NPRD: Nucleosome Positioning Region Database

Nucleosome Positioning Region Database (NPRD), which is compiling the available experimental data on locations and characteristics of nucleosome formation sites (NFSs), is the first curated NFS-oriented database. The object of the database is a single NFS described in an individual entry. When annotating results of NFS experimental mapping, we pay special attention to several important functional characteristics, such as the relationship between type of gene activity and nucleosome positioning, the influence of non-histone proteins on nucleosome formation, type of the variant of nucleosome positioning (translational or rotational), indication of tissue types and states of cell activity, description of experimental methods used and accuracy of nucleosome position determination, and the results of applying theoretical and computer methods to the analysis of contextual and conformational DNA properties. At present, the NPRD database contains 438 entries and integrates the data described in 124 original papers. The database URL: http://srs6.bionet.nsc.ru/srs6/. Then click the button ‘Databank’ and open the link NUCLEOSOME

Noncoding DNA, isochores and gene expression: nucleosome formation potential

The nucleosome formation potential of introns, intergenic spacers and exons of human genes is shown here to negatively correlate with among-tissues breadth of gene expression. The nucleosome formation potential is also found to negatively correlate with the GC content of genomic sequences; the slope of regression line is steeper in exons compared with noncoding DNA (introns and intergenic spacers). The correlation with GC content is independent of sequence length; in turn, the nucleosome formation potential of introns and intergenic spacers positively (albeit weakly) correlates with sequence length independently of GC content. These findings help explain the functional significance of the isochores (regions differing in GC content) in the human genome as a result of optimization of genomic structure for epigenetic complexity and support the notion that noncoding DNA is important for orderly chromatin condensation and chromatin-mediated suppression of tissue-specific genes

Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions

<p>Abstract</p> <p>Background</p> <p>Reliable transcription factor binding site (TFBS) prediction methods are essential for computer annotation of large amount of genome sequence data. However, current methods to predict TFBSs are hampered by the high false-positive rates that occur when only sequence conservation at the core binding-sites is considered.</p> <p>Results</p> <p>To improve this situation, we have quantified the performance of several Position Weight Matrix (PWM) algorithms, using exhaustive approaches to find their optimal length and position. We applied these approaches to bio-medically important TFBSs involved in the regulation of cell growth and proliferation as well as in inflammatory, immune, and antiviral responses (NF-κB, ISGF3, IRF1, STAT1), obesity and lipid metabolism (PPAR, SREBP, HNF4), regulation of the steroidogenic (SF-1) and cell cycle (E2F) genes expression. We have also gained extra specificity using a method, entitled SiteGA, which takes into account structural interactions within TFBS core and flanking regions, using a genetic algorithm (GA) with a discriminant function of locally positioned dinucleotide (LPD) frequencies.</p> <p>To ensure a higher confidence in our approach, we applied resampling-jackknife and bootstrap tests for the comparison, it appears that, optimized PWM and SiteGA have shown similar recognition performances. Then we applied SiteGA and optimized PWMs (both separately and together) to sequences in the Eukaryotic Promoter Database (EPD). The resulting SiteGA recognition models can now be used to search sequences for BSs using the web tool, SiteGA.</p> <p>Analysis of dependencies between close and distant LPDs revealed by SiteGA models has shown that the most significant correlations are between close LPDs, and are generally located in the core (footprint) region. A greater number of less significant correlations are mainly between distant LPDs, which spanned both core and flanking regions. When SiteGA and optimized PWM models were applied together, this substantially reduced false positives at least at higher stringencies.</p> <p>Conclusion</p> <p>Based on this analysis, SiteGA adds substantial specificity even to optimized PWMs and may be considered for large-scale genome analysis. It adds to the range of techniques available for TFBS prediction, and EPD analysis has led to a list of genes which appear to be regulated by the above TFs.</p