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A human polyomavirus was recently discovered in Merkel cell carcinoma (MCC) specimens. The Merkel cell polyomavirus (MCPyV) genome undergoes clonal integration into the host cell chromosomes of MCC tumors and expresses small T antigen and truncated large T antigen. Previous studies have consistently reported that MCPyV can be detected in approximately 80% of all MCC tumors. We sought to increase the sensitivity of detection of MCPyV in MCC by developing antibodies capable of detecting large T antigen by immunohistochemistry. In addition, we expanded the repertoire of quantitative PCR primers specific for MCPyV to improve the detection of viral DNA in MCC. Here we report that a novel monoclonal antibody detected MCPyV large T antigen expression in 56 of 58 (97%) unique MCC tumors. PCR analysis specifically detected viral DNA in all 60 unique MCC tumors tested. We also detected inactivating point substitution mutations of TP53 in the two MCC specimens that lacked large T antigen expression and in only 1 of 56 tumors positive for large T antigen. These results indicate that MCPyV is present in MCC tumors more frequently than previously reported and that mutations in TP53 tend to occur in MCC tumors that fail to express MCPyV large T antigen.

VisANT is a web-based software framework for visualizing and analyzing many types of networks of biological interactions and associations. Networks are a useful computational tool for representing many types of biological data, such as biomolecular interactions, cellular pathways and functional modules. Given user-defined sets of interactions or groupings between genes or proteins, VisANT provides: (i) a visual interface for combining and annotating network data, (ii) supporting function and annotation data for different genomes from the Gene Ontology and KEGG databases and (iii) the statistical and analytical tools needed for extracting topological properties of the user-defined networks. Users can customize, modify, save and share network views with other users, and import basic network data representations from their own data sources, and from standard exchange formats such as PSI-MI and BioPAX. The software framework we employ also supports the development of more sophisticated visualization and analysis functions through its open API for Java-based plug-ins. VisANT is distributed freely via the web at http://visant.bu.edu and can also be downloaded for individual use.

Independent determination of both haplotype sequences of an individual genome is essential to relate genetic variation to genome function, phenotype, and disease. To address the importance of phase, we have generated the most complete haplotype-resolved genome to date, “Max Planck One” (MP1), by fosmid pool-based next generation sequencing. Virtually all SNPs (>99%) and 80,000 indels were phased into haploid sequences of up to 6.3 Mb (N50 ∼1 Mb). The completeness of phasing allowed determination of the concrete molecular haplotype pairs for the vast majority of genes (81%) including potential regulatory sequences, of which >90% were found to be constituted by two different molecular forms. A subset of 159 genes with potentially severe mutations in either cis or trans configurations exemplified in particular the role of phase for gene function, disease, and clinical interpretation of personal genomes (e.g., BRCA1). Extended genomic regions harboring manifold combinations of physically and/or functionally related genes and regulatory elements were resolved into their underlying “haploid landscapes,” which may define the functional genome. Moreover, the majority of genes and functional sequences were found to contain individual or rare SNPs, which cannot be phased from population data alone, emphasizing the importance of molecular phasing for characterizing a genome in its molecular individuality. Our work provides the foundation to understand that the distinction of molecular haplotypes is essential to resolve the (inherently individual) biology of genes, genomes, and disease, establishing a reference point for “phase-sensitive” personal genomics. MP1's annotated haploid genomes are available as a public resource.

High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps—the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data. Electronic Supplementary Material Supplementary material is available in the online version of this article at http://dx.doi.org/10.1007/s11693-006-9003-3 and is accessible for authorized users.

Background An important goal in bioinformatics is to unravel the network of transcription factors (TFs) and their targets. This is important in the human genome, where many TFs are involved in disease progression. Here, classification methods are applied to identify new targets for 152 transcriptional regulators using publicly-available targets as training examples. Three types of sequence information are used: composition, conservation, and overrepresentation. Results Starting with 8817 TF-target interactions we predict an additional 9333 targets for 152 TFs. Randomized classifiers make few predictions (~2/18660) indicating that our predictions for many TFs are significantly enriched for true targets. An enrichment score is calculated and used to filter new predictions. Two case-studies for the TFs OCT4 and WT1 illustrate the usefulness of our predictions: • Many predicted OCT4 targets fall into the Wnt-pathway. This is consistent with known biology as OCT4 is developmentally related and Wnt pathway plays a role in early development. • Beginning with 15 known targets, 354 predictions are made for WT1. WT1 has a role in formation of Wilms' tumor. Chromosomal regions previously implicated in Wilms' tumor by cytological evidence are statistically enriched in predicted WT1 targets. These findings may shed light on Wilms' tumor progression, suggesting that the tumor progresses either by loss of WT1 or by loss of regions harbouring its targets. • Targets of WT1 are statistically enriched for cancer related functions including metastasis and apoptosis. Among new targets are BAX and PDE4B, which may help mediate the established anti-apoptotic effects of WT1. • Of the thirteen TFs found which co-regulate genes with WT1 (p ≤ 0.02), 8 have been previously implicated in cancer. The regulatory-network for WT1 targets in genomic regions relevant to Wilms' tumor is provided. Conclusion We have assembled a set of features for the targets of human TFs and used them to develop classifiers for the determination of new regulatory targets. Many predicted targets are consistent with the known biology of their regulators, and new targets for the Wilms' tumor regulator, WT1, are proposed. We speculate that Wilms' tumor development is mediated by chromosomal rearrangements in the location of WT1 targets. Reviewers This article was reviewed by Trey Ideker, Vladimir A. Kuznetsov(nominated by Frank Eisenhaber), and Tzachi Pilpel.

Background An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties. Principal Findings (1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4). (2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression. (3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties. (4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter. Conclusion Postprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite. Reviewers This article was reviewed by Igor Jouline, Todd Mockler(nominated by Valerian Dolja), and Sandor Pongor.