Input Gene Set Research Articles

SNPRanker: a tool for identification and scoring of SNPs associated to target genes

Summary The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL http://www.itb.cnr.it/snpranker. An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.

SNPRanker: a tool for identification and scoring of SNPs associated to target genes.

The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL http://www.itb.cnr.it/snpranker . An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.

Identification of genes involved in the same pathways using a Hidden Markov Model-based approach

The sequencing of whole genomes from various species has provided us with a wealth of genetic information. To make use of the vast amounts of data available today it is necessary to devise computer-based analysis techniques. We propose a Hidden Markov Model (HMM) based algorithm to detect groups of genes functionally similar to a set of input genes from microarray expression data. A subset of experiments from a microarray is selected based on a set of related input genes. HMMs are trained from the input genes and a group of random gene input sets to provide significance estimates. Every gene in the microarray is scored using all HMMs and significant matches with the input genes are retained. We ran this algorithm on the life cycle of Drosophila microarray data set with KEGG pathways for cell cycle and translation factors as input data sets. Results show high functional similarity in resulting gene sets, increasing our biological insight into gene pathways and KEGG annotations. The algorithm performed very well compared to the Signature Algorithm and a purely correlation-based approach. Java source codes and data sets are available at http://www.ittc.ku.edu/~xwchen/software.htm

CoPub: a literature-based keyword enrichment tool for microarray data analysis

Medline is a rich information source, from which links between genes and keywords describing biological processes, pathways, drugs, pathologies and diseases can be extracted. We developed a publicly available tool called CoPub that uses the information in the Medline database for the biological interpretation of microarray data. CoPub allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs. CoPub is freely accessible at http://services.nbic.nl/cgi-bin/copub/CoPub.pl.

DiRE: identifying distant regulatory elements of co-expressed genes.

Regulation of gene expression in eukaryotic genomes is established through a complex cooperative activity of proximal promoters and distant regulatory elements (REs) such as enhancers, repressors and silencers. We have developed a web server named DiRE, based on the Enhancer Identification (EI) method, for predicting distant regulatory elements in higher eukaryotic genomes, namely for determining their chromosomal location and functional characteristics. The server uses gene co-expression data, comparative genomics and profiles of transcription factor binding sites (TFBSs) to determine TFBS-association signatures that can be used for discriminating specific regulatory functions. DiRE's unique feature is its ability to detect REs outside of proximal promoter regions, as it takes advantage of the full gene locus to conduct the search. DiRE can predict common REs for any set of input genes for which the user has prior knowledge of co-expression, co-function or other biologically meaningful grouping. The server predicts function-specific REs consisting of clusters of specifically-associated TFBSs and it also scores the association of individual transcription factors (TFs) with the biological function shared by the group of input genes. Its integration with the Array2BIO server allows users to start their analysis with raw microarray expression data. The DiRE web server is freely available at http://dire.dcode.org.

DETECTING EVOLUTIONARY TRANSFER OF GENES USING PhIGs(1).

Organisms have acquired plastids by convoluted paths that have provided multiple opportunities for gene transfer into a host nucleus from intracellular organisms, including the cyanobacterial ancestor of plastids, the proteobacterial ancestor of mitochondria, and both green and red algae whose engulfment has led to secondary acquisition of plastids. These gene movements are most accurately demonstrated by building phylogenetic trees that identify the evolutionary origin of each gene, and one effective tool for this is "PhIGs" (Phylogenetically Inferred Groups; http://PhIGs.org), a set of databases and computer tools with a Web interface for whole-genome evolutionary analysis. PhIGs takes as input gene sets of completely sequenced genomes, builds clusters of genes using a novel, graph-based approach, and reconstructs the evolutionary relationships among all gene families. The user can view and download the sequence alignments, compare intron-exon structures, and follow links to functional genomic databases. Currently, PhIGs contains 652,756 genes from 45 genomes grouped into 61,059 gene families. Graphical displays show the relative positions of these genes among genomes. PhIGs has been used to detect the evolutionary transfer of hundreds of genes from cyanobacteria and red algae into oömycete nuclear genomes, revealing that even though they have no plastids, their ancestors did, having secondarily acquired them from an intracellular red alga. A great number of genomes are soon to become available that are relevant to our broader understanding of the movement of genes among intracellular compartments after engulfing other organisms, and PhIGs will be an effective tool to interpret these gene movements.

NADPH Oxidase Activity Selectively Modulates Vascular Endothelial Growth Factor Signaling Pathways

Vascular endothelial growth factor (VEGF) and reactive oxygen species (ROS) play critical roles in vascular physiology and pathophysiology. We have demonstrated previously that NADPH oxidase-derived ROS are required for VEGF-mediated migration and proliferation of endothelial cells. The goal of this study was to determine the extent to which VEGF signaling is coupled to NADPH oxidase activity. Human umbilical vein endothelial cells and/or human coronary artery endothelial cells were transfected with short interfering RNA against the p47(phox) subunit of NADPH oxidase, treated in the absence or presence of VEGF, and assayed for signaling, gene expression, and function. We show that NADPH oxidase activity is required for VEGF activation of phosphoinositide 3-kinase-Akt-forkhead, and p38 MAPK, but not ERK1/2 or JNK. The permissive role of NADPH oxidase on phosphoinositide 3-kinase-Akt-forkhead signaling is mediated at post-VEGF receptor levels and involves the nonreceptor tyrosine kinase Src. DNA microarrays revealed the existence of two distinct classes of VEGF-responsive genes, one that is ROS-dependent and another that is independent of ROS levels. VEGF-induced, thrombomodulin-dependent activation of protein C was dependent on NADPH oxidase activity, whereas VEGF-induced decay-accelerating factor-mediated protection of endothelial cells against complement-mediated lysis was not. Taken together, these findings suggest that NADPH oxidase-derived ROS selectively modulate some but not all the effects of VEGF on endothelial cell phenotypes.

CREME: Cis-Regulatory Module Explorer for the human genome

The binding of transcription factors to specific regulatory sequence elements is a primary mechanism for controlling gene transcription. Eukaryotic genes are often regulated by several transcription factors whose binding sites are tightly clustered and form cis-regulatory modules. In this paper, we present a web server, CREME, for identifying and visualizing cis-regulatory modules in the promoter regions of a given set of potentially co-regulated genes. CREME relies on a database of putative transcription factor binding sites that have been annotated across the human genome using a library of position weight matrices and evolutionary conservation with the mouse and rat genomes. A search algorithm is applied to this data set to identify combinations of transcription factors whose binding sites tend to co-occur in close proximity in the promoter regions of the input gene set. The identified cis-regulatory modules are statistically scored and significant combinations are reported and graphically visualized. Our web server is available at http://creme.dcode.org.

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

BackgroundMicroarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets.ResultsWe have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at .ConclusionGOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets.