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      DAVID-WS: a stateful web service to facilitate gene/protein list analysis

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          Summary: The database for annotation, visualization and integrated discovery (DAVID), which can be freely accessed at, is a web-based online bioinformatics resource that aims to provide tools for the functional interpretation of large lists of genes/proteins. It has been used by researchers from more than 5000 institutes worldwide, with a daily submission rate of ∼1200 gene lists from ∼400 unique researchers, and has been cited by more than 6000 scientific publications. However, the current web interface does not support programmatic access to DAVID, and the uniform resource locator (URL)-based application programming interface (API) has a limit on URL size and is stateless in nature as it uses URL request and response messages to communicate with the server, without keeping any state-related details. DAVID-WS (web service) has been developed to automate user tasks by providing stateful web services to access DAVID programmatically without the need for human interactions.

          Availability: The web service and sample clients (written in Java, Perl, Python and Matlab) are made freely available under the DAVID License at

          Contact: xiaoli.jiao@ ; rlempicki@

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          Identifying biological themes within lists of genes with EASE

          Biological relevance within lists of genes High-density microarray and proteomic technologies have enabled the discovery of global patterns of biological responses with respect to experimental or natural perturbations [1]. Much work has addressed the issues of data normalization and statistical selection of genes significantly modulated or clustered based upon expression profiles [2]. The net result of these efforts is one or more lists of genes. Unfortunately, little work has addressed the issue of rapidly identifying biological themes in such lists [3]. Most investigators currently annotate genes one at-a-time using internet-based databases or manual literature searches. Following this tedious process, many researchers struggle to identify the most salient biological themes in order to make sense of their results and have no systematic way to prioritize these themes for further analysis. A parallel issue in interpreting such data regards how to leverage the ever-expanding flood of functional genomic data and tools. We developed the Expression Analysis Systematic Explorer (EASE) to automate the process of biological theme determination for lists of genes and to serve as a customizable gateway to online analysis tools. This is the first report to show that the highest-ranking themes derived by a computational method can recapitulate manually derived themes in previously published results, and that these themes are stable to varying methods of gene selection. EASE performs three basic functions with any list of genes: 1) over-representation analysis of functional gene categories, 2) customizable linking to online tools, and 3) creation of descriptive annotation tables. Each of these functions uses a system of tab-delimited text files that are easy to customize and update. EASE is an easy-to-use, customizable tool that allows investigators to systematically mine the mass of functional information associated with data generated by microarray, proteomics or SAGE studies. EASE uses customizable text files for theme discovery, annotation, and linking to online tools To analyze a gene list, EASE first maps the gene identifiers to a standardized gene accession (SGA) system via a simple text file in the \Data\Convert\ directory. The default SGA system used by EASE is LocusLink numbers. Upon conversion to the SGA system, EASE maps the genes to biological categories within various classification systems. Each system is specified in a text file in the \Data\Class\ directory that maps many-to-many relationships between genes and gene categories within the classification system. Similarly, EASE maps genes to annotation fields specified in files of the \Data\ directory. Users can therefore utilize any system of identifying genes with any custom annotation fields or categorical systems by creating the associated text files in the appropriate directory, as outlined in the help files of EASE. EASE comes equipped with an automated update routine that downloads and parses public annotation data sources and installs a LocusLink-based system of files, thereby allowing researchers to use EASE with the most up-to-date annotation information. EASE constructs hyperlinks to definitions for various categorical systems and the gene categories therein with configuration files in the \Data\Class\URL data\ directory. EASE is also capable of loading the genes in the current gene list into various online tools by using simple URL configuration text files in the \Links\ directory. Both types of configuration files are text files that are simple to create or modify to facilitate the addition of new links to online tools and definitions for new categorical systems added by the user. For over-representation analysis, EASE can utilize any number of systems of categorizing genes simultaneously. EASE calculates over-representation with respect to the total number of genes assayed and annotated within each system to allow for side-by-side comparisons of categories from categorization systems with varying levels of annotation. The conversion of gene identifiers to an SGA system such as LocusLink numbers is essential to the over-representation analysis to ensure that a single gene represented by more than one identifier (typical of Genbank) receives only one "vote" for each of its categories. EASE uses the three systems of the Gene Ontology as default categorization systems, however any set of custom or public systems can be simultaneously analyzed, including: SwissProt and PIR keywords, transcription factor regulation, protein domains, pathway membership, chromosomal location, and MeSH headings or keywords extracted from gene-associated literature. The user has a choice of two statistical measures of over-representation: the one-tailed Fisher exact probability or a variant thereof - referred to as the "EASE score" - calculated by penalizing (removing) one gene within the given category from the list and calculating the resulting Fisher exact probability for that category. The EASE score represents the lower bound of all possible jackknife probabilities and has advantages in terms of penalizing the significance of categories supported by few genes. The EASE score thus favors more robust categories than the Fisher exact probability. EASE comes equipped with: 1) files for specifying genes as LocusLink numbers, Genbank, MGI, RGD, or Flybase accessions, or Affymetrix probeset identifiers, 2) annotation fields from LocusLink, 3) population files consisting of all LocusLink numbers for selected species and for various Affymetrix GeneChips and other selected microarrays and 4) classification systems derived from the Gene Ontology, KEGG, BBID and Swissprot as well as classification systems parsed from LocusLink including Proteome's "At-A-Glance", chromosome location, PFAM and SMART protein domains. Furthermore, most of these data files can be updated from their original internet sources at any time by clicking the "Update with the Most Recent Online Data" button (Figure 1). Storage of these data to local tab-delimited text files allows for quick access and obviates any concern regarding the transmission of confidential research results over the internet. Figure 1 The EASE user interface is designed for quick annotation and analysis of gene lists. Gene identifiers are pasted into the "INPUT GENES" section, and the processes of linking to online tools, over-representation analysis or annotation are launched with buttons in the "EXPLORE" section. Annotation data can be automatically retrieved from the internet and stored into local data files by clicking the [update with the most recent online data] button. Exploring a gene list with EASE The core function of EASE is to annotate or analyze a list of genes input as gene identifiers, and display the result in the system web-browser or save the result in a tab-delimited text or Microsoft Excel format. The identifiers can be loaded from a text file or pasted into EASE from another application. Upon input of identifiers, the user can generate an annotation table by clicking the "Annotate Genes" button (Figure 1). The user can also link to any number of online tools such as DAVID [4] via the "Link to:" list box; this function automatically loads the information specific to the current gene list into the online tool, thereby allowing EASE to serve as a convenient interface to these resources. The identification of biological themes in the gene list is initiated by clicking the "Find over-represented gene categories" button. This function returns an output of all gene categories ranked by over-representation, with associated probabilities, counts used in the probability calculation, associated genes from the original list and links to various online tools for these genes. The most significantly over-represented categories that result from this analysis are deemed "biological themes" of the gene list. The user can optionally limit these analyses to any particular set of gene categories to answer questions such as "what is special about the mitochondrial genes on my list compared to all mitochondrial genes on the microarray?" The user can further use the "Refine" functionality of EASE to remove specific genes from the original list and enable an over-representation analysis of the remaining genes exclusively. These two functions can be applied repeatedly until the gene list is thoroughly characterized. EASE also allows for comparisons of gene lists at a thematic level, wherein the results are expressed in terms of gene categories over-represented in one list compared to all lists combined. Calculating statistics on thousands of gene categories can lead to a few seemingly significant probabilities due simply to random chance. To address this multiple comparison issue, EASE is capable of implementing a wide variety of probability corrections including Bonferroni-type methods and bootstrap methods performed by iteratively running over-representation analyses on random gene lists to more accurately determine the true probability of observing a given categorical enrichment. Nevertheless, the power of EASE is most appropriately viewed as an exploratory tool to direct the attention of the researcher to enriched biological themes by prioritizing functional categories based on the significance of over-representation. EASE themes recapitulate manually-determined themes The published gene lists of Kayo et al. [5] were re-analyzed with EASE to test the ability of EASE to generate themes comparable to manually determined themes. In the Kayo study, the authors generated four gene lists corresponding to genes up- and down-regulated in primate muscle in response to aging or caloric restriction. These gene lists were analyzed with the categorical over-representation function of EASE using EASE scores that were corrected for multiplicity using 10,000 bootstrap iterations. All significant (p 2.2) or Significance Analysis of Microarrays (SAM; d > 2.2) [8]. The eight resulting lists were analyzed with the categorical over-representation function of EASE using EASE scores that were corrected for multiplicity using 10,000 bootstrap iterations (Figure 3). Figure 3 EASE themes are consistent despite the poor overlap of gene lists derived from the same experiment by various analytical methods. (a) Gene lists resulting from the same experiment can differ greatly due to selection criteria. Eight different methods were used to select genes up-regulated in PBMCs of HIV patients upon discontinuation of antiretroviral drug therapy. The various lists resulted from four different array-to-array normalizations, two different methods of intensity calculation and two methods of statistical selection of genes (see text). The grayed boxes on the diagonal show the total number of genes yielded by each method. The non-shaded boxes show the absolute number of genes shared by any two methods, with the percentage of genes in both lists from the combined gene lists is shown in parenthesis. MAS 4 = Affymetrix analysis software version 4.0. dChip = dChip software [6]. Rank remapping=unpublished method of DAH. Non-parametric = method of Sidirov, et al. [7]. t-Test = student T statistic. SAM = Statistical Analysis of Microarrays software [8]. (b) EASE identifies the same themes in spite of the notable variability in gene lists resulting from different methods of data normalization and gene selection criteria (see figure 3a). The six dominant categories over-represented in the genes up-regulated in PBMCs of HIV patients one month after discontinuing therapy all implicate a vigorous immune response. EASE scores (corrected for multiplicity with the bootstrap function using 10,000 random trials) are shown along with the rank (in parentheses) of each category in each of the eight lists. The category outside of the top five for each list is shaded gray. N.S. = p > 0.05 Figure 3a demonstrates the instability of the size and overlap of the gene lists that result from varying gene selection methods. The percentage of genes overlapping in any two lists was highly variable, and ranged from 7% to 60%. In spite of this striking variation, the top five biological themes returned by EASE for each of the eight gene lists were virtually the same; all derived from a group of six categories that implicate a vigorous interferon-induced immune response in patients with rebounding HIV viral loads (Figure 3b). The conversion of genes to themes with EASE allowed the "biological result" of the experiment to be determined despite substantial differences in gene list content resulting from the use of various normalization, gene intensity and statistical selection methods. "Genes To Themes" with EASE: Possible uses of the EASE Method EASE rapidly converts a list of genes into an ordered table of robust biological themes that summarize the biological result of the experiment. This method has immediate utility for finding themes that most differentiate lists of genes, e.g. up-regulated versus down-regulated in a single experiment, but could potentially be applied to compare the results of different experiments, even involving different species and/or microarray platforms. The EASE method has proven useful for a SAGE analysis of cancer (W.D. Stein, manuscript in preparation) and for microarray analyses of cancer (A. Domkowski, manuscript in preparation; K. Akagi, personal communication), cataracts (M. Kantorow, manuscript in preparation) and immune function in HIV disease [9,10]. The EASE method also enables a rapid assay for overlap between gene clusters identified in any number of experiments when the user creates gene classification schema based upon these clusters. EASE can potentially be used to facilitate the development of data normalization and gene selection criteria by observing the highest enrichment attained for EASE themes within a particular experiment in which the biological phenomenon is well characterized and confirmed. EASE allows investigators to fully leverage the potential of high-throughput functional genomics technologies to infer biological themes. A full-featured version of EASE is freely available to non-profit researchers for use on Windows operating systems and a limited online version of the EASE over-representation function is available on the DAVID website [4].
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            DAVID gene ID conversion tool

            Our current biological knowledge is spread over many independent bioinformatics databases where many different types of gene and protein identifiers are used. The heterogeneous and redundant nature of these identifiers limits data analysis across different bioinformatics resources. It is an even more serious bottleneck of data analysis for larger datasets, such as gene lists derived from microarray and proteomic experiments. The DAVID Gene ID Conversion Tool (DICT), a web-based application, is able to convert user's input gene or gene product identifiers from one type to another in a more comprehensive and high-throughput manner with a uniquely enhanced ID-ID mapping database. Availability

              Author and article information

              Oxford University Press
              1 July 2012
              27 April 2012
              27 April 2012
              : 28
              : 13
              : 1805-1806
              1Laboratory of Immunopathogenesis and Bioinformatics, 2Advanced Biomedical Computing Center, 3Clinical Services Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, MD 21702, USA and 4Laboratory of Immuno-regulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
              Author notes
              * To whom correspondence should be addressed.

              Associate Editor: Martin Bishop

              © The Author(s) 2012. Published by Oxford University Press.

              This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

              Page count
              Pages: 2
              Applications Note
              Databases and Ontologies

              Bioinformatics & Computational biology


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