Public Databases and Software for the Pathway Analysis of Cancer Genomes

The study of pathway disruption is key to understanding cancer biology. Advances in high throughput technologies have led to the rapid accumulation of genomic data. The explosion in available data has generated opportunities for investigation of concerted changes that disrupt biological functions, this in turns created a need for computational tools for pathway analysis. In this review, we discuss approaches to the analysis of genomic data and describe the publicly available resources for studying biological pathways

TRAPID : an efficient online tool for the functional and comparative analysis of de novo RNA-Seq transcriptomes

Transcriptome analysis through next-generation sequencing technologies allows the generation of detailed gene catalogs for non-model species, at the cost of new challenges with regards to computational requirements and bioinformatics expertise. Here, we present TRAPID, an online tool for the fast and efficient processing of assembled RNA-Seq transcriptome data, developed to mitigate these challenges. TRAPID offers high-throughput open reading frame detection, frameshift correction and includes a functional, comparative and phylogenetic toolbox, making use of 175 reference proteomes. Benchmarking and comparison against state-of-the-art transcript analysis tools reveals the efficiency and unique features of the TRAPID system

Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations

The EC2KEGG output for the RV1423 analysis sorted in ascending order by the FDR value. (XLSX 58 kb

WholePathwayScope: a comprehensive pathway-based analysis tool for high-throughput data

BACKGROUND: Analysis of High Throughput (HTP) Data such as microarray and proteomics data has provided a powerful methodology to study patterns of gene regulation at genome scale. A major unresolved problem in the post-genomic era is to assemble the large amounts of data generated into a meaningful biological context. We have developed a comprehensive software tool, WholePathwayScope (WPS), for deriving biological insights from analysis of HTP data. RESULT: WPS extracts gene lists with shared biological themes through color cue templates. WPS statistically evaluates global functional category enrichment of gene lists and pathway-level pattern enrichment of data. WPS incorporates well-known biological pathways from KEGG (Kyoto Encyclopedia of Genes and Genomes) and Biocarta, GO (Gene Ontology) terms as well as user-defined pathways or relevant gene clusters or groups, and explores gene-term relationships within the derived gene-term association networks (GTANs). WPS simultaneously compares multiple datasets within biological contexts either as pathways or as association networks. WPS also integrates Genetic Association Database and Partial MedGene Database for disease-association information. We have used this program to analyze and compare microarray and proteomics datasets derived from a variety of biological systems. Application examples demonstrated the capacity of WPS to significantly facilitate the analysis of HTP data for integrative discovery. CONCLUSION: This tool represents a pathway-based platform for discovery integration to maximize analysis power. The tool is freely available at

MADNet: microarray database network web server

MADNet is a user-friendly data mining and visualization tool for rapid analysis of diverse high-throughput biological data such as microarray, phage display or even metagenome experiments. It presents biological information in the context of metabolic and signalling pathways, transcription factors and drug targets through minimal user input, consisting only of the file with the experimental data. These data are integrated with information stored in various biological databases such as NCBI nucleotide and protein databases, metabolic and signalling pathway databases (KEGG), transcription regulation (TRANSFAC©) and drug target database (DrugBank). MADNet is freely available for academic use at http://www.bioinfo.hr/madnet

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

This article has been made available through the Brunel Open Access Publishing Fund. Copyright @ 2013 Pey et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. Results: We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. Conclusions: A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.Basque Governmen

Development of probabilistic models for quantitative pathway analysis of plant pest introduction for the EU territory

This report demonstrates a probabilistic quantitative pathway analysis model that can be used in risk assessment for plant pest introduction into EU territory on a range of edible commodities (apples, oranges, stone fruits and wheat). Two types of model were developed: a general commodity model that simulates distribution of an imported infested/infected commodity to and within the EU from source countries by month; and a consignment model that simulates the movement and distribution of individual consignments from source countries to destinations in the EU. The general pathway model has two modules. Module 1 is a trade pathway model, with a Eurostat database of five years of monthly trade volumes for each specific commodity into the EU28 from all source countries and territories. Infestation levels based on interception records, commercial quality standards or other information determine volume of infested commodity entering and transhipped within the EU. Module 2 allocates commodity volumes to processing, retail use and waste streams and overlays the distribution onto EU NUTS2 regions based on population densities and processing unit locations. Transfer potential to domestic host crops is a function of distribution of imported infested product and area of domestic production in NUTS2 regions, pest dispersal potential, and phenology of susceptibility in domestic crops. The consignment model covers the several routes on supply chains for processing and retail use. The output of the general pathway model is a distribution of estimated volumes of infested produce by NUTS2 region across the EU28, by month or annually; this is then related to the accessible susceptible domestic crop. Risk is expressed as a potential volume of infested fruit in potential contact with an area of susceptible domestic host crop. The output of the consignment model is a volume of infested produce retained at each stage along the specific consignment trade chain