List of lists-annotated (LOLA): a database for annotation and comparison of published microarray gene lists

The George Washington University Medical Center, Department of Biochemistry and Molecular Biology, 2300 I Street NW. Ross Hall 541, Washington, D.C. 20037, United States.
Gene (Impact Factor: 2.08). 11/2005; 360(1):78-82. DOI: 10.1016/j.gene.2005.07.008
Source: PubMed

ABSTRACT Microarray profiling of RNA expression is a powerful tool that generates large lists of transcripts that are potentially relevant to a disease or treatment. However, because the lists of changed transcripts are embedded in figures and tables, they are typically inaccessible for search engines. Due to differences in gene nomenclatures, the lists are difficult to compare between studies. LOLA (Lists of Lists Annotated) is an internet-based database for comparing gene lists from microarray studies or other genomic-scale methods. It serves as a common platform to compare and reannotate heterogeneous gene lists from different microarray platforms or different genomic methodologies such as serial analysis of gene expression (SAGE) or proteomics. LOLA () provides researchers with a means to store, annotate, and compare gene lists produced from different studies or different analyses of the same study. It is especially useful in identifying potentially "high interest" genes which are reported as significant across multiple studies and species. Its application to the fields of stem cell, cancer, and aging research is demonstrated by comparing published papers.

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    • "To corroborate our results, as well as to detect potentially important genes common to the response to OS in different tissues, we performed a meta-analysis of microarray studies that explored transcriptional changes upon OS (Edwards et al. 2004; Tomita et al. 2006, 2007; Wang et al. 2007, 2009; Chin et al. 2008; Olesen et al. 2008; Patel et al. 2008; Sforza 2008) using the LOLA database and analysis software ( (Cahan et al. 2005). Thirty genes that appear differentially expressed in our analysis of the WT cerebellum upon PQ treatment (some of them highlighted in bold in Tables S5 and S6) showed concordant regulations in other microarray reports (see above) using diverse OS experimental paradigms. "
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    ABSTRACT: The lipocalin Apolipoprotein D (ApoD), known to protect the nervous system against oxidative stress (OS) in model organisms, is up-regulated early in the mouse brain in response to the ROS generator paraquat. However, the processes triggered by this up-regulation have not been explored. We present here a study of the effect of ApoD on the early transcriptional changes upon OS in the mouse cerebellum using microarray profiling. ApoD-KO and transgenic mice over-expressing ApoD in neurons are compared to wild-type controls. In control conditions, ApoD affects the transcriptional profile of neuron and oligodendrocyte-specific genes involved in neuronal excitability, synaptic function, and myelin homeostasis. When challenged with paraquat, the absence of ApoD modifies the response of genes mainly related to OS management and myelination. Interestingly, the over-expression of ApoD in neurons almost completely abolishes the early transcriptional response to OS. We independently evaluate the expression of protein kinase Cδ, a gene up-regulated by OS only in the ApoD-KO cerebellum, and find it over-expressed in cultured ApoD-KO primary astrocytes, which points to a role for ApoD in astrocyte-microglia signaling. Our results support the hypothesis that ApoD is necessary for a proper response of the nervous system against physiological and pathological OS.
    Journal of Neurochemistry 04/2011; 117(6):949-60. DOI:10.1111/j.1471-4159.2011.07266.x · 4.24 Impact Factor
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    • "A proof-of-concept study by Hughes et al. (2000) demonstrated a general approach for functional annotation of uncharacterized genes and pharmacological perturbations using a reference database of expression profiles corresponding to 300 diverse chemical treatments and mutations in yeast (Hughes et al., 2000). However, despite significant advances in the storage, analysis and mining of microarray data, as well as the development of web-based tools, Cahan et al. (2005, 2007) noted that the resulting differentially expressed gene (DEG) lists were not amenable to electronic access, and remained inaccessible for comparison purposes. The poor consistency in determining DEGs and the uncertainty in selecting calculation methods may account for the observed shortfall. "
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    ABSTRACT: High-density oligonucleotide arrays are widely used for analysis of gene expression on a genomic scale, but the generated data remain largely inaccessible for comparative analysis purposes. Similarity searches in databases with differentially expressed gene (DEG) lists may be used to assign potential functions to new genes and to identify potential chemical inhibitors/activators and genetic suppressors/enhancers. Although this is a very promising concept, it requires the compatibility and validity of the DEG lists to be significantly improved. Using Arabidopsis and human datasets, we have developed guidelines for the performance of similarity searches against databases that collect microarray data. We found that, in comparison with many other methods, a rank-product analysis achieves a higher degree of inter- and intra-laboratory consistency of DEG lists, and is advantageous for assessing similarities and differences between them. To support this concept, we developed a tool called MASTA (microarray overlap search tool and analysis), and re-analyzed over 600 Arabidopsis microarray expression datasets. This revealed that large-scale searches produce reliable intersections between DEG lists that prove to be useful for genetic analysis, thus aiding in the characterization of cellular and molecular mechanisms. We show that this approach can be used to discover unexpected connections and to illuminate unanticipated interactions between individual genes.
    The Plant Journal 10/2009; 61(1):166-75. DOI:10.1111/j.1365-313X.2009.04043.x · 6.82 Impact Factor
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    • "Out of 15 studies on dietary restriction in mouse, only one DEG was found by four studies, and no DEGs were identified by five or more studies. Databases such as LOLA, L2L and MSigDB (Cahan et al., 2005; Newman and Weiner 2005; Subramanian et al., 2005) have been created to store DEG lists from microarray studies, to facilitate list-based meta-analysis. "
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    ABSTRACT: MOTIVATION: Many biological systems operate in a similar manner across a large number of species or conditions. Cross-species analysis of sequence and interaction data is often applied to determine the function of new genes. In contrast to these static measurements, microarrays measure the dynamic, condition-specific response of complex biological systems. The recent exponential growth in microarray expression datasets allows researchers to combine expression experiments from multiple species to identify genes that are not only conserved in sequence but also operated in a similar way in the different species studied. RESULTS: In this review we discuss the computational and technical challenges associated with these studies, the approaches that have been developed to address these challenges and the advantages of cross-species analysis of microarray data. We show how successful application of these methods lead to insights that cannot be obtained when analyzing data from a single species. We also highlight current open problems and discuss possible ways to address them.
    Bioinformatics 05/2009; 25(12):1476-83. DOI:10.1093/bioinformatics/btp247 · 4.62 Impact Factor
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