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: 2.14). 11/2005; 360(1):78-82. DOI: 10.1016/j.gene.2005.07.008
Source: PubMed


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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Available from: Sidney Wang Fu, Apr 04, 2015
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    • "Another method called mDEDS was developed by Campain and Yang and uses several different statistical measures to perform cross-species comparison of gene expression profiles [30]. Other methods includes LOLA [34] and L2L [35] which are both online tools for comparisons of ranking lists of differentially expressed genes from microarrays studies, including lists from different species. However, all these methods assume a one-to-one correspondence between genes from different species. "
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    ABSTRACT: Background Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex. Results In this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified. Conclusions The method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at
    BMC Bioinformatics 02/2013; 14(1):70. DOI:10.1186/1471-2105-14-70 · 2.58 Impact Factor
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    • "Therefore, many meta-analyses use the same species or even the same array platform to mitigate some of these heterogeneities. However, many studies do still attempt to perform cross-platform and inter-species meta-analyses, and tools such as AILUN (Array Information Library Universal Navigator) [73], A-MADMAN (Annotation-based microarray data meta-analysis tool) [74], and LOLA (List Of Lists Annotated) [75] enable cross-species meta-analysis using Entrez ID, gene symbol or other IDs as a conversion intermediary. AbsIDconvert can perform cross-platform/-species analysis efficiently using the sequence based approach. "
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    ABSTRACT: Background High-throughput molecular biology techniques yield vast amounts of data, often by detecting small portions of ribonucleotides corresponding to specific identifiers. Existing bioinformatic methodologies categorize and compare these elements using inferred descriptive annotation given this sequence information irrespective of the fact that it may not be representative of the identifier as a whole. Results All annotations, no matter the granularity, can be aligned to genomic sequences and therefore annotated by genomic intervals. We have developed AbsIDconvert, a methodology for converting between genomic identifiers by first mapping them onto a common universal coordinate system using an interval tree which is subsequently queried for overlapping identifiers. AbsIDconvert has many potential uses, including gene identifier conversion, identification of features within a genomic region, and cross-species comparisons. The utility is demonstrated in three case studies: 1) comparative genomic study mapping plasmodium gene sequences to corresponding human and mosquito transcriptional regions; 2) cross-species study of Incyte clone sequences; and 3) analysis of human Ensembl transcripts mapped by Affymetrix®; and Agilent microarray probes. AbsIDconvert currently supports ID conversion of 53 species for a given list of input identifiers, genomic sequence, or genome intervals. Conclusion AbsIDconvert provides an efficient and reliable mechanism for conversion between identifier domains of interest. The flexibility of this tool allows for custom definition identifier domains contingent upon the availability and determination of a genomic mapping interval. As the genomes and the sequences for genetic elements are further refined, this tool will become increasingly useful and accurate. AbsIDconvert is freely available as a web application or downloadable as a virtual machine at:
    BMC Bioinformatics 09/2012; 13(1):229. DOI:10.1186/1471-2105-13-229 · 2.58 Impact Factor
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    • "Several attempts have been made to overcome those hurdles. Two tools, List of lists-annotated (LOLA) [17] and List to List (L2L) [18] were created to compare gene lists against microarray data from different platforms, different nomenclatures, or even different organisms. However, these tools rely on published data and need to be manually curated. "
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    ABSTRACT: Microarray experiments are becoming increasingly common in biomedical research, as is their deposition in publicly accessible repositories, such as Gene Expression Omnibus (GEO). As such, there has been a surge in interest to use this microarray data for meta-analytic approaches, whether to increase sample size for a more powerful analysis of a specific disease (e.g. lung cancer) or to re-examine experiments for reasons different than those examined in the initial, publishing study that generated them. For the average biomedical researcher, there are a number of practical barriers to conducting such meta-analyses such as manually aggregating, filtering and formatting the data. Methods to automatically process large repositories of microarray data into a standardized, directly comparable format will enable easier and more reliable access to microarray data to conduct meta-analyses. We present a straightforward, simple but robust against potential outliers method for automatic quality control and pre-processing of tens of thousands of single-channel microarray data files. GEO GDS files are quality checked by comparing parametric distributions and quantile normalized to enable direct comparison of expression level for subsequent meta-analyses. 13,000 human 1-color experiments were processed to create a single gene expression matrix that subsets can be extracted from to conduct meta-analyses. Interestingly, we found that when conducting a global meta-analysis of gene-gene co-expression patterns across all 13,000 experiments to predict gene function, normalization had minimal improvement over using the raw data. Normalization of microarray data appears to be of minimal importance on analyses based on co-expression patterns when the sample size is on the order of thousands microarray datasets. Smaller subsets, however, are more prone to aberrations and artefacts, and effective means of automating normalization procedures not only empowers meta-analytic approaches, but aids in reproducibility by providing a standard way of approaching the problem.Data availability: matrix containing normalized expression of 20,813 genes across 13,000 experiments is available for download at . Source code for GDS files pre-processing is available from the authors upon request.
    BMC Bioinformatics 10/2011; 12 Suppl 10(Suppl 10):S2. DOI:10.1186/1471-2105-12-S10-S2 · 2.58 Impact Factor
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