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  • 2010-2014  (3)
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  • 1
    Publication Date: 2012-09-30
    Description: Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods. Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes. Availability: Implemented in the Bioconductor package iBBiG Contact: aedin@jimmy.harvard.edu
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 2
    Publication Date: 2012-03-01
    Description: : High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline. Availability: nEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev . Contact: cholmes@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 3
    Publication Date: 2013-02-02
    Description: Periodicity in nucleotide sequences arises from regular repeating patterns which may reflect important structure and function. Although a three-base periodicity in coding regions has been known for some time and has provided the basis for powerful gene prediction algorithms, its origins are still not fully understood. Here, we show that, contrary to common belief, amino acid (AA) bias and codon usage bias are insufficient to create base-3 periodicity. This article applies the rigorous method of spectral envelope to systematically characterize the contributions of codon bias, AA bias and protein structural motifs to the three-base periodicity of coding sequences. The method is also used to classify CpG islands in the human genome. In addition, we show how spectral envelope can be used to trace the evolution of viral genomes and monitor global sequence changes without having to align to previously known genomes. This approach also detects reassortment events, such as those that led to the 2009 pandemic H1N1 virus.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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