Dataset Information

Leukemia gene atlas--a public platform for integrative exploration of genome-wide molecular data.

ABSTRACT: Leukemias are exceptionally well studied at the molecular level and a wealth of high-throughput data has been published. But further utilization of these data by researchers is severely hampered by the lack of accessible integrative tools for viewing and analysis. We developed the Leukemia Gene Atlas (LGA) as a public platform designed to support research and analysis of diverse genomic data published in the field of leukemia. With respect to leukemia research, the LGA is a unique resource with comprehensive search and browse functions. It provides extensive analysis and visualization tools for various types of molecular data. Currently, its database contains data from more than 5,800 leukemia and hematopoiesis samples generated by microarray gene expression, DNA methylation, SNP and next generation sequencing analyses. The LGA allows easy retrieval of large published data sets and thus helps to avoid redundant investigations. It is accessible at www.leukemia-gene-atlas.org.

SUBMITTER: Hebestreit K

PROVIDER: S-EPMC3375295 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Leukemia gene atlas--a public platform for integrative exploration of genome-wide molecular data.

Hebestreit Katja K Gröttrup Sören S Emden Daniel D Veerkamp Jannis J Ruckert Christian C Klein Hans-Ulrich HU Müller-Tidow Carsten C Dugas Martin M

PloS one 20120614 6

Leukemias are exceptionally well studied at the molecular level and a wealth of high-throughput data has been published. But further utilization of these data by researchers is severely hampered by the lack of accessible integrative tools for viewing and analysis. We developed the Leukemia Gene Atlas (LGA) as a public platform designed to support research and analysis of diverse genomic data published in the field of leukemia. With respect to leukemia research, the LGA is a unique resource with ...[more]

PMID: 22720055

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Project description:BackgroundWe present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development.DescriptionThe Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations.ConclusionThe Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at: http://bioinformatics.ubc.ca/atlas/

Project description:BACKGROUND: Genome-wide methylation profiling has led to more comprehensive insights into gene regulation mechanisms and potential therapeutic targets. Illumina Human Methylation BeadChip is one of the most commonly used genome-wide methylation platforms. Similar to other microarray experiments, methylation data is susceptible to various technical artifacts, particularly batch effects. To date, little attention has been given to issues related to normalization and batch effect correction for this kind of data. METHODS: We evaluated three common normalization approaches and investigated their performance in batch effect removal using three datasets with different degrees of batch effects generated from HumanMethylation27 platform: quantile normalization at average ? value (QN?); two step quantile normalization at probe signals implemented in "lumi" package of R (lumi); and quantile normalization of A and B signal separately (ABnorm). Subsequent Empirical Bayes (EB) batch adjustment was also evaluated. RESULTS: Each normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset. For the dataset with minor batch effects (Dataset 1), normalization alone appeared adequate and "lumi" showed the best performance. However, all methods left substantial batch effects intact in the datasets with obvious batch effects and further correction was necessary. Without any correction, 50 and 66 percent of CpGs were associated with batch effects in Dataset 2 and 3, respectively. After QN?, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26 percent for Dataset 2; and 37, 46, and 35 percent for Dataset 3, respectively. Additional EB correction effectively removed such remaining non-biological effects. More importantly, the two-step procedure almost tripled the numbers of CpGs associated with the outcome of interest for the two datasets. CONCLUSION: Genome-wide methylation data from Infinium Methylation BeadChip can be susceptible to batch effects with profound impacts on downstream analyses and conclusions. Normalization can reduce part but not all batch effects. EB correction along with normalization is recommended for effective batch effect removal.

Dataset Information

Leukemia gene atlas--a public platform for integrative exploration of genome-wide molecular data.

Publications

Leukemia gene atlas--a public platform for integrative exploration of genome-wide molecular data.

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