; Heidi L. Rehm

ClinVar is a freely accessible, public archive of reports of the relationships among genomic variants and phenotypes. To facilitate evaluation of the clinical significance of each variant, ClinVar aggregates submissions of the same variant, displays supporting data from each submission, and determines if the submitted clinical interpretations are conflicting or concordant. The unit describes how to (1) identify sequence and structural variants of interest in ClinVar by multiple searching approaches, including Variation Viewer and (2) understand the display of submissions to ClinVar and the evidence supporting each interpretation. By following this protocol, ClinVar users will be able to learn how to incorporate the wealth of resources and knowledge in ClinVar into variant curation and interpretation.


Arthritis Research & Therapy | 2000

Active synovial matrix metalloproteinase-2 is associated with radiographic erosions in patients with early synovitis

Raphaela Goldbach-Mansky; Jennifer M. Lee; Joseph Hoxworth; David L. Smith; Paul H. Duray; H. Ralph Schumacher; Cheryl Yarboro; John H. Klippel; David E. Kleiner; Hani El-Gabalawy


Cosmetics and toiletries | 2005

Recent polymer technologies for hair care

Bernice Ridley; Colleen Rocafort; Julie Shlepr; Julie Castner; Dale Willis; M. Creamer; A. Keenan; M. Merlau Johnson; A. Kar; A. Nakatani; D. Routzahn; C.E. Schwartz; Muyuan Wang; F. Zeng; J. Jachowicz; J. C. Chuang; T. Winkler; R. Mcmullen; Stan Chen; D. Streuli; Tom Burns; Jennifer M. Lee; Bethany K. Johnson; Erik Gyzen


Journal of Clinical Oncology | 2016

Landscape scanning of cancer gene panels: A report from the NIH Genetic Testing Registry (GTR).

Wendy S. Rubinstein; Adriana J. Malheiro; Brandi L. Kattman; Baoshan Gu; Vichet Hem; Kenneth S. Katz; Michael Ovetsky; Guangfeng Song; Ricardo Villamarín-Salomón; Craig Wallin; Donna Maglott; Jennifer M. Lee


Archive | 2013

Figure 1. [Overview of the flow of...].

Melissa J. Landrum; Jennifer M. Lee; George Riley; Wonhee Jang; Wendy S. Rubinstein; Deanna M. Church; Donna Maglott

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Donna Maglott

National Institutes of Health

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Melissa J. Landrum

National Institutes of Health

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Wendy S. Rubinstein

National Institutes of Health

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Wonhee Jang

National Institutes of Health

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George Riley

National Institutes of Health

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Deanna M. Church

National Institutes of Health

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Kenneth S. Katz

National Institutes of Health

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Michael Ovetsky

National Institutes of Health

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Adriana J. Malheiro

National Institutes of Health

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Baoshan Gu

National Institutes of Health

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