Myc-binding-site recognition in the human genome is determined by chromatin context.

Large-scale chromatin immunoprecipitation (ChIP) studies have been effective in unravelling the distribution of DNA-binding transcription factors along eukaryotic genomes, but specificity determinants remain elusive. Gene-regulatory regions display distinct histone variants and modifications (or marks). An attractive hypothesis is that these marks modulate protein recognition, but whether or not this applies to transcription factors remains unknown. Based on large-scale datasets and quantitative ChIP, we dissect the correlations between 35 histone marks and genomic binding by the transcription factor Myc. Our data reveal a relatively simple combinatorial organization of histone marks in human cells, with a few main groups of marks clustering on distinct promoter populations. A stretch of chromatin bearing high H3 K4/K79 methylation and H3 acetylation (or 'euchromatic island'), which is generally associated with a pre-engaged basal transcription machinery, is a strict pre-requisite for recognition of any target site by Myc (whether the consensus CACGTG or an alternative sequence). These data imply that tethering of a transcription factor to restricted chromatin domains is rate-limiting for sequence-specific DNA binding in vivo.

Graph-based identification of cancer signaling pathways from published gene expression signatures using PubLiME.

Gene expression technology has become a routine application in many laboratories and has provided large amounts of gene expression signatures that have been identified in a variety of cancer types. Interpretation of gene expression signatures would profit from the availability of a procedure capable of assigning differentially regulated genes or entire gene signatures to defined cancer signaling pathways. Here we describe a graph-based approach that identifies cancer signaling pathways from published gene expression signatures. Published gene expression signatures are collected in a database (PubLiME: Published Lists of Microarray Experiments) enabled for cross-platform gene annotation. Significant co-occurrence modules composed of up to 10 genes in different gene expression signatures are identified. Significantly co-occurring genes are linked by an edge in an undirected graph. Edge-betweenness and k-clique clustering combined with graph modularity as a quality measure are used to identify communities in the resulting graph. The identified communities consist of cell cycle, apoptosis, phosphorylation cascade, extra cellular matrix, interferon and immune response regulators as well as communities of unknown function. The genes constituting different communities are characterized by common genomic features and strongly enriched cis-regulatory modules in their upstream regulatory regions that are consistent with pathway assignment of those genes.

Localizing hotspots of antisense transcription.

Analysis of the transcriptome by computational and experimental methods has established that sense-antisense transcriptional units are a common phenomenon. Although the regulatory potential of antisense transcripts has been experimentally verified in a number of studies, the biological importance of sense-antisense regulation of gene expression is still a matter of debate. Here, we report the identification of sequence features that are associated with antisense transcription. We show that the sequence composition of the first exon and the 5'end of the first intron of many human genes is similar to the sequence composition observed in promoter regions as measured by the density of known transcription regulatory motifs. Cloned intron-derived fragments were found to possess bidirectional promoter activity. In agreement with the reported abundance of antisense transcripts overlapping the 5'UTR, mapping of the 5'ends of antisense transcripts to the corresponding sense transcripts revealed that the first exon and the 5'end of the first intron are hotspots of antisense transcription as measured by the number of antisense transcription start sites per unit sequence. CpG dinucleotide suppression that is typically weak in non-methylated promoter regions is similarly weakened upstream as well as downstream of the first exon. In support of antisense transcripts playing a regulatory role, we find that 5'UTRs and first exons of genes with overlapping antisense transcripts are significantly longer than the genomic average. Interestingly, a similar size distribution of 5'UTRs and first exons is observed for genes silenced by CpG island methylation in human cancer.

DSMM: a Database of Simulated Molecular Motions.

We describe a Database of Simulated Molecular Motions (DSMM). This database is designed to serve as a single searchable site for locating movies and animations from simulations of biomolecules. DSMM is accessible via a webserver at: http://projects.villa-bosch.de/mcm/database/dsmm.

Mining published lists of cancer related microarray experiments: identification of a gene expression signature having a critical role in cell-cycle control.

BACKGROUND: Routine application of gene expression microarray technology is rapidly producing large amounts of data that necessitate new approaches of analysis. The analysis of a specific microarray experiment profits enormously from cross-comparing to other experiments. This process is generally performed by numerical meta-analysis of published data where the researcher chooses the datasets to be analyzed based on assumptions about the biological relations of published datasets to his own data, thus severely limiting the possibility of finding surprising connections. Here we propose using a repository of published gene lists for the identification of interesting datasets to be subjected to more detailed numerical analysis. RESULTS: We have compiled lists of genes that have been reported as differentially regulated in cancer related microarray studies. We searched these gene lists for statistically significant overlaps with lists of genes regulated by the tumor suppressors p16 and pRB. We identified a highly significant overlap of p16 and pRB target genes with genes regulated by the EWS/FLI fusion protein. Detailed numerical analysis of these data identified two sets of genes with clearly distinct roles in the G1/S and the G2/M phases of the cell cycle, as measured by enrichment of Gene Ontology categories. CONCLUSION: We show that mining of published gene lists in the absence of numerical detail about gene expression levels constitutes a fast, easy to perform, widely applicable, and unbiased route towards the identification of biologically related gene expression microarray datasets.

Fishing new proteins in the twilight zone of genomes: the test case of outer membrane proteins in Escherichia coli K12, Escherichia coli O157:H7, and other Gram-negative bacteria.

We address the problem of clustering the whole protein content of genomes into three different categories-globular, all-alpha, and all-beta membrane proteins-with the aim of fishing new membrane proteins in the pool of nonannotated proteins (twilight zone). The focus is then mainly on outer membrane proteins. This is performed by using an integrated suite of programs (Hunter) specifically developed for predicting the occurrence of signal peptides in proteins of Gram-negative bacteria and the topography of all-alpha and all-beta membrane proteins. Hunter is tested on the well and partially annotated proteins (2160 and 760, respectively) of Escherichia coli K 12 scoring as high as 95.6% in the correct assignment of each chain to the category. Of the remaining 1253 nonannotated sequences, 1099 are predicted globular, 136 are all-alpha, and 18 are all-beta membrane proteins. In Escherichia coli 0157:H7 we filtered 1901 nonannotated proteins. Our analysis classifies 1564 globular chains, 327 inner membrane proteins, and 10 outer membrane proteins. With Hunter, new membrane proteins are added to the list of putative membrane proteins of Gram-negative bacteria. The content of outer membrane proteins per genome (nine are analyzed) ranges from 1.5% to 2.4%, and it is one order of magnitude lower than that of inner membrane proteins. The finding is particularly relevant when it is considered that this is the first large-scale analysis based on validated tools that can predict the content of outer membrane proteins in a genome and can allow cross-comparison of the same protein type between different species.

Automated DNA chip annotation tables at IFOM: the importance of synchronisation and cross-referencing of sequence databases.

The increasing popularity of DNA chip technology for the study of gene expression is producing, for each experiment, a sizable quantity of numerical data to analyse and an accompanying large number of gene identifiers that should be associated with the relevant biological annotation. We describe here a website at IFOM (FIRC Institute of Molecular Oncology) where we release regularly updated annotation tables for the most used Affymetrix oligonucleotide DNA chips and for the whole Research Genetics 46K clone collection for cDNA arrays. These tables are synchronised with every new release of the mouse and human UniGene databases (NCBI; National Center for Biotechnology Information), allowing fast and easy preliminary annotation of DNA array experiments. We also report some comparative evidence about the importance of biological database synchronisation and cross-references in the process of generating annotation tables for DNA chips.

Lap2alpha expression is controlled by E2F and deregulated in various human tumors.

Deregulation of the retinoblastoma (pRB) tumor suppressor pathway is frequently observed in human cancer and associated with aberrant activity of E2F transcription factors. We have performed microarray based analysis with the aim of identifying potential downstream mediators of the tumor suppressing activity of pRB. Here we report that the expression of LAP2 (lamina-associated polypeptide 2) is under direct control of E2F transcription factors. Chromatin immunoprecipitation assays show that the LAP2 promoter is bound by endogenous E2F in vivo. The LAP2 promoter is transactivated by ectopically expressed E2F and mutation of E2F binding sites eliminates this effect. We studied the expression level of LAP2alpha in human tumors by tissue microarray analysis and found LAP2alpha over expression in a significant percentage of primary larynx, lung, stomach, breast, and colon cancer tissues. In agreement with its regulation by E2F, LAP2alpha over expression in primary tumors was found to be correlated with tumor proliferation rate.

SPEPlip: the detection of signal peptide and lipoprotein cleavage sites.

SUMMARY: SPEPlip is a neural network-based method, trained and tested on a set of experimentally derived signal peptides from eukaryotes and prokaryotes. SPEPlip identifies the presence of sorting signals and predicts their cleavage sites. The accuracy in cross-validation is similar to that of other available programs: the rate of false positives is 4 and 6%, for prokaryotes and eukaryotes respectively and that of false negatives is 3% in both cases. When a set of 409 prokaryotic lipoproteins is predicted, SPEPlip predicts 97% of the chains in the signal peptide class. However, by integrating SPEPlip with a regular expression search utility based on the PROSITE pattern, we can successfully discriminate signal peptide-containing chains from lipoproteins. We propose the method for detecting and discriminating signal peptides containing chains and lipoproteins. AVAILABILITY: It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/

GenePicker: replicate analysis of Affymetrix gene expression microarrays.

UNLABELLED: GenePicker allows efficient analysis of Affymetrix gene expression data performed in replicate, through definition of analysis schemes, data normalization, t-test/ANOVA, Change-Fold Change-analysis and yields lists of differentially expressed genes with high confidence. Comparison of noise and signal analysis schemes allows determining a signal-to-noise ratio in a given experiment. Change Call, Fold Change and Signal mean ratios are used in the analysis. While each parameter alone yields gene lists that contain up to 30% false positives, the combination of these parameters nearly eliminates the false positives as verified by northern blotting, quantitative PCR in numerous independent experiments as well as by the analysis of spike-in data. AVAILABILITY: http://www.ifom-firc.it/RESEARCH/Appl_Bioinfo/tools.html. SUPPLEMENTARY INFORMATION: http://www.ifom-firc.it/RESEARCH/Appl_Bioinfo/tools.html.