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. 2005 Jul 1;33(Web Server issue):W638-43.
doi: 10.1093/nar/gki490.

RACE: Remote Analysis Computation for gene Expression data

Michael Psarros  1 , Steffen Heber, Manuela Sick, Gnanasekaran Thoppae, Keith Harshman, Beate Sick
Affiliations

RACE: Remote Analysis Computation for gene Expression data

Michael Psarros et al. Nucleic Acids Res. .

Abstract

The Remote Analysis Computation for gene Expression data (RACE) suite is a collection of bioinformatics web tools designed for the analysis of DNA microarray data. RACE performs probe-level data preprocessing, extensive quality checks, data visualization and data normalization for Affymetrix GeneChips. In addition, it offers differential expression analysis on normalized expression levels from any array platform. RACE estimates the false discovery rates of lists of potentially regulated genes and provides a Gene Ontology-term analysis tool for GeneChip data to support the biological interpretation and annotation of results. The analysis is fully automated but can be customized by flexible parameter settings. To offer a convenient starting point for subsequent analyses, and to provide maximum transparency, the R scripts used to generate the results can be downloaded along with the output files. RACE is freely available for use at http://race.unil.ch.

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Figures

Figure 1
Figure 1
‘PLM pseudo image’ tool output. The spatial distribution of residuals obtained from probe-level fitting over multiple arrays is shown. (a) High-quality data showing almost no defects; (b) low-quality data showing large artifacts.
Figure 2
Figure 2
‘Bias 5′ to 3′ end plot’ tool output. Each line represents the overall 5′ to 3′ intensity bias of a different chip.
Figure 3
Figure 3
‘p-Value histogram’ output. The number of genes (‘Frequency’) which fall into each p-value bin is presented. In the insert, the False Discovery Rate versus the p-value threshold is plotted.
Figure 4
Figure 4
‘MvA plots’ output. The expression ratio (log base 2) of genes is plotted against their average expression intensity. Circles identify genes that pass user-defined p-value and fold-change value thresholds.
Figure 5
Figure 5
‘GO-term chart’ output. Biological function GO terms calculated to be statistically overrepresented in a user-specified gene list are reported along with the number of genes from the list associated with each term.
See this image and copyright information in PMC

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