MOTIVATION: Data management and quality control of output from Illumina sequencers is a disk space- and time-consuming task. Thus, we developed Aozan to automatically handle data transfer, demultiplexing, conversion and quality control once a run has finished. This software greatly improves run data management and the monitoring of run statistics via automatic emails and HTML web reports. AVAILABILITY AND IMPLEMENTATION: Aozan is implemented in Java and Python, supported on Linux systems, and distributed under the GPLv3 License at: http://www.outils.genomique.biologie.ens.fr/aozan/ . Aozan source code is available on GitHub: https://github.com/GenomicParisCentre/aozan . CONTACT: [email protected].

Abstract Summary: Oligonucleotide microarray probes are designed to match specific transcripts present in databases that are regularly updated. As a consequence probes should be checked every new database release. We thus developed an informatics tool allowing the semi-automatic update of probe collections of long oligonucleotides and applied it to the mouse RefSeq database. Availability: http://www.bio.espci.fr/sol/ Contact: [email protected] Supplementary information: Supplementary data are available at http://www.bio.espci.fr/sol/

BACKGROUND: Raw data normalization is a critical step in microarray data analysis because it directly affects data interpretation. Most of the normalization methods currently used are included in the R/BioConductor packages but it is often difficult to identify the most appropriate method. Furthermore, the use of R commands for functions and graphics can introduce mistakes that are difficult to trace. We present here a script written in R that provides a flexible means of access to and monitoring of data normalization for two-color microarrays. This script combines the power of BioConductor and R analysis functions and reduces the amount of R programming required. RESULTS: Goulphar was developed in and runs using the R language and environment. It combines and extends functions found in BioConductor packages (limma and marray) to correct for dye biases and spatial artifacts. Goulphar provides a wide range of optional and customizable filters for excluding incorrect signals during the pre-processing step. It displays informative output plots, enabling the user to monitor the normalization process, and helps adapt the normalization method appropriately to the data. All these analyses and graphical outputs are presented in a single PDF report. CONCLUSION: Goulphar provides simple, rapid access to the power of the R/BioConductor statistical analysis packages, with precise control and visualization of the results obtained. Complete documentation, examples and online forms for setting script parameters are available from http://transcriptome.ens.fr/goulphar/.