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1.  Interfaces to PeptideAtlas: a case study of standard data access systems 
Briefings in Bioinformatics  2011;13(5):615-626.
Access to public data sets is important to the scientific community as a resource to develop new experiments or validate new data. Projects such as the PeptideAtlas, Ensembl and The Cancer Genome Atlas (TCGA) offer both access to public data and a repository to share their own data. Access to these data sets is often provided through a web page form and a web service API. Access technologies based on web protocols (e.g. http) have been in use for over a decade and are widely adopted across the industry for a variety of functions (e.g. search, commercial transactions, and social media). Each architecture adapts these technologies to provide users with tools to access and share data. Both commonly used web service technologies (e.g. REST and SOAP), and custom-built solutions over HTTP are utilized in providing access to research data. Providing multiple access points ensures that the community can access the data in the simplest and most effective manner for their particular needs. This article examines three common access mechanisms for web accessible data: BioMart, caBIG, and Google Data Sources. These are illustrated by implementing each over the PeptideAtlas repository and reviewed for their suitability based on specific usages common to research. BioMart, Google Data Sources, and caBIG are each suitable for certain uses. The tradeoffs made in the development of the technology are dependent on the uses each was designed for (e.g. security versus speed). This means that an understanding of specific requirements and tradeoffs is necessary before selecting the access technology.
doi:10.1093/bib/bbr067
PMCID: PMC3431717  PMID: 22941959
BioMart; Google Data Sources; caBIG; data access; proteomics
2.  Affymetrix GeneChip microarray preprocessing for multivariate analyses 
Briefings in Bioinformatics  2011;13(5):536-546.
Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses—detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.
doi:10.1093/bib/bbr072
PMCID: PMC3431718  PMID: 22210854
microarray; preprocessing; gene expression; multivariate analysis
3.  Obituary: Walter Fitch and the orthology paradigm 
Briefings in Bioinformatics  2011;12(5):377-378.
doi:10.1093/bib/bbr058
PMCID: PMC3178060  PMID: 21949265

Results 1-3 (3)