Nucleic Acids Research

Nucleic Acids Research 2005 33(Database Issue):D562-D566; doi:10.1093/nar/gki022

This Article Abstract Print PDF (504K) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Archive Download to citation manager Commercial Re-use Guidelines for Open Access NAR Content Google Scholar Articles by Barrett, T. Articles by Edgar, R. Search for Related Content PubMed PubMed Citation Articles by Barrett, T. Articles by Edgar, R. Social Bookmarking          What's this?

Nucleic Acids Research, 2005, Vol. 33, Database issue D562-D566
© 2005, the authors
Nucleic Acids Research, Vol. 33, Database issue © Oxford University Press 2005; all rights reserved

NCBI GEO: mining millions of expression profiles—database and tools

Tanya Barrett, Tugba O. Suzek, Dennis B. Troup, Stephen E. Wilhite, Wing-Chi Ngau, Pierre Ledoux, Dmitry Rudnev, Alex E. Lash, Wataru Fujibuchi and Ron Edgar^*

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 45 Center Drive, Bethesda, MD, USA

^* To whom correspondence should be addressed. Tel: +301 435 3449; Fax: +301 480 0109; Email: edgar{at}ncbi.nlm.nih.gov
Present addresses: Alex E. Lash, Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
Wataru Fujibuchi, Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

Received September 2, 2004; Accepted September 21, 2004

	ABSTRACT

TOP ABSTRACT INTRODUCTION REPOSITORY ORGANIZATION AND DATA... RETRIEVAL, QUERY AND ANALYSIS CONCLUSION REFERENCES

 
The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30 000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION REPOSITORY ORGANIZATION AND DATA... RETRIEVAL, QUERY AND ANALYSIS CONCLUSION REFERENCES

 
Since 2000, the Gene Expression Omnibus (GEO) has served as a public repository for high-throughput molecular abundance experimental data, providing free distribution and shared access to comprehensive datasets (1). These data include single and multiple channel microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules. Data generated by innovative applications of microarray technology are also accepted, e.g. chromatin immunoprecipitation (ChIP-chips) for identifying protein-binding DNA regions and tiling arrays for genome annotation. Data from non-array-based high-throughput functional genomics and proteomics technologies are also archived, including serial analysis of gene expression (SAGE), and mass spectrometry peptide profiling.

The initial aim of GEO—to function as a robust, versatile high-throughput data repository—has been accomplished. As of fall 2004, GEO holds over 30 000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms, submitted by over 600 researchers. Typically, GEO records are accessed over 15 000 times each weekday by over 1000 unique users, and bulk FTP downloads average 30 000 per month. Although GEO represents a huge reservoir of gene expression data that is widely used by the scientific community, it was recognized that the full potential of the repository could only be achieved by making these data easy to search and analyze, even by individuals having little experience in the field, without the need of massive data downloads. This paper describes database developments and tools that enable effective exploration, query and visualization of hundreds of experiments and millions of gene expression profiles using user-friendly Web-based interfaces.

	REPOSITORY ORGANIZATION AND DATA FLOW

TOP ABSTRACT INTRODUCTION REPOSITORY ORGANIZATION AND DATA... RETRIEVAL, QUERY AND ANALYSIS CONCLUSION REFERENCES

 
The principle architecture of the GEO database remains as described previously (1). Briefly, data submitted to GEO are stored in a relational database partitioned into three upper-level entity types: Platform, Sample and Series. A Platform describes the list of elements (e.g. oligonucleotide probesets, cDNAs, SAGE tags, antibodies) being assayed or that may be detected and quantified in that experiment. A Sample references a Platform and describes the abundance measurement of each feature element for a single hybridization or experimental condition. A Series brings together related Samples that make up an experiment and may include tables of extracted summary sets of significant genes or analysis as defined by the submitter. Each individual entity is assigned a unique and stable accession number; the accession number prefix indicates whether the record is a GEO Platform (GPL), Sample (GSM), or Series (GSE).

Unlike metadata that are stored in designated fields within database tables, Platform and Sample data tables are not fully granulated, but are stored as text objects. This design allows GEO to remain adaptable and responsive to developing technology trends, as it permits optimal flexibility in the quantity and type of data stored. For example, Platform elements may be described by any number of auxiliary attributes, and Sample data tables may contain all classes of supplementary and supporting measurements and calculations. The data within these tables may be extracted for higher-level rendering, indexing, search and retrieval purposes. Recent enhancements to the database include addition of supplementary metadata fields intended to facilitate and encourage MIAME (Minimum Information About Microarray Experiment) compliant data submissions (2), and acceptance of raw data contributions for storage and retrieval, e.g. Affymetrix .cel files or cDNA array scanned images.

Submission and standards
GEO aims at a balance between a submission procedure that is user-friendly and not overly rigid, while still encouraging high-quality data and a high level of experimental annotation. An infrastructure is provided so that submitters can present their data in a MIAME-compliant fashion (2). Submissions are validated syntactically according to a limited set of criteria and are subject to basic curation, assuring that records contain meaningful information and are organized correctly. Data depositors retain editorial control and are responsible for the content and quality of their records as outlined in the open letter published recently by the Microarray Gene Expression Data (MGED) Society board (3). GEO obviously could not attempt to independently verify the validity, merit, quality or biological significance of submitted data.

Once submitters establish their own private GEO accounts, there are three ways in which data may be deposited with GEO:

1. Interactive web-based forms. For each Platform and Sample submission, a text tab-delimited data table file is uploaded and validated. Metadata fields are entered interactively through a series of Web forms. This process is straightforward and is most useful when submitting relatively few entries. Updates to individual records may also be performed using similar interactive Web forms.
   
2. Direct submission using Simple Omnibus Format in text or SOFT format. SOFT was designed for rapid batch submission of data, and files may be easily produced from common spreadsheet and database applications. A single SOFT file can hold both data and metadata for multiple Platforms, Samples and Series, and can be uploaded directly to the database. Batch updates may also be quickly and efficiently performed using SOFT format. Detailed information on SOFT format is available on the GEO Web site.
   
3. Submitters may FTP files in valid MAGE_ML (4) format to GEO.
   

Records may remain private for several months, typically pending journal publication. Manuscript reviewers may gain confidential access to data prior to publication using read-only passwords.

DataSets and profiles
It was evident early-on that retrieval of data by means of accession number alone, or browsing by categories, would be insufficient to allow effective data mining and essential linkage between expression data and other sequence information and publication resources. High-throughput molecular abundance data are inherently more complex than other data types, such as sequence or bibliographic records; the strong association between measured entities and the biological and statistical context in which they were extracted must be considered; GEO stores a wide assortment of high-throughput experimental data processed by multiple means and analyzed by various methods. To address these issues, an additional level of curation was introduced where submitted samples are assembled into biologically meaningful and statistically comparable GEO DataSets (GDS). GDS records provide a coherent synopsis about an experiment, and serve as the basis for downstream data mining and display tools.

Samples within a GDS refer to the same Platform, that is, a common set of elements are assayed. Calculations are computed on the ‘value’ column extracted from original Sample data tables. These value measurements are calculated in an equivalent manner for each Sample within a GDS, i.e. considerations such as background processing and normalization are consistent across the GDS. Samples within DataSets are further grouped and classified into subsets according to the experimental variables under examination in the study, for instance ‘tissue’ or ‘strain’.

The Sample-centric tabular data under the control of the GDS upper level object then undergoes a final re-factoring into a gene-oriented view, and the results are indexed into a query engine and retrieval system, and display suites. The NCBI Entrez (5) database system is used as grounds for the query engine and retrieval system; two databases are defined:

1. GEO DataSets stores all experimental metadata, providing an ‘experiment-centric’ perspective of GEO data. The query interface is accessible from the GEO homepage or directly at http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=gds.
   
2. GEO Profiles stores individual gene expression profiles, providing a ‘gene-centric’ perspective of GEO data. The query interface is accessible from the GEO homepage or directly at http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=geo.
   

Thus, each DataSet entity defines a single experiment in GEO DataSets, and each DataSet parents a multitude of profile entities in GEO Profiles (Figure 1).

View larger version (27K): [in this window] [in a new window]   Figure 1. Schematic diagram of the relationships between GEO Platform, Sample, DataSet and Profiles. For each gene on a Platform (e.g. Gene A), multiple Sample measurement values are generated (Sample1–Sample3). Related Samples make up a DataSet, from which multiple, individual gene profile entities are generated.

 

	RETRIEVAL, QUERY AND ANALYSIS

TOP ABSTRACT INTRODUCTION REPOSITORY ORGANIZATION AND DATA... RETRIEVAL, QUERY AND ANALYSIS CONCLUSION REFERENCES

 
Basic retrieval
There are several ways and formats in which GEO data may be retrieved:

1. Individual Platform, Sample, Series and GDS records can be accessed directly on the Web via their GEO accession number. Related records are intra-linked on the GEO site, such that one may conveniently navigate to associated Platform, Sample, Series and GDS records.
   
2. GDS records may be browsed by title, type, Platform or organism at http://www.ncbi.nlm.nih.gov/geo/gds/gds_browse.cgi. User-submitted records may also be browsed by category or submitter.
   
3. All user-submitted records, GDS value matrices with annotation, and raw data are available for bulk download via FTP. User-submitted records are grouped as compressed Series and Platform ‘family’ files, which incorporate all related accessions. Equivalent files are available for individual download from each record on the Web.
   

Query and mining
Effective searches may be accomplished by querying Entrez GEO DataSets and/or Entrez GEO Profiles. As with other NCBI Entrez databases (5), both trivial and sophisticated query and mining is achieved using Boolean phrases that may be restricted to, or combined with, a number of supported attribute fields.

Experiments of interest may be located by searching GEO DataSets for attributes such as experimental variable information, technology type, author, organism or any text keywords from the GDS description or original submitter-supplied Sample and Series records. For example, all dual channel nucleotide microarray experimental DataSets exploring metastasis in humans can be located using the query ‘dual channel[Experiment Type] AND metastasis AND human[Organism]’. Retrievals display the DataSet title, a brief experiment description, taxonomy, experimental variable types and links to the parent Platform, reference Series record and the complete GDS record. Once a relevant DataSet has been identified, users may go on to further explore that experiment either by taking advantage of the various supplementary tools on the GDS record page (Figure 2C) or by restricting subsequent GEO Profiles searches to that DataSet.

View larger version (66K): [in this window] [in a new window]   Figure 2. Selection of GEO web screenshots and how they link with each other. (A) GEO Profiles retrieval results; each entity includes sequence identifier and DataSet information, and a thumbnail profile image. Links to other Entrez databases or related profiles are provided above the thumbnail image. (B) Expanded profile chart depicts values (red bars) and rank (blue bars) information for one gene across each Sample in a GEO DataSet. Experimental subset groupings are reflected in labels at foot of chart. (C) DataSet record includes experiment summary information, DataSet subset classifications, and access to data mining features such as hierarchical cluster heat map and ‘Query subset A versus B’ tool. (D) DataSet hierarchical cluster heat map calculated by un-centered correlation coefficient/average linkage option. Regions of interest are selected using the red image cropper box, then either expanded to view Sample and gene annotation, downloaded, charted as line plots, or linked directly to corresponding Entrez GEO Profiles records.

 
The elemental unit in GEO Profiles is a gene, sequence or other reporter molecule, and its traced behavior along the measured conditions of the experiment, hence a ‘profile’. GEO Profiles are annotated in accordance with concurrent Entrez Gene and UniGene resources, and may be queried for attributes such as gene name, GenBank accession number, SAGE tag, GDS accession, DataSet description or profiles flagged as having significant effects with regards to specific experimental variables. For example, the query ‘Type 1 diabetes[GDS Text] AND apolipoprotein[Gene Description] NOT Homo sapiens[Organism]’ retrieves all apolipoprotein-related gene profiles in Type 1 diabetes-related datasets in organisms other than human. Query results display reporter annotation, brief experimental information, taxonomy and a bar-graph thumbnail image of the profile (Figure 2A). The thumbnail images are helpful for rapid batch profile scanning and comparison. A click on a thumbnail reveals the profile details (Figure 2B). Gene expression values extracted from original sample records are represented by red bars. Blue bars represent intra-sample percentile rank information, providing an indication of the relative expression level of that gene compared to all other genes on the array. Experimental structure is reflected in subgroup labels along the bottom of each chart allowing even complex experiments involving multiple and overlapping subset types to be clearly visualized. Standard GEO Profile retrievals are ordered according to subset effect flags by default, bringing potentially significant and interesting profiles to the fore. However, users may select alternative sorting schema based on mean value, deviation or outliers.

Selected GEO Profile entities possess intra-database links. ‘Profile neighbors’ connects genes that show a similar profile shape within a DataSet, as calculated by Pearson correlation coefficients. ‘Sequence neighbors’ retrieves related profiles based on nucleotide sequence similarity by BLAST (6) across all DataSets, and ‘Homologs’ retrieves profiles of genes belonging to the same HomoloGene group. Sequence and profile neighbor retrievals are weighted by presumed relevance, and are subject to cutoffs so as to limit the number of links that can be managed.

Entrez GEO DataSets and GEO Profiles are fully integrated with each other, as well as with other NCBI Entrez databases (7). Where possible, links are provided to GenBank, PubMed, Gene, UniGene, OMIM, Homologene, SNP, Taxonomy, SAGEMap and MapViewer. These links are reciprocal, meaning they can be traced back to GEO from any of the above resources, and facilitate seamless navigation and cross-referencing between databases.

Supplementary features
In addition to the Entrez query system, several supporting tools and features are provided to assist with enhanced mining and visualization of data:

1. Cluster heat maps. Pre-computed sample and gene hierarchical cluster heat maps are provided for most DataSets (Figure 2). Users are given the option to view clusters calculated using a variety of distance metrics (Euclidean distance, Pearson correlation or un-centered correlation coefficient) and clustering methods (single linkage, complete linkage or average linkage). Multiple cluster portions of interest may be selected, expanded, downloaded, charted as line plots or linked directly to Entrez GEO-Profile records.
   
2. Query subset A versus B. This feature identifies gene expression profiles of interest by calculating average rank or value differences between experimental subsets within a DataSet. For example, a user can specify that he wants to locate genes displaying 10-fold higher expression values in time point ‘A’ compared to time point ‘B’, and he will be directed to profiles matching those criteria.
   
3. Subset effects. Profiles are flagged if they display significant differences in expression values or ranks between subsets. This feature retrieves all profiles, either DataSet-specific or across all DataSets, that are flagged as having significant profiles with respect to a specific experimental variable, e.g. ‘age’ or ‘strain’.
   
4. Value distribution. Box and whisker plots for each Sample within a DataSet are presented, allowing an overview of the distribution of values across a DataSet.
   
5. GEO BLAST. This interface allows users to search for GEO Profiles of interest based on nucleotide sequence similarity using BLAST. The GEO BLAST database contains all GenBank sequences represented in GEO DataSets. Furthermore, standard BLAST output as performed using NCBI's BLAST interface, displays ‘E’ icon links where appropriate, linking directly to GEO Profiles expression data.
   

	CONCLUSION

TOP ABSTRACT INTRODUCTION REPOSITORY ORGANIZATION AND DATA... RETRIEVAL, QUERY AND ANALYSIS CONCLUSION REFERENCES

 
GEO represents a large compendium of gene expression data, addressing a wide range of biological issues across many organisms. The database already contains approximately half a billion measurements, and continues to grow at an average rate of >20 million per month. While very valuable, these data are not immediately interpretable or human readable in the raw form. To address this issue, database applications have been developed to facilitate complex data mining by providing query capabilities and concise displays that allow human scanning and data reduction. Tools are provided to help identify and categorize gene and sample relationships. Additional context is provided through comprehensive integration with sequence information, mapping and bibliographic resources.

As an open repository, the data in GEO have typically been analyzed and studied, and in most cases, the results published in journals. Nonetheless, pooling disparate data into one location and organizing them to be analyzable and cross-comparable using common interfaces adds a valuable analytic layer not attainable when considering individual experiments. Mining GEO data can provide clues as to the function of uncharacterized genes and genetic networks by examining spatial and temporal expression patterns (8–10), and co-regulation with well-characterized markers. Cross-comparison of independently generated but experimentally similar datasets can corroborate interesting gene expression trends that may be overlooked in one experiment alone (11). The GEO database and tools may also substantiate laboratory findings, or suggest supportive or negating evidence for research proposals and hypotheses (12). Reanalysis and reinterpretation of GEO data can provide valuable insights into other fields (13,14). Such opportunities for discovery will only increase as the database continues to grow in size and diversity.

Future plans for GEO are continued development of submission and retrieval formats, further integration with NCBI resources, and enhancements to data visualization and mining features. The features described herein are mostly relevant to gene expression studies; separate tools and graphical representations specific to other data types, such as proteomic technologies and comparative genomic hybridization, are also planned.

	ACKNOWLEDGEMENTS

 
We thank the Entrez/PubMed development team for ongoing support. Michael Domrachev implemented the first GEO database and provided support while moving to the MIAME enhanced schema. We thank Sergey Kurdin for web page designs and JS code, Todd Groesbeck for generation of manuscript figures, and Jim Ostell and David Lipman for advising on this project and review of this manuscript.

	Notes

 
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use permissions, please contact journals.permissions{at}oupjournals.org.

	REFERENCES

TOP ABSTRACT INTRODUCTION REPOSITORY ORGANIZATION AND DATA... RETRIEVAL, QUERY AND ANALYSIS CONCLUSION REFERENCES

 

1. Edgar,R., Domrachev,M. and Lash,A.E. ( (2002) ) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res., , 30, , 207–210.[Abstract/Free Full Text] .

2. Brazma,A., Hingamp,P., Quackenbush,J., Sherlock,G., Spellman,P., Stoeckert,C., Aach,J., Ansorge,W., Ball,C.A., Causton,H.C. et al. ( (2001) ) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genet., , 29, , 365–371.[CrossRef][Web of Science][Medline] .

3. Ball,C., Brazma,A., Causton,H., Chervitz,S., Edgar,R., Hingamp,P., Matese,J.C., Parkinson,H., Quackenbush,J., Ringwald,M. et al. ( (2004) ) Microarray Data Standards: An Open Letter. PLoS Biol., , 2, , 23–24.[CrossRef] .

4. Spellman,P.T., Miller,M., Stewart,J., Troup,C., Sarkans,U., Chervitz,S., Bernhart,D., Sherlock,G., Ball,C., Lepage,M. et al. ( (2002) ) Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol., , 3, , RESEARCH0046. .

5. Schuler,G.D., Epstein,J.A., Ohkawa,H. and Kans,J.A. ( (1996) ) Entrez: molecular biology database and retrieval system. Methods Enzymol., , 266, , 141–162.[Web of Science][Medline] .

6. Altschul,S.F., Gish,W., Miller,W., Myers,E.W. and Lipman,D.J. ( (1990) ) Basic local alignment search tool. J. Mol. Biol., , 215, , 403–410.[CrossRef][Web of Science][Medline] .

7. Wheeler,D.L., Church,D.M., Edgar,R., Federhen,S., Helmberg,W., Madden,T.L., Pontius,J.U., Schuler,G.D., Schriml,L.M., Sequeira,E. et al. ( (2004) ) Database resources of the National Center for Biotechnology Information: update. Nucleic Acids Res., , 32, , D35–D40.[Abstract/Free Full Text] .

8. Tasheva,E.S., Ke,A. and Conrad,G.W. ( (2004) ) Analysis of the expression of chondroadherin in mouse ocular and non-ocular tissues. Mol. Vis., , 10, , 544–554.[Web of Science][Medline] .

9. Oliver B. ( (2003) ) Fast males. Heredity, , 91, , 535–536.[CrossRef][Web of Science][Medline] .

10. Gomez-Merino,F.C., Brearley,C.A., Ornatowska,M., Abdel-Haliem,M.E., Zanor,M.I. and Mueller-Roeber,B. ( (2004) ) AtDGK2, a novel diacylglycerol kinase from Arabidopsis thaliana, phosphorylates 1-stearoyl-2-arachidonoyl-sn-glycerol and 1,2-dioleoyl-sn-glycerol and exhibits cold-inducible gene expression. J. Biol. Chem., , 279, , 8230–8241.[Abstract/Free Full Text] .

11. Lee,H.K., Hsu,A.K., Sajdak,J., Qin,J. and Pavlidis,P. ( (2004) ) Coexpression analysis of human genes across many microarray data sets. Genome Res., , 14, , 1085–1094.[Abstract/Free Full Text] .

12. Puffenberger,E.G., Hu-Lince,D., Parod,J.M., Craig,D.W., Dobrin,S.E., Conway,A.R., Donarum,E.A., Strauss,K.A., Dunckley,T., Cardenas,J.F. et al. ( (2004) ) Mapping of sudden infant death with dysgenesis of the testes syndrome (SIDDT) by a SNP genome scan and identification of TSPYL loss of function. Proc. Natl Acad. Sci. USA, , 101, , 11689–11694.[Abstract/Free Full Text] .

13. Reverter,A., McWilliam,S.M., Barris,W. and Dalrymple,B.P. ( (2004) ) A rapid method for computationally inferring transcriptome coverage and microarray sensitivity. Bioinformatics, , doi:10.1093/bioinformatics/bth472. .

14. Cheadle,C., Cho-Chung,Y.S., Becker,K.G. and Vawter,M.P. ( (2003) ) Application of z-score transformation to Affymetrix data. Appl. Bioinformatics, , 2, , 209–217.[Medline] .

CiteULike    Connotea    Del.icio.us    What's this?

This article has been cited by other articles:

				Y. Benita, H. Kikuchi, A. D. Smith, M. Q. Zhang, D. C. Chung, and R. J. Xavier An integrative genomics approach identifies Hypoxia Inducible Factor-1 (HIF-1)-target genes that form the core response to hypoxia Nucleic Acids Res., June 2, 2009; (2009) gkp425v1. [Abstract] [Full Text] [PDF]

				C. Huttenhower, E. M. Haley, M. A. Hibbs, V. Dumeaux, D. R. Barrett, H. A. Coller, and O. G. Troyanskaya Exploring the human genome with functional maps Genome Res., June 1, 2009; 19(6): 1093 - 1106. [Abstract] [Full Text] [PDF]

				W. B. Langdon, G. J. G. Upton, and A. P. Harrison Probes containing runs of guanines provide insights into the biophysics and bioinformatics of Affymetrix GeneChips Brief Bioinform, May 1, 2009; 10(3): 259 - 277. [Abstract] [Full Text] [PDF]

				J. A. Meyers, D. W. Su, and A. Lerner Chronic Lymphocytic Leukemia and B and T Cells Differ in Their Response to Cyclic Nucleotide Phosphodiesterase Inhibitors J. Immunol., May 1, 2009; 182(9): 5400 - 5411. [Abstract] [Full Text] [PDF]

				P. Gennemark and D. Wedelin Benchmarks for identification of ordinary differential equations from time series data Bioinformatics, March 15, 2009; 25(6): 780 - 786. [Abstract] [Full Text] [PDF]

				C. Taccioli, E. Fabbri, R. Visone, S. Volinia, G. A. Calin, L. Y. Fong, R. Gambari, A. Bottoni, M. Acunzo, J. Hagan, et al. UCbase & miRfunc: a database of ultraconserved sequences and microRNA function Nucleic Acids Res., January 1, 2009; 37(suppl_1): D41 - D48. [Abstract] [Full Text] [PDF]

				T.-t. Soong, K. O. Wrzeszczynski, and B. Rost Physical protein-protein interactions predicted from microarrays Bioinformatics, November 15, 2008; 24(22): 2608 - 2614. [Abstract] [Full Text] [PDF]

				B. Haibe-Kains, C. Desmedt, C. Sotiriou, and G. Bontempi A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? Bioinformatics, October 1, 2008; 24(19): 2200 - 2208. [Abstract] [Full Text] [PDF]

				A. R. Grosso, A. Q. Gomes, N. L. Barbosa-Morais, S. Caldeira, N. P. Thorne, G. Grech, M. von Lindern, and M. Carmo-Fonseca Tissue-specific splicing factor gene expression signatures Nucleic Acids Res., September 1, 2008; 36(15): 4823 - 4832. [Abstract] [Full Text] [PDF]

				R. D. Palmer, N. L. Barbosa-Morais, E. L. Gooding, B. Muralidhar, C. M. Thornton, M. R. Pett, I. Roberts, D. T. Schneider, N. Thorne, S. Tavare, et al. Pediatric Malignant Germ Cell Tumors Show Characteristic Transcriptome Profiles Cancer Res., June 1, 2008; 68(11): 4239 - 4247. [Abstract] [Full Text] [PDF]

				B. Xu, D. Geerts, K. Qian, H. Zhang, and G. Zhu Myeloid ecotropic viral integration site 1 (MEIS) 1 involvement in embryonic implantation Hum. Reprod., June 1, 2008; 23(6): 1394 - 1406. [Abstract] [Full Text] [PDF]

				M. Sharma, J. Batra, U. Mabalirajan, S. Sharma, R. Nagarkatti, J. Aich, S. K. Sharma, P. V. Niphadkar, and B. Ghosh A Genetic Variation in Inositol Polyphosphate 4 Phosphatase A Enhances Susceptibility to Asthma Am. J. Respir. Crit. Care Med., April 1, 2008; 177(7): 712 - 719. [Abstract] [Full Text] [PDF]

				D. Ruau, R. Ensenat-Waser, T. C. Dinger, D. S. Vallabhapurapu, A. Rolletschek, C. Hacker, T. Hieronymus, A. M. Wobus, A. M. Muller, and M. Zenke Pluripotency Associated Genes Are Reactivated by Chromatin-Modifying Agents in Neurosphere Cells Stem Cells, April 1, 2008; 26(4): 920 - 926. [Abstract] [Full Text] [PDF]

				I. G. Yulug and B. Gur-Dedeoglu Functional genomics in translational cancer research: focus on breast cancer Brief Funct Genomic Proteomic, March 7, 2008; (2008) eln009v1. [Abstract] [Full Text] [PDF]

				I. Fenic, H. M. Hossain, V. Sonnack, S. Tchatalbachev, F. Thierer, J. Trapp, K. Failing, K. S. Edler, M. Bergmann, M. Jung, et al. In Vivo Application of Histone Deacetylase Inhibitor Trichostatin-A Impairs Murine Male Meiosis J Androl, March 1, 2008; 29(2): 172 - 185. [Abstract] [Full Text] [PDF]

				K. E. Mortensen, L. N. Conley, J. Hedegaard, T. Kalstad, P. Sorensen, C. Bendixen, and A. Revhaug Regenerative response in the pig liver remnant varies with the degree of resection and rise in portal pressure Am J Physiol Gastrointest Liver Physiol, March 1, 2008; 294(3): G819 - G830. [Abstract] [Full Text] [PDF]

				I. Takigawa and H. Mamitsuka Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis Bioinformatics, January 15, 2008; 24(2): 250 - 257. [Abstract] [Full Text] [PDF]

				J. M. Arteaga-Salas, H. Zuzan, W. B. Langdon, G. J. G. Upton, and A. P. Harrison An overview of image-processing methods for Affymetrix GeneChips Brief Bioinform, January 1, 2008; 9(1): 25 - 33. [Abstract] [Full Text] [PDF]

				R. S. Stearman, L. Dwyer-Nield, M. C. Grady, A. M. Malkinson, and M. W. Geraci A Macrophage Gene Expression Signature Defines a Field Effect in the Lung Tumor Microenvironment Cancer Res., January 1, 2008; 68(1): 34 - 43. [Abstract] [Full Text] [PDF]

				M. M. Gosink, H. T. Petrie, and N. F. Tsinoremas Electronically subtracting expression patterns from a mixed cell population Bioinformatics, December 15, 2007; 23(24): 3328 - 3334. [Abstract] [Full Text] [PDF]

				W. Fujibuchi, L. Kiseleva, T. Taniguchi, H. Harada, and P. Horton CellMontage: similar expression profile search server Bioinformatics, November 15, 2007; 23(22): 3103 - 3104. [Abstract] [Full Text] [PDF]

				D. G Lemay, A. M Zivkovic, and J B. German Building the bridges to bioinformatics in nutrition research Am. J. Clinical Nutrition, November 1, 2007; 86(5): 1261 - 1269. [Abstract] [Full Text] [PDF]

				D. Geerts, C. J. Wallick, D.-L. T. Koomoa, J. Koster, R. Versteeg, R. C. V. Go, and A. S. Bachmann Expression of Prenylated Rab Acceptor 1 Domain Family, Member 2 (PRAF2) in Neuroblastoma: Correlation with Clinical Features, Cellular Localization, and Cerulenin-Mediated Apoptosis Regulation Clin. Cancer Res., November 1, 2007; 13(21): 6312 - 6319. [Abstract] [Full Text] [PDF]

				Y. C. Chan, S. Raengpradub, K. J. Boor, and M. Wiedmann Microarray-Based Characterization of the Listeria monocytogenes Cold Regulon in Log- and Stationary-Phase Cells Appl. Envir. Microbiol., October 15, 2007; 73(20): 6484 - 6498. [Abstract] [Full Text] [PDF]

				A. Chandramouli, J. Shi, Y. Feng, H. Holubec, R. M.Shanas, A. K. Bhattacharyya, W. Zheng, and M. A. Nelson Haploinsufficiency of the cdc2l gene contributes to skin cancer development in mice Carcinogenesis, September 1, 2007; 28(9): 2028 - 2035. [Abstract] [Full Text] [PDF]

				M. F. Burkart, J. D. Wren, J. I. Herschkowitz, C. M. Perou, and H. R. Garner Clustering microarray-derived gene lists through implicit literature relationships Bioinformatics, August 1, 2007; 23(15): 1995 - 2003. [Abstract] [Full Text] [PDF]

				J. Lee, T. Bansal, A. Jayaraman, W. E. Bentley, and T. K. Wood Enterohemorrhagic Escherichia coli Biofilms Are Inhibited by 7-Hydroxyindole and Stimulated by Isatin Appl. Envir. Microbiol., July 1, 2007; 73(13): 4100 - 4109. [Abstract] [Full Text] [PDF]

				M. L. Green and P. D. Karp Using genome-context data to identify specific types of functional associations in pathway/genome databases Bioinformatics, July 1, 2007; 23(13): i205 - i211. [Abstract] [Full Text] [PDF]

				Y. Lu, B. Bulka, M. desJardins, and S. J. Freeland Amino acid quantitative structure property relationship database: a web-based platform for quantitative investigations of amino acids Protein Eng. Des. Sel., July 1, 2007; 20(7): 347 - 351. [Abstract] [Full Text] [PDF]

				S. C. Smith, G. Oxford, A. S. Baras, C. Owens, D. Havaleshko, D. L. Brautigan, M. K. Safo, and D. Theodorescu Expression of Ral GTPases, Their Effectors, and Activators in Human Bladder Cancer Clin. Cancer Res., July 1, 2007; 13(13): 3803 - 3813. [Abstract] [Full Text] [PDF]

				L. L. Elnitski, P. Shah, R. T. Moreland, L. Umayam, T. G. Wolfsberg, and A. D. Baxevanis The ENCODEdb portal: Simplified access to ENCODE Consortium data Genome Res., June 1, 2007; 17(6): 954 - 959. [Abstract] [Full Text] [PDF]

				P. McGann, R. Ivanek, M. Wiedmann, and K. J. Boor Temperature-Dependent Expression of Listeria monocytogenes Internalin and Internalin-Like Genes Suggests Functional Diversity of These Proteins among the Listeriae Appl. Envir. Microbiol., May 1, 2007; 73(9): 2806 - 2814. [Abstract] [Full Text] [PDF]

				K. Hanada, X. Zhang, J. O. Borevitz, W.-H. Li, and S.-H. Shiu A large number of novel coding small open reading frames in the intergenic regions of the Arabidopsis thaliana genome are transcribed and/or under purifying selection Genome Res., May 1, 2007; 17(5): 632 - 640. [Abstract] [Full Text] [PDF]

				A. E. Platts, D. J. Dix, H. E. Chemes, K. E. Thompson, R. Goodrich, J. C. Rockett, V. Y. Rawe, S. Quintana, M. P. Diamond, L. F. Strader, et al. Success and failure in human spermatogenesis as revealed by teratozoospermic RNAs Hum. Mol. Genet., April 1, 2007; 16(7): 763 - 773. [Abstract] [Full Text] [PDF]

				H. Inaoka, Y. Fukuoka, and I. S. Kohane Evidence of spatially bound gene regulation in Mus musculus: Decreased gene expression proximal to microRNA genomic location PNAS, March 20, 2007; 104(12): 5020 - 5025. [Abstract] [Full Text] [PDF]

				M. D. Howell, N. Fahlgren, E. J. Chapman, J. S. Cumbie, C. M. Sullivan, S. A. Givan, K. D. Kasschau, and J. C. Carrington Genome-Wide Analysis of the RNA-DEPENDENT RNA POLYMERASE6/DICER-LIKE4 Pathway in Arabidopsis Reveals Dependency on miRNA- and tasiRNA-Directed Targeting PLANT CELL, March 1, 2007; 19(3): 926 - 942. [Abstract] [Full Text] [PDF]

				M. E. Lenburg, A. Sinha, D. V. Faller, and G. V. Denis Tumor-specific and Proliferation-specific Gene Expression Typifies Murine Transgenic B Cell Lymphomagenesis J. Biol. Chem., February 16, 2007; 282(7): 4803 - 4811. [Abstract] [Full Text] [PDF]

				F. Kolpakov, V. Poroikov, R. Sharipov, Y. Kondrakhin, A. Zakharov, A. Lagunin, L. Milanesi, and A. Kel CYCLONET--an integrated database on cell cycle regulation and carcinogenesis Nucleic Acids Res., January 12, 2007; 35(suppl_1): D550 - D556. [Abstract] [Full Text] [PDF]

				L. Bao, M. Zhou, L. Wu, L. Lu, D. Goldowitz, R. W. Williams, and Y. Cui PolymiRTS Database: linking polymorphisms in microRNA target sites with complex traits Nucleic Acids Res., January 12, 2007; 35(suppl_1): D51 - D54. [Abstract] [Full Text] [PDF]

				L. Bulow, M. Schindler, and R. Hehl PathoPlant(R): a platform for microarray expression data to analyze co-regulated genes involved in plant defense responses Nucleic Acids Res., January 12, 2007; 35(suppl_1): D841 - D845. [Abstract] [Full Text] [PDF]

				T. Barrett, D. B. Troup, S. E. Wilhite, P. Ledoux, D. Rudnev, C. Evangelista, I. F. Kim, A. Soboleva, M. Tomashevsky, and R. Edgar NCBI GEO: mining tens of millions of expression profiles--database and tools update Nucleic Acids Res., January 12, 2007; 35(suppl_1): D760 - D765. [Abstract] [Full Text] [PDF]

				C. M. Smith, J. H. Finger, T. F. Hayamizu, I. J. McCright, J. T. Eppig, J. A. Kadin, J. E. Richardson, and M. Ringwald The mouse Gene Expression Database (GXD): 2007 update Nucleic Acids Res., January 12, 2007; 35(suppl_1): D618 - D623. [Abstract] [Full Text] [PDF]

				T. Obayashi, K. Kinoshita, K. Nakai, M. Shibaoka, S. Hayashi, M. Saeki, D. Shibata, K. Saito, and H. Ohta ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis Nucleic Acids Res., January 12, 2007; 35(suppl_1): D863 - D869. [Abstract] [Full Text] [PDF]

				C. Galuschka, M. Schindler, L. Bulow, and R. Hehl AthaMap web tools for the analysis and identification of co-regulated genes Nucleic Acids Res., January 12, 2007; 35(suppl_1): D857 - D862. [Abstract] [Full Text] [PDF]

				J. Demeter, C. Beauheim, J. Gollub, T. Hernandez-Boussard, H. Jin, D. Maier, J. C. Matese, M. Nitzberg, F. Wymore, Z. K. Zachariah, et al. The Stanford Microarray Database: implementation of new analysis tools and open source release of software Nucleic Acids Res., January 12, 2007; 35(suppl_1): D766 - D770. [Abstract] [Full Text] [PDF]

				W. S. Lai, J. S. Parker, S. F. Grissom, D. J. Stumpo, and P. J. Blackshear Novel mRNA Targets for Tristetraprolin (TTP) Identified by Global Analysis of Stabilized Transcripts in TTP-Deficient Fibroblasts Mol. Cell. Biol., December 15, 2006; 26(24): 9196 - 9208. [Abstract] [Full Text] [PDF]

				V. Saxena, D. Orgill, and I. Kohane Absolute enrichment: gene set enrichment analysis for homeostatic systems Nucleic Acids Res., December 2, 2006; 34(22): e151 - e151. [Abstract] [Full Text] [PDF]

				K. M. Lonergan, R. Chari, R. J. deLeeuw, A. Shadeo, B. Chi, M.-S. Tsao, S. Jones, M. Marra, V. Ling, R. Ng, et al. Identification of Novel Lung Genes in Bronchial Epithelium by Serial Analysis of Gene Expression Am. J. Respir. Cell Mol. Biol., December 1, 2006; 35(6): 651 - 661. [Abstract] [Full Text] [PDF]

				L. Elnitski, V. X. Jin, P. J. Farnham, and S. J.M. Jones Locating mammalian transcription factor binding sites: A survey of computational and experimental techniques Genome Res., December 1, 2006; 16(12): 1455 - 1464. [Abstract] [Full Text] [PDF]

				J. T. Chang and J. R. Nevins GATHER: a systems approach to interpreting genomic signatures Bioinformatics, December 1, 2006; 22(23): 2926 - 2933. [Abstract] [Full Text] [PDF]

				C. Huttenhower, M. Hibbs, C. Myers, and O. G. Troyanskaya A scalable method for integration and functional analysis of multiple microarray datasets Bioinformatics, December 1, 2006; 22(23): 2890 - 2897. [Abstract] [Full Text] [PDF]

				S. Yoon, Y. Yang, J. Choi, and J. Seong Large scale data mining approach for gene-specific standardization of microarray gene expression data Bioinformatics, December 1, 2006; 22(23): 2898 - 2904. [Abstract] [Full Text] [PDF]

				A. C. Aplin, M. Gelati, E. Fogel, E. Carnevale, and R. F. Nicosia Angiopoietin-1 and vascular endothelial growth factor induce expression of inflammatory cytokines before angiogenesis Physiol Genomics, October 3, 2006; 27(1): 20 - 28. [Abstract] [Full Text] [PDF]

				J. A. Nielsen, D. Maric, P. Lau, J. L. Barker, and L. D. Hudson Identification of a Novel Oligodendrocyte Cell Adhesion Protein Using Gene Expression Profiling J. Neurosci., September 27, 2006; 26(39): 9881 - 9891. [Abstract] [Full Text] [PDF]

				D. Nam, S.-B. Kim, S.-K. Kim, S. Yang, S.-Y. Kim, and I.-S. Chu ADGO: analysis of differentially expressed gene sets using composite GO annotation Bioinformatics, September 15, 2006; 22(18): 2249 - 2253. [Abstract] [Full Text] [PDF]

				S.-E. Hong, S.-H. Rho, Y. I. Yeom, and D. H. Kim HCNet: a database of heart and calcium functional network Bioinformatics, August 15, 2006; 22(16): 2053 - 2054. [Abstract] [Full Text] [PDF]

				T. Koide, R. Z. N. Vencio, and S. L. Gomes Global Gene Expression Analysis of the Heat Shock Response in the Phytopathogen Xylella fastidiosa. J. Bacteriol., August 1, 2006; 188(16): 5821 - 5830. [Abstract] [Full Text] [PDF]

				C. J. Mattingly, M. C. Rosenstein, A. P. Davis, G. T. Colby, J. N. Forrest Jr, and J. L. Boyer The Comparative Toxicogenomics Database: A Cross-Species Resource for Building Chemical-Gene Interaction Networks Toxicol. Sci., August 1, 2006; 92(2): 587 - 595. [Abstract] [Full Text] [PDF]

				A. S. Siddiqui, A. D. Delaney, A. Schnerch, O. L. Griffith, S. J. M. Jones, and M. A. Marra Sequence biases in large scale gene expression profiling data Nucleic Acids Res., July 13, 2006; 34(12): e83 - e83. [Abstract] [Full Text] [PDF]

				N. Crapoulet, P. Barbry, D. Raoult, and P. Renesto Global Transcriptome Analysis of Tropheryma whipplei in Response to Temperature Stresses. J. Bacteriol., July 1, 2006; 188(14): 5228 - 5239. [Abstract] [Full Text] [PDF]

				T. J. Lukas, W. W. Luo, H. Mao, N. Cole, and T. Siddique Informatics-assisted Protein Profiling in a Transgenic Mouse Model of Amyotrophic Lateral Sclerosis Mol. Cell. Proteomics, July 1, 2006; 5(7): 1233 - 1244. [Abstract] [Full Text] [PDF]

				N. Harish, R. Gupta, P. Agarwal, V. Scaria, and B. Pillai DyNAVacS: an integrative tool for optimized DNA vaccine design. Nucleic Acids Res., July 1, 2006; 34(Web Server issue): W264 - W266. [Abstract] [Full Text] [PDF]

				Y.-P. Wang, L. Liang, B.-C. Han, Y. Quan, X. Wang, T. Tao, and Z.-L. Ji GEPS: the Gene Expression Pattern Scanner. Nucleic Acids Res., July 1, 2006; 34(Web Server issue): W492 - W497. [Abstract] [Full Text] [PDF]

				G. Lelandais, P. Vincens, A. Badel-Chagnon, S. Vialette, C. Jacq, and S. Hazout Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms Bioinformatics, June 1, 2006; 22(11): 1359 - 1366. [Abstract] [Full Text] [PDF]

				D. R. Hoen, K. C. Park, N. Elrouby, Z. Yu, N. Mohabir, R. K. Cowan, and T. E. Bureau Transposon-Mediated Expansion and Diversification of a Family of ULP-like Genes Mol. Biol. Evol., June 1, 2006; 23(6): 1254 - 1268. [Abstract] [Full Text] [PDF]

				J. Domka, J. Lee, and T. K. Wood YliH (BssR) and YceP (BssS) Regulate Escherichia coli K-12 Biofilm Formation by Influencing Cell Signaling Appl. Envir. Microbiol., April 1, 2006; 72(4): 2449 - 2459. [Abstract] [Full Text] [PDF]

				P. L. Whetzel, H. Parkinson, H. C. Causton, L. Fan, J. Fostel, G. Fragoso, L. Game, M. Heiskanen, N. Morrison, P. Rocca-Serra, et al. The MGED Ontology: a resource for semantics-based description of microarray experiments Bioinformatics, April 1, 2006; 22(7): 866 - 873. [Abstract] [Full Text] [PDF]

				M. Stein, J. Dittgen, C. Sanchez-Rodriguez, B.-H. Hou, A. Molina, P. Schulze-Lefert, V. Lipka, and S. Somerville Arabidopsis PEN3/PDR8, an ATP Binding Cassette Transporter, Contributes to Nonhost Resistance to Inappropriate Pathogens That Enter by Direct Penetration PLANT CELL, March 1, 2006; 18(3): 731 - 746. [Abstract] [Full Text] [PDF]

				D. L. Wheeler, T. Barrett, D. A. Benson, S. H. Bryant, K. Canese, V. Chetvernin, D. M. Church, M. DiCuccio, R. Edgar, S. Federhen, et al. Database resources of the National Center for Biotechnology Information Nucleic Acids Res., January 1, 2006; 34(suppl_1): D173 - D180. [Abstract] [Full Text] [PDF]

				X. Wang, H. Zhao, Q. Xu, W. Jin, C. Liu, H. Zhang, Z. Huang, X. Zhang, Y. Zhang, D. Xin, et al. HPtaa database-potential target genes for clinical diagnosis and immunotherapy of human carcinoma Nucleic Acids Res., January 1, 2006; 34(suppl_1): D607 - D612. [Abstract] [Full Text] [PDF]

				A. Elfilali, S. Lair, C. Verbeke, P. La Rosa, F. Radvanyi, and E. Barillot ITTACA: a new database for integrated tumor transcriptome array and clinical data analysis Nucleic Acids Res., January 1, 2006; 34(suppl_1): D613 - D616. [Abstract] [Full Text] [PDF]

				M. Dai, P. Wang, A. D. Boyd, G. Kostov, B. Athey, E. G. Jones, W. E. Bunney, R. M. Myers, T. P. Speed, H. Akil, et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data Nucleic Acids Res., November 10, 2005; 33(20): e175 - e175. [Abstract] [Full Text] [PDF]

				J. Ishibashi, R. L. Perry, A. Asakura, and M. A. Rudnicki MyoD induces myogenic differentiation through cooperation of its NH2- and COOH-terminal regions J. Cell Biol., November 7, 2005; 171(3): 471 - 482. [Abstract] [Full Text] [PDF]

				J. A. Holzwarth, R. P. Middleton, M. Roberts, R. Mansourian, F. Raymond, and S. S. Hannah The Development of a High-Density Canine Microarray J. Hered., November 1, 2005; 96(7): 817 - 820. [Abstract] [Full Text] [PDF]

				T. F. Ducey, M. B. Carson, J. Orvis, A. P. Stintzi, and D. W. Dyer Identification of the Iron-Responsive Genes of Neisseria gonorrhoeae by Microarray Analysis in Defined Medium J. Bacteriol., July 15, 2005; 187(14): 4865 - 4874. [Abstract] [Full Text] [PDF]

				S. H. Chen, Y. Babichev, N. Rodrigues, D. Voskas, L. Ling, V. P. K. H. Nguyen, and D. J. Dumont Gene expression analysis of Tek/Tie2 signaling Physiol Genomics, July 14, 2005; 22(2): 257 - 267. [Abstract] [Full Text] [PDF]

				A. Boorsma, B. C. Foat, D. Vis, F. Klis, and H. J. Bussemaker T-profiler: scoring the activity of predefined groups of genes using gene expression data Nucleic Acids Res., July 1, 2005; 33(suppl_2): W592 - W595. [Abstract] [Full Text] [PDF]

				R. Christmas, I. Avila-Campillo, H. Bolouri, B. Schwikowski, M. Anderson, R. Kelley, N. Landys, C. Workman, T. Ideker, E. Cerami, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks Am. Assoc. Cancer Res. Educ. Book, April 1, 2005; 2005(1): 12 - 16. [Full Text] [PDF]

				D. L. Wheeler, T. Barrett, D. A. Benson, S. H. Bryant, K. Canese, D. M. Church, M. DiCuccio, R. Edgar, S. Federhen, W. Helmberg, et al. Database resources of the National Center for Biotechnology Information Nucleic Acids Res., January 1, 2005; 33(suppl_1): D39 - D45. [Abstract] [Full Text] [PDF]

				C. Brooksbank, G. Cameron, and J. Thornton The European Bioinformatics Institute's data resources: towards systems biology Nucleic Acids Res., January 1, 2005; 33(suppl_1): D46 - D53. [Abstract] [Full Text] [PDF]

This Article Abstract Print PDF (504K) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Archive Download to citation manager Commercial Re-use Guidelines for Open Access NAR Content Google Scholar Articles by Barrett, T. Articles by Edgar, R. Search for Related Content PubMed PubMed Citation Articles by Barrett, T. Articles by Edgar, R. Social Bookmarking          What's this?

Online ISSN 1362-4962 - Print ISSN 0305-1048

Copyright © 2009 Oxford University Press

Oxford Journals Oxford University Press

• Site Map
• Privacy Policy
• Frequently Asked Questions

Other Oxford University Press sites: Oxford University Press Oxford Journals China Oxford Journals Japan American National Biography Booksellers' Information Service Children's Fiction and Poetry Children's Reference Corporate & Special Sales Dictionaries Dictionary of National Biography Digital Reference English Language Teaching Higher Education Textbooks Humanities International Education Unit Law Medicine Music Online Products Oxford English Dictionary Reference Rights and Permissions Science School Books Social Sciences Very Short Introductions World's Classics