Dataset Information

ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.

ABSTRACT: Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts-controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics.Here, we present a pathway profiling toolkit, Adaptive Signature Selection and InteGratioN (ASSIGN), which enables robust and context-specific pathway analyses by efficiently capturing pathway activity in heterogeneous sets of samples and across profiling technologies. The ASSIGN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease. We demonstrate the robustness and versatility of ASSIGN in estimating pathway activity in simulated data, cell lines perturbed pathways and in primary tissues samples including The Cancer Genome Atlas breast carcinoma samples and liver samples exposed to genotoxic carcinogens.Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.

SUBMITTER: Shen Y

PROVIDER: S-EPMC4443674 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.

Shen Ying Y Rahman Mumtahena M Piccolo Stephen R SR Gusenleitner Daniel D El-Chaar Nader N NN Cheng Luis L Monti Stefano S Bild Andrea H AH Johnson W Evan WE

Bioinformatics (Oxford, England) 20150122 11

<h4>Motivation</h4>Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts-controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-pr ...[more]

PMID: 25617415

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Project description:BackgroundComputational analysis of complex diseases involving multiple organs requires the integration of multiple different models into a unified model. Different models are often constructed in heterogeneous formats. Thus, the integration of the models requires a standard language format that can effectively represent essential biological information. However, the previously introduced formats have limitations that prevent from adequately representing essential biological information, particularly specifications of bio-molecules and biological contexts.ResultsWe defined an XML-based markup language called context-oriented directed association markup language (CODA-ML), which better represents essential biological information. The CODA-ML has two major strengths in designating molecular specifications and biological contexts. It can cover heterogeneous entity types involved in biological events (e.g. gene/protein, compound, cellular function, disease). Molecular types of entities can have molecular specifications which include detailed information of a molecule from isoforms to modifications, enabling high-resolution representation of molecules. In addition, it can distinguish biological events that vary depending on different biological contexts such as cell types or disease conditions. Especially representation of inter-cellular events as well as intra-cellular events is available. These two major strengths can resolve contradictory associations when different models are integrated into one unified model, which improves the accuracy of the model.ConclusionsWith the CODA-ML, diverse models such as signaling pathways, metabolic pathways, and gene regulatory pathways can be represented in a unified language format. Heterogeneous entity types can be covered by the CODA-ML, thus it enables detailed description for the mechanisms of diseases or drugs from multiple perspectives (e.g., molecule, function or disease). The CODA-ML is expected to help integrate different models into one systemic model in an efficient and effective. The unified model can be used to perform computational analysis not only for cancer but also for other complex diseases involving multiple organs beyond a single cell.

Dataset Information

ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.

Publications

ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.

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