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Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

Reporter/Curator: Stephen J. Williams, Ph.D.

 

UPDATED on 9/6/2017

On 9/6/2017, Aviva Lev-Ari, PhD, RN had attend a talk by Paul Nioi, PhD, Amgen, at HMS, Harvard BioTechnology Club (GSAS).

Nioi discussed his 2016 paper in NEJM, 2016, 374:2131-2141

Variant ASGR1 Associated with a Reduced Risk of Coronary Artery Disease

Paul Nioi, Ph.D., Asgeir Sigurdsson, B.Sc., Gudmar Thorleifsson, Ph.D., Hannes Helgason, Ph.D., Arna B. Agustsdottir, B.Sc., Gudmundur L. Norddahl, Ph.D., Anna Helgadottir, M.D., Audur Magnusdottir, Ph.D., Aslaug Jonasdottir, M.Sc., Solveig Gretarsdottir, Ph.D., Ingileif Jonsdottir, Ph.D., Valgerdur Steinthorsdottir, Ph.D., Thorunn Rafnar, Ph.D., Dorine W. Swinkels, M.D., Ph.D., Tessel E. Galesloot, Ph.D., Niels Grarup, Ph.D., Torben Jørgensen, D.M.Sc., Henrik Vestergaard, D.M.Sc., Torben Hansen, Ph.D., Torsten Lauritzen, D.M.Sc., Allan Linneberg, Ph.D., Nele Friedrich, Ph.D., Nikolaj T. Krarup, Ph.D., Mogens Fenger, Ph.D., Ulrik Abildgaard, D.M.Sc., Peter R. Hansen, D.M.Sc., Anders M. Galløe, Ph.D., Peter S. Braund, Ph.D., Christopher P. Nelson, Ph.D., Alistair S. Hall, F.R.C.P., Michael J.A. Williams, M.D., Andre M. van Rij, M.D., Gregory T. Jones, Ph.D., Riyaz S. Patel, M.D., Allan I. Levey, M.D., Ph.D., Salim Hayek, M.D., Svati H. Shah, M.D., Muredach Reilly, M.B., B.Ch., Gudmundur I. Eyjolfsson, M.D., Olof Sigurdardottir, M.D., Ph.D., Isleifur Olafsson, M.D., Ph.D., Lambertus A. Kiemeney, Ph.D., Arshed A. Quyyumi, F.R.C.P., Daniel J. Rader, M.D., William E. Kraus, M.D., Nilesh J. Samani, F.R.C.P., Oluf Pedersen, D.M.Sc., Gudmundur Thorgeirsson, M.D., Ph.D., Gisli Masson, Ph.D., Hilma Holm, M.D., Daniel Gudbjartsson, Ph.D., Patrick Sulem, M.D., Unnur Thorsteinsdottir, Ph.D., and Kari Stefansson, M.D., Ph.D.

N Engl J Med 2016; 374:2131-2141June 2, 2016DOI: 10.1056/NEJMoa1508419

Abstract
Article
References
Citing Articles (22)
Metrics

BACKGROUND

Several sequence variants are known to have effects on serum levels of non–high-density lipoprotein (HDL) cholesterol that alter the risk of coronary artery disease.

METHODS

We sequenced the genomes of 2636 Icelanders and found variants that we then imputed into the genomes of approximately 398,000 Icelanders. We tested for association between these imputed variants and non-HDL cholesterol levels in 119,146 samples. We then performed replication testing in two populations of European descent. We assessed the effects of an implicated loss-of-function variant on the risk of coronary artery disease in 42,524 case patients and 249,414 controls from five European ancestry populations. An augmented set of genomes was screened for additional loss-of-function variants in a target gene. We evaluated the effect of an implicated variant on protein stability.

RESULTS

We found a rare noncoding 12-base-pair (bp) deletion (del12) in intron 4 of ASGR1, which encodes a subunit of the asialoglycoprotein receptor, a lectin that plays a role in the homeostasis of circulating glycoproteins. The del12 mutation activates a cryptic splice site, leading to a frameshift mutation and a premature stop codon that renders a truncated protein prone to degradation. Heterozygous carriers of the mutation (1 in 120 persons in our study population) had a lower level of non-HDL cholesterol than noncarriers, a difference of 15.3 mg per deciliter (0.40 mmol per liter) (P=1.0×10−16), and a lower risk of coronary artery disease (by 34%; 95% confidence interval, 21 to 45; P=4.0×10−6). In a larger set of sequenced samples from Icelanders, we found another loss-of-function ASGR1 variant (p.W158X, carried by 1 in 1850 persons) that was also associated with lower levels of non-HDL cholesterol (P=1.8×10−3).

CONCLUSIONS

ASGR1 haploinsufficiency was associated with reduced levels of non-HDL cholesterol and a reduced risk of coronary artery disease. (Funded by the National Institutes of Health and others.)

 

Amgen’s deCODE Genetics Publishes Largest Human Genome Population Study to Date

Mark Terry, BioSpace.com Breaking News Staff reported on results of one of the largest genome sequencing efforts to date, sequencing of the genomes of 2,636 people from Iceland by deCODE genetics, Inc., a division of Thousand Oaks, Calif.-based Amgen (AMGN).

Amgen had bought deCODE genetics Inc. in 2012, saving the company from bankruptcy.

There were a total of four studies, published on March 25, 2015 on the online version of Nature Genetics; titled “Large-scale whole-genome sequencing of the Icelandic population[1],” “Identification of a large set of rare complete human knockouts[2],” “The Y-chromosome point mutation rate in humans[3]” and “Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease[4].”

The project identified some new genetic variants which increase risk of Alzheimer’s disease and confirmed some variants known to increase risk of diabetes and atrial fibrillation. A more in-depth post will curate these findings but there was an interesting discrete geographic distribution of certain rare variants located around Iceland. The dataset offers a treasure trove of meaningful genetic information not only about the Icelandic population but offers numerous new targets for breast, ovarian cancer as well as Alzheimer’s disease.

View Mark Terry’s article here on Biospace.com.

“This work is a demonstration of the unique power sequencing gives us for learning more about the history of our species,” said Kari Stefansson, founder and chief executive officer of deCode and one of the lead authors in a statement, “and for contributing to new means of diagnosing, treating and preventing disease.”

The scale and ambition of the study is impressive, but perhaps more important, the research identified a new genetic variant that increases the risk of Alzheimer’s disease and already had identified an APP variant that is associated with decreased risk of Alzheimer’s Disease. It also confirmed variants that increase the risk of diabetes and a variant that results in atrial fibrillation.
The database of human genetic variation (dbSNP) contained over 50 million unique sequence variants yet this database only represents a small proportion of single nucleotide variants which is thought to exist. These “private” or rare variants undoubtedly contribute to important phenotypes, such as disease susceptibility. Non-SNV variants, like indels and structural variants, are also under-represented in public databases. The only way to fully elucidate the genetic basis of a trait is to consider all of these types of variants, and the only way to find them is by large-scale sequencing.

Curation of Population Genomic Sequencing Programs/Corporate Partnerships

Click on “Curation of genomic studies” below for full Table

Curation of genomic studies
Study Partners Population Enrolled Disease areas Analysis
Icelandic Genome

Project

deCODE/Amgen Icelandic 2,636 Variants related to: Alzheimer’s, cardiovascular, diabetes WES + EMR; blood samples
Genome Sequencing Study Geisinger Health System/Regeneron Northeast PA, USA 100,000 Variants related to hypercholestemia, autism, obesity, other diseases WES +EMR +MyCode;

– Blood samples

The 100,000 Genomes Project National Health Service/NHS Genome Centers/ 10 companies forming Gene Consortium including Abbvie, Alexion, AstraZeneca, Biogen, Dimension, GSK, Helomics, Roche,   Takeda, UCB Rare disorders population UK Starting to recruit 100,000 Initially rare diseases, cancer, infectious diseases WES of blood, saliva and tissue samples

Ref paper

Saudi Human Genome Program 7 centers across Saudi Arabia in conjunction with King Abdulaziz City Science & Tech., King Faisal Hospital & Research Centre/Life Technologies General population Saudi Arabia 20,000 genomes over three years First focus on rare severe early onset diseases: diabetes, deafness, cardiovascular, skeletal deformation Whole genome sequence blood samples + EMR
Genome of the Netherlands (GoNL) Consortium consortium of the UMCG,LUMCErasmus MCVU university and UMCU. Samples where contributed by LifeLinesThe Leiden Longevity StudyThe Netherlands Twin Registry (NTR), The Rotterdam studies, and The Genetic Research in Isolated Populations program. All the sequencing work is done by BGI Hong Kong. Families in Netherlands 769 Variants, SNV, indels, deletions from apparently healthy individuals, family trios Whole genome NGS of whole blood no EMR

Ref paper in Nat. Genetics

Ref paper describing project

Faroese FarGen project Privately funded Faroe Islands Faroese population 50,000 Small population allows for family analysis Combine NGS with EMR and genealogy reports
Personal Genome Project Canada $4000.00 fee from participants; collaboration with University of Toronto and SickKids Organization; technical assistance with Harvard Canadian Health System Goal: 100,000 ? just started no defined analysis goals yet Whole exome and medical records
Singapore Sequencing Malay Project (SSMP) Singapore Genome Variation Project

Singapore Pharmacogenomics Project

Malaysian 100 healthy Malays from Singapore Pop. Health Study Variant analysis Deep whole genome sequencing
GenomeDenmark four Danish universities (KU, AU, DTU and AAU), two hospitals (Herlev and Vendsyssel) and two private firms (Bavarian Nordic and BGI-Europe). 150 complete genomes; first 30 published in Nature Comm. ? See link
Neuromics Consortium University of Tübingen and 18 academic and industrial partners (see link for description) European and Australian 1,100 patients with neuro-

degenerative and neuro-

muscular disease

Moved from SNP to whole exome analysis Whole Exome, RNASeq

References

  1. Gudbjartsson DF, Helgason H, Gudjonsson SA, Zink F, Oddson A, Gylfason A, Besenbacher S, Magnusson G, Halldorsson BV, Hjartarson E et al: Large-scale whole-genome sequencing of the Icelandic population. Nature genetics 2015, advance online publication.
  2. Sulem P, Helgason H, Oddson A, Stefansson H, Gudjonsson SA, Zink F, Hjartarson E, Sigurdsson GT, Jonasdottir A, Jonasdottir A et al: Identification of a large set of rare complete human knockouts. Nature genetics 2015, advance online publication.
  3. Helgason A, Einarsson AW, Gumundsdottir VB, Sigursson A, Gunnarsdottir ED, Jagadeesan A, Ebenesersdottir SS, Kong A, Stefansson K: The Y-chromosome point mutation rate in humans. Nature genetics 2015, advance online publication.
  4. Steinberg S, Stefansson H, Jonsson T, Johannsdottir H, Ingason A, Helgason H, Sulem P, Magnusson OT, Gudjonsson SA, Unnsteinsdottir U et al: Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nature genetics 2015, advance online publication.

Other post related to DECODE, population genomics, and NGS on this site include:

Illumina Says 228,000 Human Genomes Will Be Sequenced in 2014

CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics

CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics and Computational Genomics – Part IIB

Human genome: UK to become world number 1 in DNA testing

Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

Sequencing the exomes of 1,100 patients with neurodegenerative and neuromuscular diseases: A consortium of 18 European and Australian institutions

University of California Santa Cruz’s Genomics Institute will create a Map of Human Genetic Variations

Three Ancestral Populations Contributed to Modern-day Europeans: Ancient Genome Analysis

Impact of evolutionary selection on functional regions: The imprint of evolutionary selection on ENCODE regulatory elements is manifested between species and within human populations

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1:00PM 11/13/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

1:00 p.m. Panel Discussion Genomics in Prenatal and Childhood Disorders

Genomics in Prenatal and Childhood Disorders

     Moderator:

David Sweetser, M.D., Ph.D.
Unit Chief, Division of Medical Genetics; Attending Physician in Pediatric Hematology/Oncology,
Massachusetts General Hospital for Children

Genomics revolutionized medicine and genetic variation in a larger scale

Cases one on Causing Autism – mutations in a gene of synapse formation, clinical trials

Treatment: IGF1

Genetics: embryo – implant only the healthy embryo – newborn comprehensive genetics testing in the medical record integrated – Standard language of GENE-DRUG interaction not only drug-drug interaction

Potential Harms: May or may not happen disease – stigma issues

Explaining to parents the conditions is very difficult for MDs

Panelists:

3. Diana Bianchi, M.D.
Executive Director, Mother Infant Research Institute;
Vice Chair for Research and Academic Affairs,
Department of Pediatrics; Attending Geneticists and Neonatologist;
Natalie V. Zucker Professor, Tufts University School of Medicine

Medical Geneticist – Pediatrics

  • Prenatal screening and diagnosis – chromosomal abnormality – Down Syndrome, testing is more precise 70% fewer procedures to correct defects due to screening prenatally.
  • Prenatal diagnostics — patient is not in front of us, ultrasound examination, options to terminate pregnancies, genetic counseling — changed due to Genomics
  • Prenatal treatment to down syndrome before the birth – Transcriptomic approach, treat the fetus prebirth
  • Standard of care – all pregnant women – must receive from MD the option for screening for down syndrome, it is a test positive or negative
  • NOW – DNA allows to test for  fetal sex, chromosome in maternal circulation fetal and maternal genetics — Mother may have chromosomal variation
  • high false positive – DNA for Down Syndrome, 97% effective Micro duplication only 5%
  • genetics information protection act – sue prospective employer using Genome, life insurance issues
  • most data available is on Down Syndrome, of all parents informed of a fetus with Down Syndrome – 40% continues the pregnancy
  • accuracy in testing, offering choice and treatment are LEADING principles NOT elimination of a disease (i.e. down syndromes)
  • in ten years — GENOME OF EVERY FETUS TO BE SEQUENCE

for reference see Prenatal Treatment of Down’s Syndrome: a Reality?

and ref list by Dr. Bianchi

2. Holmes Morton, M.D. @ClinicSpecChild
Medical Director, Clinic for Special Children

Small population in Lancaster, PA – risk for untreatable disease 52,000 screens 4.2 millions in US are screened Target mutation analysis, diagnosis very effectively. Harrisburg, PA – small scale natural history studies

Carrier testing offered in 70s. Discourages  from marriage, culture reaction is different. Working in the community, clinical practice using exon sequencing, combine population genetics and molecular biology.Translate Genomics to Clinical, small number of risk factors

History of genetics in population important to establish treatment

Upon birth, affected newborns get matching bone marrow transplant, thus, bypass stem cells – Gene therapy is another thing

1. Benjamin Solomon, Ph.D., M.D.
Chief, Division of Medical Genomics,
Inova Translational Medicine Institute

Longer term, statistical model in asthma research,  rigorous process on patient consent, life insurance, mutation that parents also have. Consequences: actionable findings are communicated
135 Genes – sequencing for some conditions
100,000 deliveries 10% ENTER THE STUDY, CASE BY CASE BASIS O PARTICIPATE, WHO SHOULD BE TESTED

Questions from the Podium

– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

@HarvardPMConf

#PMConf

@SachsAssociates

@MGH

@MassGeneral

@TuftsMedicalCtr

@MedscapePeds

@ClinicSpecChild

@InovaHealth

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Curator: Aviva Lev-Ari, PhD, RN

Population Genetics

HAPAA: a tool for ancestral haploblock reconstruction. Specifically, given the genotype  (for instance, as derived by an Illumina genotyping array) of an individual of admixed ancestry, find the source population for each segment of the individual’s genome.

Protein Interaction Networks

A tool for aligning multiple global protein interaction networks; Graemlin also supports search for homology between a query module of proteins and a database of interaction networks.

Machine Learning

CONTRA: Conditionally trained models for sequence analysis. SeeCONTRAlign, a protein sequence aligner with very high accuracy, especially in twilight alignments. See CONTRAfold, an RNA secondary structure prediction tool. Stay tuned for more…

RNA Structure Prediction

CONTRAfold: Prediction of RNA secondary structure with a Conditional Log-Linear model that relies on automatically trained parameters, rather than on a physics-based energy model of RNA folding.

Protein Alignment

CONTRAlign: A protein sequence aligner that users can optionally train on feature sets such as secondary structure and solvent accessibility; see the CONTRA project above.
A protein multiple sequence aligner that exhibits high accuracy on popular benchmarks.
A protein multiple aligner that automatically finds domain structures of sequences with shuffled and repeated domain architectures.

Motif Finding

MotifCut: a non-parametric graph-based motif finding algorithm.
MotifScan: a non-parametric method for representing motifs and scanning DNA sequences for known motifs.
 CompareProspector: motif finding with Gibbs sampling & alignment.

Genomic Alignment

Stanford ENCODE: Multiple Alignments of 1% of the Human genome.
Typhon: BLAST-like sequence search to a multiple alignments database.
LAGAN: tools for genomic alignment. These include the MLAGAN multiple alignment tool, and Shuffle-LAGAN for alignment with rearrangements.

Microarray Analysis

Application of Independent Component Analysis (ICA) to microarrays.

Researchers Hope New Database Becomes Universal Cancer Genomics Tool

Swiss scientists hope that a new online database called “arrayMap” will bring cancer genomics to the desktop, laptop, and tablet computers of pathologists and researchers everywhere.

The database combines genomic information from three sources: large repositories such as the NCBI Gene Expression Omnibus (GEO) and Cancer Genome Atlas (CGA); journal literature; and submissions from individual investigators. It incorporates more than 42,000 genomic copy number arrays—normal and abnormal DNA comparisons—from 195 cancer types.

“arrayMap includes a wider range of human cancer copy number samples than any single repository,” said principal investigator Michael Baudis, M.D. Ease of access, visualization, and data manipulation, he added, are top priorities in its ongoing development.

A product of the University of Zurich Institute for Molecular Life Sciences, where Baudis researches bioinformatics and oncogenomics, arrayMap illustrates the importance of copy number abnormalities (CNA)—dysfunctional DNA gains or losses that visibly lengthen or shorten certain chromosomes—in the diagnosis, staging, and treatment of various malignancies.

“I have this particular tumor type—are there any CNAs in it that can tell me anything about prognosis or treatment?” said Michael Rossi, Ph.D., director of the Winship Cancer Institute cancer genomics program at the Emory University School of Medicine in Atlanta. “Data mining tools like arrayMap are incredibly useful to help answer such questions.”

arrayMap – genomic arrays for copy number profiling in human cancer

arrayMap is a curated reference database and bioinformatics resource targeting copy number profiling data in human cancer. The arrayMap database provides an entry point for meta-analysis and systems level data integration of high-resolution oncogenomic CNA data. The current data reflects:

  • 42875 genomic copy number arrays
  • 634 experimental series
  • 256 array platforms
  • 197 ICD-O cancer entities
  • 480 publications (Pubmed entries)

For the majority of the samples, probe level visualization as well as customized data representation facilitate gene level and genome wide data review. Results from multi-case selections can be connected to downstream data analysis and visualization tools, as we provide through our Progenetix project.

arrayMap is developed by the group “Theoretical Cytogenetics and Oncogenomics” at the Institute of Molecular Life Sciences of the University of Zurich.

These tools were developed for our research projects. You are welcome to try them out, but there is only sparse documentation. If more support and/or custom analysis is needed, please contact Michael Baudis regarding a collaborative project.

MIT: A New Approach Uses Compression to Speed Up Genome Analysis

Public-Domain Computing Resources

Structural Bioinformatics

The BetaWrap program detects the right-handed parallel beta-helix super-secondary structural motif in primary amino acid sequences by using beta-strand interactions learned from non-beta-helix structures.
Wrap-and-pack detects beta-trefoils in protein sequences by using both pairwise beta-strand interactions and 3-D energetic packing information
The BetaWrapPro program predicts right-handed beta-helices and beta-trefoils by using both sequence profiles and pairwise beta-strand interactions, and returns coordinates for the structure.
The MSARi program indentifies conserved RNA secondary structure in non-coding RNA genes and mRNAs by searching multiple sequence alignments of a large set of candidate catalogs for correlated arrangements of reverse-complementary regions
The Paircoil2 program predicts coiled-coil domains in protein sequences by using pairwise residue correlations obtained from a coiled-coil database. The original Paircoil program is still available for use.
The MultiCoil program predicts the location of coiled-coil regions in amino acid sequences and classifies the predictions as dimeric or trimeric. An updated version, Multicoil2, will soon be available.
The LearnCoil Histidase Kinase program uses an iterative learning algorithm to detect possible coiled-coil domains in histidase kinase receptors.
The LearnCoil-VMF program uses an iterative learning algorithm to detect coiled-coil-like regions in viral membrane-fusion proteins.
The Trilogy program discovers novel sequence-structure patterns in proteins by exhaustively searching through three-residue motifs using both sequence and structure information.
The ChainTweak program efficiently samples from the neighborhood of a given base configuration by iteratively modifying a conformation using a dihedral angle representation.
The TreePack program uses a tree-decomposition based algorithm to solve the side-chain packing problem more efficiently. This algorithm is more efficient than SCWRL 3.0 while maintaining the same level of accuracy.
PartiFold: Ensemble prediction of transmembrane protein structures. Using statistical mechanics principles, partiFold computes residue contact probabilities and sample super-secondary structures from sequence only.
tFolder: Prediction of beta sheet folding pathways. Predict a coarse grained representation of the folding pathway of beta sheet proteins in a couple of minutes.
RNAmutants: Algorithms for exploring the RNA mutational landscape.Predict the effect of mutations on structures and reciprocally the influence of structures on mutations. A tool for molecular evolution studies and RNA design.
AmyloidMutants is a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability.

Genomics

GLASS aligns large orthologous genomic regions using an iterative global alignment system. Rosetta identifies genes based on conservation of exonic features in sequences aligned by GLASS.
RNAiCut – Automated Detection of Significant Genes from Functional Genomic Screens.
MinoTar – Predict microRNA Targets in Coding Sequence.

Systems Biology

The Struct2Net program predicts protein-protein interactions (PPI) by integrating structure-based information with other functional annotations, e.g. GO, co-expression and co-localization etc. The structure-based protein interaction prediction is conducted using a protein threading server RAPTOR plus logistic regression.
IsoRank is an algorithm for global alignment of multiple protein-protein interaction (PPI) networks. The intuition is that a protein in one PPI network is a good match for a protein in another network if the former’s neighbors are good matches for the latter’s neighbors.

Other

t-sample is an online algorithm for time-series experiments that allows an experimenter to determine which biological samples should be hybridized to arrays to recover expression profiles within a given error bound.

http://people.csail.mit.edu/bab/computing_new.html#systems

Compressive genomics

http://www.nature.com/nbt/journal/v30/n7/abs/nbt.2241.html

Nature Biotechnology 30, 627–630 (2012) doi:10.1038/nbt.2241

Published online 10 July 2012

STANFORD UNIVERSITY: Resources

BMIR is committed to the development of research tools as part of its goal to provide reusable, computational building blocks to facilitate the development of a vast array of systems. Some of these resources are described below.

Resources

The National Center for Biomedical Ontology (NCBO)

NCBO

The National Center for Biomedical Ontology is a consortium of leading biologists, clinicians, informaticians, and ontologists who develop innovative technology and methods that allow scientists to create, disseminate, and manage biomedical information and knowledge in machine-processable form.

visit site

Protégé

Protege Logo

Protégé is a free, open-source platform that provides its community of more than 80,000 users with a suite of tools to construct domain models and knowledge-based applications with ontologies.

visit site

PharmGKB

PharmGKB

PharmGKB curates information that establishes knowledge about the relationships among drugs, diseases and genes, including their variations and gene products. Our mission is to catalyze pharmacogenomics research.

visit site

Simbios

Simbios Logo

About Simbios

Simbios, the National NIH Center for Physics-based Simulation of Biological Structures is devoted to helping biomedical researchers understand biological form and function. It provides infrastructure, software, and training to assist users as they create novel drugs, synthetic tissues, medical devices, and surgical interventions.

Simbios scientists investigate structure-function studies on a wide scale of biology – from molecules to organisms, and are currently focusing on challenging biological problems in RNA folding, myosin dynamics, neuromuscular biomechanics and cardiovascular dynamics.

visit site

Stanford BioMedical Informatics Research (BMIR) – Publications by Project

There are 8 publications for the project “Genomic Nosology for Medicine (GNOMED)”.

BMIR-2009-1362
Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and ‘‘antibodyome’’ measures
L. Li, P. Wadia, M. Sarwal, N. Kambham, T. Sigdel, D. B. Miklos, R. Chen, M. Naesens, A. J. Butte
PNAS, 106, 11, 4148-4153. Published in 2009
BMIR-2008-1338
Using SNOMED-CT For Translational Genomics Data Integration
J. Dudley, D. P. Chen, A. J. Butte
Ronald Cornet, Kent Spackman (eds.): Representing and sharing knowledge using SNOMED. Proceedings of the 3rd International Conference on Knowledge Rep, Pheonix (AZ), USA, CEUR Workshop Proceedings, ISSN 1613-0073, online CEUR-WS.org/Vol-410/, 91-96. Published in 2008
BMIR-2008-1303
The Ultimate Model Organism
A. J. Butte
Science, 320, 5874, 325-327. Published in 2008
BMIR-2008-1293
Novel Integration of Hopsital Electronic Medical Records and Gene Expression Measurements to Identify Genetic Markers of Maturation
D. P. Chen, S. C. Weber, P. S. Constantinou, T. A. Ferris, H. J. Lowe, A. J. Butte
Pacific Symposium on Biocomputing, Big Island, Hawaii, 13, 243-254. Published in 2008
BMIR-2008-1292
Enabling Integrative Genomic Analysis of High-Impact Human Diseases through Text Mining
J. Dudley, A. J. Butte
Pacific Symposium on Biocomputing, Big Island, Hawaii, 13, 580-591. Published in 2008
BMIR-2007-1297
Methodologies for Extracting Functional Pharmacogenomic Experiments from International Repository
Y. Lin, A. P. Chiang, P. Yao, R. Chen, A. J. Butte, R. S. Lin
AMIA Annual Symposium, Chicago, IL, 463-467. Published in 2007
BMIR-2007-1296
Clinical Arrays of Laboratory Measures, or “Clinarrays”, Built from an Electronic Health Record Enable Disease Subtyping by Severity
D. P. Chen, S. C. Weber, P. S. Constantinou, T. A. Ferris, H. J. Lowe, A. J. Butte
AMIA Annual Symposium, Chicago, IL, 115-119. Published in 2007
BMIR-2006-1232
Finding Disease-Related Genomic Experiments Within an International Repository: First Steps in Translational Bioinformatics
A. J. Butte, R. Chen
Annual Symposium of the American Medical Informatics Association, Washington, D.C., 106-10. Published in 2006
http://bmir.stanford.edu/publications/project.php/genomic_nosology_for_medicine_gnomed

Featured Publications

BMIR-2011-1468
The National Center for Biomedical Ontology
M. A. Musen, N. F. Noy, C. G. Chute, M. A. Storey, B. Smith, N. H. Shah
. Published in 2011
BMIR-2009-1378
Prototyping a Biomedical Ontology Recommender Service
C. Jonquet, N. H. Shah, M. A. Musen
Bio-Ontologies: Knowledge in Biology, SIG, ISMB ECCB 2009, Stockholm, Sweden. Published in 2009
BMIR-2009-1376
Translational bioinformatics applications in genome medicine
A. J. Butte
Genome Medicine, 1, 6, 64. Published in 2009
BMIR-2009-1362
Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and ‘‘antibodyome’’ measures
L. Li, P. Wadia, M. Sarwal, N. Kambham, T. Sigdel, D. B. Miklos, R. Chen, M. Naesens, A. J. Butte
PNAS, 106, 11, 4148-4153. Published in 2009
BMIR-2009-1361
Technology for Building Intelligent Systems: From Psychology to Engineering
M. A. Musen
Modeling Complex Systems, Bill Shuart, Will Spaulding and Jeffrey Poland, U Nebraska P, Lincoln, Nebraska, Vol 52 of the Nebraska Symposium on Motivation, 145-184. Published in 2009
BMIR-2009-1358
Software-Engineering Challenges of Building and Deploying Reusable Problem Solvers
M. J. O’Connor, C. I. Nyulas, A. Okhmatovskaia, D. Buckeridge, S. W. Tu, M. A. Musen
Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 24, 3. Published in 2009
BMIR-2009-1355
Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events
A. P. Chiang, A. J. Butte
Clinical Pharmacology and Therapeutics, 28 January 2009, Advance online publication, doi:10.1038/clpt.2008.274. Published in 2009
BMIR-2009-1318
Knowledge-Data Integration for Temporal Reasoning in a Clinical Trial System
M. J. O’Connor, R. D. Shankar, D. B. Parrish, A. K. Das
International Journal of Medical Informatics, 78, Suppl. 1, S77-S85. Published in 2009
BMIR-2008-1353
GeneChaser: Identifying all biological and clinical conditions in which genes of interest are differentially expressed
R. Chen, R. Mallelwar, A. Thosar, S. Venkatasubrahmanyam, A. J. Butte
BMC Bioinformatics, 9, 1, 548. (doi:10.1186/1471-2105-9-548). Published in 2008
BMIR-2008-1346
FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease
R. Chen, A. A. Morgan, J. Dudley, A. M. Deshpande, L. Li, K. Kodama, A. P. Chiang, A. J. Butte
Genome Biology, 9, 12, R170 (doi:10.1186/gb-2008-9-12-r170). Published in 2008
BMIR-2008-1341
Translational Bioinformatics: Coming of Age
A. J. Butte
Journal of the American Medical Informatics Association, JAMIA, 15, 6, 709-14. Published in 2008
BMIR-2008-1329
An Ontology-Driven Framework for Deploying JADE Agent Systems
C. I. Nyulas, M. J. O’Connor, S. W. Tu, A. Okhmatovskaia, D. Buckeridge, M. A. Musen
IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Sydney, Australia, 2, 573-577. Published in 2008
BMIR-2008-1322
Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-Detection Algorithms
D. Buckeridge, A. Okhmatovskaia, S. W. Tu, C. I. Nyulas, M. J. O’Connor, M. A. Musen
Journal of the American Medical Informatics Association, 15, 6, 760-769. Published in 2008
BMIR-2008-1319
Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer’s Disease
K. S. Supekar, V. Menon, M. A. Musen, D. L. Rubin, M. Greicius
Public Library of Science-Computational Biology., PLoS Computational Biology, June 2008. Published in 2008
BMIR-2008-1315
Medical Imaging on the Semantic Web: Annotation and Image Markup
D. L. Rubin, P. Mongkolwat, V. Kleper, K. S. Supekar, D. S. Channin
AAAI Spring Symposium Series, Semantic Scientific Knowledge Integration, Stanford. Published in 2008
BMIR-2008-1303
The Ultimate Model Organism
A. J. Butte
Science, 320, 5874, 325-327. Published in 2008
BMIR-2008-1298
BioPortal: A Web Portal to Biomedical Ontologies
D. L. Rubin, D. de Abreu Moreira, P. P. Kanjamala, M. A. Musen
AAAI Spring Symposium Series, Symbiotic Relationships between Semantic Web and Knowledge Engineering, Stanford University, (in press). Published in 2008
BMIR-2007-1295
AILUN: reannotating gene expression data automatically
R. Chen, L. Li, A. J. Butte
Nature Methods, 4, 11, 879. Published in 2007
BMIR-2007-1281
Evaluation and Integration of 49 Genome-wide Experiments and the Prediction of Previously Unknown Obesity-related Genes
S. B. English, A. J. Butte
Bioinformatics, Epub. Published in 2007
BMIR-2007-1261
Protege: A Tool for Managing and Using Terminology in Radiology Applications
D. L. Rubin, N. F. Noy, M. A. Musen
Journal of Digital Imaging, J Digit Imaging. Published in 2007
BMIR-2007-1244
Efficiently Querying Relational Databases using OWL and SWRL
M. J. O’Connor, R. D. Shankar, S. W. Tu, C. I. Nyulas, A. K. Das, M. A. Musen
The First International Conference on Web Reasoning and Rule Systems, Innsbruck, Austria, Springer, LNCS 4524, 361-363. Published in 2007
BMIR-2006-1090
Creation and implications of a phenome-genome network
A. J. Butte, I. S. Kohane
Nature Biotechnology, 24, 1, 55 – 62. Published in 2006
http://bmir.stanford.edu/publications/

NATIONAL CENTERS FOR BIOMEDICAL COMPUTING

SimBioS
National Center for Simulation of Biological Structures (SimBioS) at Stanford University

MAGNet
National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet) at Columbia University

NA-MIC Logo
National Alliance for Medical Image Computing (NA-MIC) at Brigham and Women’s Hospital, Boston, MA

I2B2
Integrating Biology and the Bedside (I2B2) at Brigham and Women’s Hospital, Boston, MA

NCBO
National Center for Biomedical Ontology (NCBO) at Stanford University

IDASH
Integrate Data for Analysis, Anonymization, and Sharing (IDASH) at the University of California, San Diego

http://www.ncbcs.org/

 

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