PolyDoms: a whole genome database for the identification of non-synonymous coding SNPs with the potential to impact disease

As knowledge of human genetic polymorphisms grows, so does the opportunity and challenge of identifying those polymorphisms that may impact the health or disease risk of an individual person. A critical need is to organize large-scale polymorphism analyses and to prioritize candidate non-synonymous coding SNPs (nsSNPs) that should be tested in experimental and epidemiological studies to establish their context-specific impacts on protein function. In addition, with emerging high-resolution clinical genetics testing, new polymorphisms must be analyzed in the context of all available protein feature knowledge including other known mutations and polymorphisms. To approach this, we developed PolyDoms () as a database to integrate the results of multiple algorithmic procedures and functional criteria applied to the entire Entrez dbSNP dataset. In addition to predicting structural and functional impacts of all nsSNPs, filtering functions enable group-based identification of potentially harmful nsSNPs among multiple genes associated with specific diseases, anatomies, mammalian phenotypes, gene ontologies, pathways or protein domains. PolyDoms, thus, provides a means to derive a list of candidate SNPs to be evaluated in experimental or epidemiological studies for impact on protein functions and disease risk associations. PolyDoms will continue to be curated to improve its usefulness

CisMols Analyzer: identification of compositionally similar cis-element clusters in ortholog conserved regions of coordinately expressed genes

Combinatorial interactions of sequence-specific trans-acting factors with localized genomic cis-element clusters are the principal mechanism for regulating tissue-specific and developmental gene expression. With the emergence of expanding numbers of genome-wide expression analyses, the identification of the cis-elements responsible for specific patterns of transcriptional regulation represents a critical area of investigation. Computational methods for the identification of functional cis-regulatory modules are difficult to devise, principally because of the short length and degenerate nature of individual cis-element binding sites and the inherent complexity that is generated by combinatorial interactions within cis-clusters. Filtering candidate cis-element clusters based on phylogenetic conservation is helpful for an individual ortholog gene pair, but combining data from cis-conservation and coordinate expression across multiple genes is a more difficult problem. To approach this, we have extended an ortholog gene-pair database with additional analytical architecture to allow for the analysis and identification of maximal numbers of compositionally similar and phylogenetically conserved cis-regulatory element clusters from a list of user-selected genes. The system has been successfully tested with a series of functionally related and microarray profile-based co-expressed ortholog pairs of promoters and genes using known regulatory regions as training sets and co-expressed genes in the olfactory and immunohematologic systems as test sets. CisMols Analyzer is accessible via a Web interface at

GenomeTrafac: a whole genome resource for the detection of transcription factor binding site clusters associated with conventional and microRNA encoding genes conserved between mouse and human gene orthologs

Transcriptional cis-regulatory control regions frequently are found within non-coding DNA segments conserved across multi-species gene orthologs. Adopting a systematic gene-centric pipeline approach, we report here the development of a web-accessible database resource—GenomeTraFac ()—that allows genome-wide detection and characterization of compositionally similar cis-clusters that occur in gene orthologs between any two genomes for both microRNA genes as well as conventional RNA-encoding genes. Each ortholog gene pair can be scanned to visualize overall conserved sequence regions, and within these, the relative density of conserved cis-element motif clusters form graph peak structures. The results of these analyses can be mined en masse to identify most frequently represented cis-motifs in a list of genes. The system also provides a method for rapid evaluation and visualization of gene model-consistency between orthologs, and facilitates consideration of the potential impact of sequence variation in conserved non-coding regions to impact complex cis-element structures. Using the mouse and human genomes via the NCBI Reference Sequence database and the Sanger Institute miRBase, the system demonstrated the ability to identify validated transcription factor targets within promoter and distal genomic regulatory regions of both conventional and microRNA genes

Regression based predictor for p53 transactivation

<p>Abstract</p> <p>Background</p> <p>The p53 protein is a master regulator that controls the transcription of many genes in various pathways in response to a variety of stress signals. The extent of this regulation depends in part on the binding affinity of p53 to its response elements (REs). Traditional profile scores for p53 based on position weight matrices (PWM) are only a weak indicator of binding affinity because the level of binding also depends on various other factors such as interaction between the nucleotides and, in case of p53-REs, the extent of the spacer between the dimers.</p> <p>Results</p> <p>In the current study we introduce a novel <it>in-silico </it>predictor for p53-RE transactivation capability based on a combination of multidimensional scaling and multinomial logistic regression. Experimentally validated known p53-REs along with their transactivation capabilities are used for training. Through cross-validation studies we show that our method outperforms other existing methods. To demonstrate the utility of this method we (a) rank putative p53-REs of target genes and target microRNAs based on the predicted transactivation capability and (b) study the implication of polymorphisms overlapping p53-RE on its transactivation capability.</p> <p>Conclusion</p> <p>Taking into account both nucleotide interactions and the spacer length of p53-RE, we have created a novel <it>in-silico </it>regression-based transactivation capability predictor for p53-REs and used it to analyze validated and novel p53-REs and to predict the impact of SNPs overlapping these elements.</p

VisCap: inference and visualization of germ-line copy-number variants from targeted clinical sequencing data

Purpose: To develop and validate VisCap, a software program targeted to clinical laboratories for inference and visualization of germ-line copy-number variants (CNVs) from targeted next-generation sequencing data. Genet Med 18 7, 712–719. Methods: VisCap calculates the fraction of overall sequence coverage assigned to genomic intervals and computes log2 ratios of these values to the median of reference samples profiled using the same test configuration. Candidate CNVs are called when log2 ratios exceed user-defined thresholds. Genet Med 18 7, 712–719. Results: We optimized VisCap using 14 cases with known CNVs, followed by prospective analysis of 1,104 cases referred for diagnostic DNA sequencing. To verify calls in the prospective cohort, we used droplet digital polymerase chain reaction (PCR) to confirm 10/27 candidate CNVs and 72/72 copy-neutral genomic regions scored by VisCap. We also used a genome-wide bead array to confirm the absence of CNV calls across panels applied to 10 cases. To improve specificity, we instituted a visual scoring system that enabled experienced reviewers to differentiate true-positive from false-positive calls with minimal impact on laboratory workflow. Genet Med 18 7, 712–719. Conclusions: VisCap is a sensitive method for inferring CNVs from targeted sequence data from targeted gene panels. Visual scoring of data underlying CNV calls is a critical step to reduce false-positive calls for follow-up testing. Genet Med 18 7, 712–719