Fabio d’Alessi

Here we present a novel web tool for the statistical analysis of gene expression data in multiple tag sampling experiments. Differentially expressed genes are detected by using six different test statistics. Result tables, linked to the GenBank, UniGene, or LocusLink database, can be browsed or searched in different ways. Software is freely available at the site: http://telethon.bio.unipd.it/bioinfo/IDEG6_form/, together with additional information on statistical methodologies.

MOTIVATION: To perform a computational and statistical study on a large set of gene expression data pertaining six adult human tissues (brain, liver, skeletal muscle, ovary, retina and uterus) for analyzing the expression of ribosomal protein genes. RESULTS: Unexpectedly, in each of the considered tissues large variations in the expression of ribosomal protein genes were observed. Moreover, when comparing the expression levels of 89 ribosomal protein genes in six different tissues, 13 genes appeared differentially expressed among tissues. AVAILABILITY: The expression data of the ribosomal protein genes together with supplementary material (complete transcriptional profiles of the considered human tissues) are freely available at the site GETProfiles (http://telethon.bio.unipd.it/GETProfiles/). CONTACT: danieli@bio.unipd.it

We present the Human Muscle Gene Map (HMGM), the first comprehensive and updated high-resolution expression map of human skeletal muscle. The 1078 entries of the map were obtained by merging data retrieved from UniGene with the RH mapping information on 46 novel muscle transcripts, which showed no similarity to any known sequence. In the map, distances are expressed in megabase pairs. About one-quarter of the map entries represents putative novel genes. Genes known to be specifically expressed in muscle account for <4% of the total. The genomic distribution of the map entries confirmed the previous finding that muscle genes are selectively concentrated in chromosomes 17, 19, and X. Five chromosomal regions are suspected to have a significant excess of muscle genes. Present data support the hypothesis that the biochemical and functional properties of differentiated muscle cells may result from the transcription of a very limited number of muscle-specific genes along with the activity of a large number of genes, shared with other tissues, but showing different levels of expression in muscle. [The sequence data described in this paper have been submitted to the EMBL data library under accession nos. F23198-F23242.]

By applying a novel software tool, information on 4080 UniGene clusters was retrieved from three adult human skeletal muscle cDNA libraries, which were selected for being neither normalized nor subtracted. Reconstruction of a transcriptional profile of the corresponding tissue was attempted by a computational approach, classifying each transcript according to its level of expression. About 25% of the transcripts accounted for about 80% of the detected transcriptional activity, whereas most genes showed a low level of expression. This in silico transcriptional profile was then compared with data obtained by a SAGE study. A fairly good agreement between the two methods was observed. About 400 genes, highly expressed in skeletal muscle or putatively skeletal muscle-specific, may represent the minimal set of genes needed to determine the tissue specificity. These genes could be used as a convenient reference to monitor major changes in the transcriptional profile of adult human skeletal muscle in response to different physiological or pathological conditions, thus providing a framework for designing DNA microarrays and initiating biological studies.

PURPOSE: To reconstruct the transcriptional profile of the human adult retina and the genomic map of the genes expressed in this tissue. METHODS: Original software was used for the retrieval and analysis of records from UniGene (http://www.ncbi.nlm.nih. gov/UniGene/) pertaining to selected cDNA libraries from adult human retina. RESULTS: The 4974 genes reported so far to be expressed in retina were included in a catalog available on the Internet. For each entry, an estimation of the level of expression of the corresponding gene in the retina was provided. A high-resolution genomic map of the human retina was built up by inclusion of 3152 genes showing a precise and unique map assignment. The correspondence was established between 53 gene-orphan retinal diseases and clusters of genes expressed in the retina. CONCLUSIONS: The in silico reconstruction of the transcriptional profile of the adult human retina provides preliminary information on the pattern of genomic expression in this tissue. The chromosomal location of many retinal genes, combined with their expression data, should speed up the identification of genes involved in retinal diseases.