Software L 2 L : a simple tool for discovering the hidden significance in microarray expression data

L2L is a database consisting of lists of differentially expressed genes compiled from published mammalian microarray studies, along with an easy-to-use application for mining the database with the user's own microarray data. As illustrated by re-analysis of a recent study of diabetic nephropathy, L2L identifies novel biological patterns in microarray data, providing insights into the underlying nature of biological processes and disease. L2L is available online at the authors' website [http://depts.washington. edu/l2l/]. Rationale In only a few years since their development, high-throughput, whole-genome DNA microarrays have become an invaluable tool throughout biology. The appeal of microarrays seems most irresistible when the biological problem is most intractable; microarrays have become perhaps the most popular contemporary tool for hypothesis generation. Yet interpreting the mountain of data produced by a microarray experiment can be a frustrating chore. The most common outcome of such an experiment is a list of genes, or many such lists: genes that are induced or repressed under one condition or another, at one time point or another, in one cluster or another. The daunting task is to extract some meaning from these lists, either by identifying 'critical genes' which might single-handedly produce a biological effect, or by finding patterns in the list that point to an underlying biological process. The latter universally involves annotating each gene on the list and looking for groups of genes that share a particular characteristic. Until recently, this was done entirely by hand. Each gene was assigned, after a laborious literature search, to an arbitrary functional category like 'DNA repair' or 'metabolism'. A hypothesis might be based on which arbitrary categories appeared most often. Like any non-systematic approach, this one is vulnerable to our very human knack of seeing whatever pattern we wish in a noisy field. The Gene Ontology (GO) consortium [1] has brought systematic order to the field of gene annotation by pre-categorizing genes by biological process, molecular function, and cell component thus eliminating the pattern-creating risk of post hoc annotation. A number of software tools now exist to automate the process of annotating a list of genes with GO categories. Several of these, including EASE [2], GOMiner [3], Onto-Express [4] and GO::TermFinder [5], also calculate the over-abundance of each category in the list, along with its statistical significance. However, even after functional annotation of the list of genes, uncertainty remains as to whether the results advance understanding of the biology at work in the system, and, if the system is a complex disease, whether the results help explain why the gene expression changes occurred. An alternative approach to interpreting gene expression data is to compare it with other related (or potentially related) gene expression data. The motivation is that microarray experiments exhibiting common changes in gene expression are likely to share one or more underlying molecular mechanisms. Published: 31 August 2005 Genome Biology 2005, 6:R81 (doi:10.1186/gb-2005-6-9-r81) Received: 5 April 2005 Revised: 16 June 2005 Accepted: 26 July 2005 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2005/6/9/R81 Genome Biology 2005, 6:R81 R81.2 Genome Biology 2005, Volume 6, Issue 9, Article R81 Newman and Weiner http://genomebiology.com/2005/6/9/R81 Furthermore, in some experiments, the underlying cause of the gene expression changes is well-defined: a specific gene deletion, for example, or treatment with a single receptor ligand. In such cases, the ability to connect the user's experiment with gene expression changes caused by a well-defined perturbation may lead immediately to a hypothesis regarding the underlying mechanism in the system under study. L2L is a database and associated software tool (Figure 1a) that systematically compares the user's own list of differentially expressed genes with a database of lists of differentially expressed genes that were derived from published microarray data, with the goal of finding common expression patterns that can help generate new hypotheses. The L2L Microarray Database was culled from 111 selected publications, and contains 357 lists of genes that were found to be either upregulated or downregulated under a particular experimental condition. The conditions represented in the database range from normal ageing to space flight, and from interferon treatment to histone deacetylase inhibition (Figure 1b). The L2L Microarray Analysis Tool compares each list in the database with a list of genes supplied by the user, and reports the statistical significance of any overlap between them. It also annotates each gene on the user's list with all the lists in the database on which it is found. The results are presented as a set of hyperlinked HTML documents, which can be conveniently explored by surfing from list to list and gene to gene. L2L is available as an easy-to-use online tool [6], and as a downloadable, command-line application released under the GNU General Public License. L2L Microarray Database The need for a standardized format for presenting and storing microarray data from disparate platforms has been recognized for several years. A consortium of researchers [7] has detailed a standardized format for presenting microarray data (MAIME) [8] as well as a markup language in which to encode those now-standardized data (MAGE-ML) [9]. The data can be deposited in any of a number of large public repositories, including CIBEX, ArrayExpress, Oncomine and the NIH's Gene Expression Omnibus (GEO) [10-13]. All of these include web-accessible data-mining tools for browsing experiments and searching for the expression results associated with a particular gene. The sheer volume of deposited data is staggering, and represents a gold mine for bioinformaticians. Yet it all remains remarkably inaccessible to lay biologists. Although we can search GEO, for example, for microarray-identified genes one-by-one, there is no simple way to compare our data en masse with any other data in the repository, much less against all the data in the repository. Furthermore, repositories can make it difficult to extract the original results from the mass of deposited data; an interested user is often required to essentially re-analyze the data, with little knowledge of the original data analysis protocol or, in some cases, without access to all of the relevant data (for instance, GEO submissions do not usually include Affymetrix test-statistic data, a qualitative 'change call' which can be more accurate than the quantitative fold-change for detecting differential expression [14]). The L2L Microarray Database collects an interesting subset of this public data in its most essential and accessible form simple, well-annotated lists of genes, using a universal identifier, which were found to be either upregulated or downregulated under a particular condition. It is not intended to be an alternative to the public repositories, but an accessible and L2L and the L2L Microarray Database Figure 1 L2L and the L2L microarray database. (a) The centerpiece of L2L is the L2L Microarray Database, a collection of published microarray data in the form of lists of genes that are upor downregulated in some condition. The L2L Microarray Analysis Tool (MAT) is a program that compares those lists with a user's microarray data, and reports statistically significant overlaps. The analysis tool includes a web browser interface, but the L2L application itself can be downloaded and run directly from the command line for batch or customized analyses. Three additional sets of lists, based on the three organizing principles of Gene Ontology, can also be used with the analysis tool. (b) The L2L Microarray Database contains over 350 lists compiled from over 100 selected microarray publications. A wide variety of topics are represented, from chromatin modifications and DNA damage to the immune response and adipocyte differentiation. (a)