DoOP: Databases of Orthologous Promoters, collections of clusters of orthologous upstream sequences from chordates and plants

DoOP (http://doop.abc.hu/) is a database of eukaryotic promoter sequences (upstream regions) aiming to facilitate the recognition of regulatory sites conserved between species. The annotated first exons of human and Arabidopsis thaliana genes were used as queries in BLAST searches to collect the most closely related orthologous first exon sequences from Chordata and Viridiplantae species. Up to 3000 bp DNA segments upstream from these first exons constitute the clusters in the chordate and plant sections of the Database of Orthologous Promoters. Release 1.0 of DoOP contains 21 061 chordate clusters from 284 different species and 7548 plant clusters from 269 different species. The database can be used to find and retrieve promoter sequences of a given gene from various species and it is also suitable to see the most trivial conserved sequence blocks in the orthologous upstream regions. Users can search DoOP with either sequence or text (annotation) to find promoter clusters of various genes. In addition to the sequence data, the positions of the conserved sequence blocks derived from multiple alignments, the positions of repetitive elements and the positions of transcription start sites known from the Eukaryotic Promoter Database (EPD) can be viewed graphically

Pseudofam: the pseudogene families database

Pseudofam (http://pseudofam.pseudogene.org) is a database of pseudogene families based on the protein families from the Pfam database. It provides resources for analyzing the family structure of pseudogenes including query tools, statistical summaries and sequence alignments. The current version of Pseudofam contains more than 125 000 pseudogenes identified from 10 eukaryotic genomes and aligned within nearly 3000 families (approximately one-third of the total families in PfamA). Pseudofam uses a large-scale parallelized homology search algorithm (implemented as an extension of the PseudoPipe pipeline) to identify pseudogenes. Each identified pseudogene is assigned to its parent protein family and subsequently aligned to each other by transferring the parent domain alignments from the Pfam family. Pseudogenes are also given additional annotation based on an ontology, reflecting their mode of creation and subsequent history. In particular, our annotation highlights the association of pseudogene families with genomic features, such as segmental duplications. In addition, pseudogene families are associated with key statistics, which identify outlier families with an unusual degree of pseudogenization. The statistics also show how the number of genes and pseudogenes in families correlates across different species. Overall, they highlight the fact that housekeeping families tend to be enriched with a large number of pseudogenes

An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis

Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is a major cause of cancer-associated mortality. Secondary bone cancer (SBC) is a complex disease caused by metastasis of tumor cells from their primary site and is characterized by intricate interplay of molecular interactions. Identification of targets for multifactorial diseases such as SBC, the most frequent complication of breast and prostate cancers, is a challenge. Towards achieving our aim of identification of targets specific to SBC, we constructed a 'Cancer Genes Network', a representative protein interactome of cancer genes. Using graph theoretical methods, we obtained a set of key genes that are relevant for generic mechanisms of cancers and have a role in biological essentiality. We also compiled a curated dataset of 391 SBC genes from published literature which serves as a basis of ontological correlates of secondary bone cancer. Building on these results, we implement a strategy based on generic cancer genes, SBC genes and gene ontology enrichment method, to obtain a set of targets that are specific to bone metastasis. Through this study, we present an approach for probing one of the major complications in cancers, namely, metastasis. The results on genes that play generic roles in cancer phenotype, obtained by network analysis of 'Cancer Genes Network', have broader implications in understanding the role of molecular regulators in mechanisms of cancers. Specifically, our study provides a set of potential targets that are of ontological and regulatory relevance to secondary bone cancer.Comment: 54 pages (19 pages main text; 11 Figures; 26 pages of supplementary information). Revised after critical reviews. Accepted for Publication in PLoS ON

Immunological network signatures of cancer progression and survival

<p>Abstract</p> <p>Background</p> <p>The immune contribution to cancer progression is complex and difficult to characterize. For example in tumors, immune gene expression is detected from the combination of normal, tumor and immune cells in the tumor microenvironment. Profiling the immune component of tumors may facilitate the characterization of the poorly understood roles immunity plays in cancer progression. However, the current approaches to analyze the immune component of a tumor rely on incomplete identification of immune factors.</p> <p>Methods</p> <p>To facilitate a more comprehensive approach, we created a ranked immunological relevance score for all human genes, developed using a novel strategy that combines text mining and information theory. We used this score to assign an immunological grade to gene expression profiles, and thereby quantify the immunological component of tumors. This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies. To further characterize immunological relevance for genes, the relevance score was charted against both the human interactome and cancer information, forming an expanded interactome landscape of tumor immunity. We applied this approach to expression profiles in melanomas, thus identifying and grading their immunological components, followed by identification of their associated protein interactions.</p> <p>Results</p> <p>The power of this strategy was demonstrated by the observation of early activation of the adaptive immune response and the diversity of the immune component during melanoma progression. Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival.</p> <p>Conclusions</p> <p>The assignment of a ranked immunological relevance score to all human genes extends the content of existing immune gene resources and enriches our understanding of immune involvement in complex biological networks. The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease. In so doing, it stratifies patients according to their immune profiles, which may lead to effective computational prognostic and clinical guides.</p

ProKinO: An Ontology for Integrative Analysis of Protein Kinases in Cancer

Protein kinases are a large and diverse family of enzymes that are genomically altered in many human cancers. Targeted cancer genome sequencing efforts have unveiled the mutational profiles of protein kinase genes from many different cancer types. While mutational data on protein kinases is currently catalogued in various databases, integration of mutation data with other forms of data on protein kinases such as sequence, structure, function and pathway is necessary to identify and characterize key cancer causing mutations. Integrative analysis of protein kinase data, however, is a challenge because of the disparate nature of protein kinase data sources and data formats., where the mutations are spread over 82 distinct kinases. We also provide examples of how ontology-based data analysis can be used to generate testable hypotheses regarding cancer mutations.

Genome-wide promoter analysis of histone modifications in human monocyte-derived antigen presenting cells

<p>Abstract</p> <p>Background</p> <p>Monocyte-derived macrophages and dendritic cells (DCs) are important in inflammatory processes and are often used for immunotherapeutic approaches. Blood monocytes can be differentiated into macrophages and DCs, which is accompanied with transcriptional changes in many genes, including chemokines and cell surface markers.</p> <p>Results</p> <p>To study the chromatin modifications associated with this differentiation, we performed a genome wide analysis of histone H3 trimethylation on lysine 4 (H3K4me3) and 27 (H3K27me3) as well as acetylation of H3 lysines (AcH3) in promoter regions. We report that both H3K4me3 and AcH3 marks significantly correlate with transcriptionally active genes whereas H3K27me3 mark is associated with inactive gene promoters. During differentiation, the H3K4me3 levels decreased on monocyte-specific CD14, CCR2 and CX3CR1 but increased on DC-specific TM7SF4/DC-STAMP, TREM2 and CD209/DC-SIGN genes. Genes associated with phagocytosis and antigen presentation were marked by H3K4me3 modifications. We also report that H3K4me3 levels on clustered chemokine and surface marker genes often correlate with transcriptional activity.</p> <p>Conclusion</p> <p>Our results provide a basis for further functional correlations between gene expression and histone modifications in monocyte-derived macrophages and DCs.</p

wKinMut: An integrated tool for the analysis and interpretation of mutations in human protein kinases

BACKGROUND: Protein kinases are involved in relevant physiological functions and a broad number of mutations in this superfamily have been reported in the literature to affect protein function and stability. Unfortunately, the exploration of the consequences on the phenotypes of each individual mutation remains a considerable challenge. RESULTS: The wKinMut web-server offers direct prediction of the potential pathogenicity of the mutations from a number of methods, including our recently developed prediction method based on the combination of information from a range of diverse sources, including physicochemical properties and functional annotations from FireDB and Swissprot and kinase-specific characteristics such as the membership to specific kinase groups, the annotation with disease-associated GO terms or the occurrence of the mutation in PFAM domains, and the relevance of the residues in determining kinase subfamily specificity from S3Det. This predictor yields interesting results that compare favourably with other methods in the field when applied to protein kinases. Together with the predictions, wKinMut offers a number of integrated services for the analysis of mutations. These include: the classification of the kinase, information about associations of the kinase with other proteins extracted from iHop, the mapping of the mutations onto PDB structures, pathogenicity records from a number of databases and the classification of mutations in large-scale cancer studies. Importantly, wKinMut is connected with the SNP2L system that extracts mentions of mutations directly from the literature, and therefore increases the possibilities of finding interesting functional information associated to the studied mutations. CONCLUSIONS: wKinMut facilitates the exploration of the information available about individual mutations by integrating prediction approaches with the automatic extraction of information from the literature (text mining) and several state-of-the-art databases. wKinMut has been used during the last year for the analysis of the consequences of mutations in the context of a number of cancer genome projects, including the recent analysis of Chronic Lymphocytic Leukemia cases and is publicly available at http://wkinmut.bioinfo.cnio.es

Identifying Schistosoma japonicum Excretory/Secretory Proteins and Their Interactions with Host Immune System

Schistosoma japonicum is a major infectious agent of schistosomiasis. It has been reported that large number of proteins excreted and secreted by S. japonicum during its life cycle are important for its infection and survival in definitive hosts. These proteins can be used as ideal candidates for vaccines or drug targets. In this work, we analyzed the protein sequences of S. japonicum and found that compared with other proteins in S. japonicum, excretory/secretory (ES) proteins are generally longer, more likely to be stable and enzyme, more likely to contain immune-related binding peptides and more likely to be involved in regulation and metabolism processes. Based on the sequence difference between ES and non-ES proteins, we trained a support vector machine (SVM) with much higher accuracy than existing approaches. Using this SVM, we identified 191 new ES proteins in S. japonicum, and further predicted 7 potential interactions between these ES proteins and human immune proteins. Our results are useful to understand the pathogenesis of schistosomiasis and can serve as a new resource for vaccine or drug targets discovery for anti-schistosome

The Energy Landscape Analysis of Cancer Mutations in Protein Kinases

The growing interest in quantifying the molecular basis of protein kinase activation and allosteric regulation by cancer mutations has fueled computational studies of allosteric signaling in protein kinases. In the present study, we combined computer simulations and the energy landscape analysis of protein kinases to characterize the interplay between oncogenic mutations and locally frustrated sites as important catalysts of allostetric kinase activation. While structurally rigid kinase core constitutes a minimally frustrated hub of the catalytic domain, locally frustrated residue clusters, whose interaction networks are not energetically optimized, are prone to dynamic modulation and could enable allosteric conformational transitions. The results of this study have shown that the energy landscape effect of oncogenic mutations may be allosteric eliciting global changes in the spatial distribution of highly frustrated residues. We have found that mutation-induced allosteric signaling may involve a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. The presented study has demonstrated that activation cancer mutations may affect the thermodynamic equilibrium between kinase states by allosterically altering the distribution of locally frustrated sites and increasing the local frustration in the inactive form, while eliminating locally frustrated sites and restoring structural rigidity of the active form. The energy landsape analysis of protein kinases and the proposed role of locally frustrated sites in activation mechanisms may have useful implications for bioinformatics-based screening and detection of functional sites critical for allosteric regulation in complex biomolecular systems