UNLABELLED: Gene expression index calculations from Affymetrix GeneChips have been dominated by the Affymetrix MAS, dChip, and RMA methods. A new method to estimate the gene expression value utilizing the probe sequence information named position-dependent nearest-neighbor (PDNN) has been suggested by Zhang et al. (2003). Here we describe an open source implementation of the PDNN method for the statistical language R. AVAILABILITY: The package can be downloaded from http://www.bioconductor.org/repository/devel/package/html/affypdnn.html CONTACT: [email protected].

BACKGROUND: Short oligonucleotide arrays have several probes measuring the expression level of each target transcript. Therefore the selection of probes is a key component for the quality of measurements. However, once probes have been selected and synthesized on an array, it is still possible to re-evaluate the results using an updated mapping of probes to genes, taking into account the latest biological knowledge available. METHODS: We investigated how probes found on recent commercial microarrays for human genes (Affymetrix HG-U133A) were matching a recent curated collection of human transcripts: the NCBI RefSeq database. We also built mappings and used them in place of the original probe to genes associations provided by the manufacturer of the arrays. RESULTS: In a large number of cases, 36%, the probes matching a reference sequence were consistent with the grouping of probes by the manufacturer of the chips. For the remaining cases there were discrepancies and we show how that can affect the analysis of data. CONCLUSIONS: While the probes on Affymetrix arrays remain the same for several years, the biological knowledge concerning the genomic sequences evolves rapidly. Using up-to-date knowledge can apparently change the outcome of an analysis.

Abstract Summary: PROBEWIZ designs PCR primers for amplifying probes for cDNA arrays. The probes are designed to have minimal homology to other expressed sequences from a given organism. The primer selection is based on user-defined penalties for homology, primer quality, and proximity to the 3′end. Availability: PROBEWIZ is accessible from http://www.cbs.dtu.dk/services/DNAarray/probewiz.html Contact: [email protected]; [email protected]

UNLABELLED: We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero and four errors in the predicted 1300 bp sequence when tested on wild-type TP53 sequence. AVAILABILITY: The trained neural network is available for academic use by contacting [email protected]

MOTIVATION: The whole genomes submitted to GenBank contain valuable information about the function of genes as well as the upstream sequences and whole cell expression provides valuable information on gene regulation. To utilize these large amounts of data for a biological understanding of the regulation of gene expression, new automatic methods for pattern finding are needed. RESULTS: Two word-analysis algorithms for automatic discovery of regulatory sequence elements have been developed. We show that sequence patterns correlated to whole cell expression data can be found using Kolmogorov-Smirnov tests on the raw data, thereby eliminating the need for clustering co-regulated genes. Regulatory elements have also been identified by systematic calculations of the significance of correlations between words found in the functional annotation of genes and DNA words occurring in their promoter regions. Application of these algorithms to the Saccharomyces cerevisiae genome and publicly available DNA array data sets revealed a highly conserved 9-mer occurring in the upstream regions of genes coding for proteasomal subunits. Several other putative and known regulatory elements were also found. AVAILABILITY: Upon request.

MOTIVATION: A new approach to the prediction of eukaryotic PolII promoters from DNA sequence takes advantage of a combination of elements similar to neural networks and genetic algorithms to recognize a set of discrete subpatterns with variable separation as one pattern: a promoter. The neural networks use as input a small window of DNA sequence, as well as the output of other neural networks. Through the use of genetic algorithms, the weights in the neural networks are optimized to discriminate maximally between promoters and non-promoters. RESULTS: After several thousand generations of optimization, the algorithm was able to discriminate between vertebrate promoter and non-promoter sequences in a test set with a correlation coefficient of 0.63. In addition, all five known transcription start sites on the plus strand of the complete adenovirus genome were within 161 bp of 35 predicted transcription start sites. On standardized test sets consisting of human genomic DNA, the performance of Promoter2.0 compares well with other software developed for the same purpose. AVAILABILITY: Promoter2.0 is available as a Web server at http://www.cbs.dtu. dk/services/promoter/