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1.  RamiGO: an R/Bioconductor package providing an AmiGO Visualize interface 
Bioinformatics  2013;29(5):666-668.
Summary: The R/Bioconductor package RamiGO is an R interface to AmiGO that enables visualization of Gene Ontology (GO) trees. Given a list of GO terms, RamiGO uses the AmiGO visualize API to import Graphviz-DOT format files into R, and export these either as images (SVG, PNG) or into Cytoscape for extended network analyses. RamiGO provides easy customization of annotation, highlighting of specific GO terms, colouring of terms by P-value or export of a simplified summary GO tree. We illustrate RamiGO functionalities in a genome-wide gene set analysis of prognostic genes in breast cancer.
Availability and implementation: RamiGO is provided in R/Bioconductor, is open source under the Artistic-2.0 License and is available with a user manual containing installation, operating instructions and tutorials. It requires R version 2.15.0 or higher. URL: http://bioconductor.org/packages/release/bioc/html/RamiGO.html
Contact: markus.schroeder@ucdconnect.ie
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/bts708
PMCID: PMC3582261  PMID: 23297033
2.  survcomp: an R/Bioconductor package for performance assessment and comparison of survival models 
Bioinformatics  2011;27(22):3206-3208.
Summary: The survcomp package provides functions to assess and statistically compare the performance of survival/risk prediction models. It implements state-of-the-art statistics to (i) measure the performance of risk prediction models; (ii) combine these statistical estimates from multiple datasets using a meta-analytical framework; and (iii) statistically compare the performance of competitive models.
Availability: The R/Bioconductor package survcomp is provided open source under the Artistic-2.0 License with a user manual containing installation, operating instructions and use case scenarios on real datasets. survcomp requires R version 2.13.0 or higher. http://bioconductor.org/packages/release/bioc/html/survcomp.html
Contact: bhaibeka@jimmy.harvard.edu; mschroed@jimmy.harvard.edu
Supplementary Information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr511
PMCID: PMC3208391  PMID: 21903630
3.  Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV 
Bioinformatics  2011;27(19):2648-2654.
Motivation: The ability to detect copy-number variation (CNV) and loss of heterozygosity (LOH) from exome sequencing data extends the utility of this powerful approach that has mainly been used for point or small insertion/deletion detection.
Results: We present ExomeCNV, a statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies, from mapped short sequence reads, and we assess both the method's power and the effects of confounding variables. We apply our method to a cancer exome resequencing dataset. As expected, accuracy and resolution are dependent on depth-of-coverage and capture probe design.
Availability: CRAN package ‘ExomeCNV’.
Contact: fsathira@fas.harvard.edu; snelson@ucla.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr462
PMCID: PMC3179661  PMID: 21828086
4.  iBBiG: iterative binary bi-clustering of gene sets 
Bioinformatics  2012;28(19):2484-2492.
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
doi:10.1093/bioinformatics/bts438
PMCID: PMC3463116  PMID: 22789589
5.  RNA-Seq analysis in MeV 
Bioinformatics  2011;27(22):3209-3210.
Summary: RNA-Seq is an exciting methodology that leverages the power of high-throughput sequencing to measure RNA transcript counts at an unprecedented accuracy. However, the data generated from this process are extremely large and biologist-friendly tools with which to analyze it are sorely lacking. MultiExperiment Viewer (MeV) is a Java-based desktop application that allows advanced analysis of gene expression data through an intuitive graphical user interface. Here, we report a significant enhancement to MeV that allows analysis of RNA-Seq data with these familiar, powerful tools. We also report the addition to MeV of several RNA-Seq-specific functions, addressing the differences in analysis requirements between this data type and traditional gene expression data. These tools include automatic conversion functions from raw count data to processed RPKM or FPKM values and differential expression detection and functional annotation enrichment detection based on published methods.
Availability: MeV version 4.7 is written in Java and is freely available for download under the terms of the open-source Artistic License version 2.0. The website (http://mev.tm4.org/) hosts a full user manual as well as a short quick-start guide suitable for new users.
Contact: johnq@jimmy.harvard.edu
doi:10.1093/bioinformatics/btr490
PMCID: PMC3208390  PMID: 21976420
6.  Defining an informativeness metric for clustering gene expression data 
Bioinformatics  2011;27(8):1094-1100.
Motivation: Unsupervised ‘cluster’ analysis is an invaluable tool for exploratory microarray data analysis, as it organizes the data into groups of genes or samples in which the elements share common patterns. Once the data are clustered, finding the optimal number of informative subgroups within a dataset is a problem that, while important for understanding the underlying phenotypes, is one for which there is no robust, widely accepted solution.
Results: To address this problem we developed an ‘informativeness metric’ based on a simple analysis of variance statistic that identifies the number of clusters which best separate phenotypic groups. The performance of the informativeness metric has been tested on both experimental and simulated datasets, and we contrast these results with those obtained using alternative methods such as the gap statistic.
Availability: The method has been implemented in the Bioconductor R package attract; it is also freely available from http://compbio.dfci.harvard.edu/pubs/attract_1.0.1.zip.
Contact: jess@jimmy.harvard.edu; johnq@jimmy.harvard.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr074
PMCID: PMC3072547  PMID: 21330289
7.  It is time to end the patenting of software 
Bioinformatics (Oxford, England)  2006;22(12):1416-1417.
doi:10.1093/bioinformatics/btl167
PMCID: PMC2836512  PMID: 16766564

Results 1-7 (7)