PLIPS, an automatically collected database of protein lists reported by proteomics studies

Mining protein lists from proteomics studies: applications for drug discovery

IMPORTANCE OF THE FIELD

In recent years, proteomics has become a common technique applied to a wide spectrum of scientific problems, including the identification of diagnostic biomarkers, monitoring the effects of drug treatments or identification of chemical properties of a protein or a drug. Although being significantly different in scientific essence, the ultimate result of the majority of proteomics studies is a protein list. Thousands of independent proteomics studies have reported protein lists in various functional contexts.

AREAS COVERED IN THIS REVIEW

We review here the spectrum of scientific problems where proteomics technology was applied recently to deliver protein lists. The available bioinformatics methods commonly used to understand the properties of the protein lists are compared.

WHAT THE READER WILL GAIN

The types and common functional properties of the reported protein lists are discussed. The range of scientific problems where this knowledge could be potentially helpful with a focus on drug discovery issues is explored.

TAKE HOME MESSAGE

Reported protein lists represent a valuable resource which can be used for a variety of goals, ranging from biomarkers discovery to identification of novel therapeutic implications of known drugs.

Multi-edge gene set networks reveal novel insights into global relationships between biological themes

Curated gene sets from databases such as KEGG Pathway and Gene Ontology are often used to systematically organize lists of genes or proteins derived from high-throughput data. However, the information content inherent to some relationships between the interrogated gene sets, such as pathway crosstalk, is often underutilized. A gene set network, where nodes representing individual gene sets such as KEGG pathways are connected to indicate a functional dependency, is well suited to visualize and analyze global gene set relationships. Here we introduce a novel gene set network construction algorithm that integrates gene lists derived from high-throughput experiments with curated gene sets to construct co-enrichment gene set networks. Along with previously described co-membership and linkage algorithms, we apply the co-enrichment algorithm to eight gene set collections to construct integrated multi-evidence gene set networks with multiple edge types connecting gene sets. We demonstrate the utility of approach through examples of novel gene set networks such as the chromosome map co-differential expression gene set network. A total of twenty-four gene set networks are exposed via a web tool called MetaNet, where context-specific multi-edge gene set networks are constructed from enriched gene sets within user-defined gene lists. MetaNet is freely available at http://blaispathways.dfci.harvard.edu/metanet/.

BioProfiling.de: analytical web portal for high-throughput cell biology

BioProfiling.de provides a comprehensive analytical toolkit for the interpretation gene/protein lists. As input, BioProfiling.de accepts a gene/protein list. As output, in one submission, the gene list is analyzed by a collection of tools which employs advanced enrichment or network-based statistical frameworks. The gene list is profiled with respect to the most information available regarding gene function, protein interactions, pathway relationships, in silico predicted microRNA to gene associations, as well as, information collected by text mining. BioProfiling.de provides a user friendly dialog-driven web interface for several model organisms and supports most available gene identifiers. The web portal is freely available at http://www.BioProfiling.de/gene_list.

MIPS: curated databases and comprehensive secondary data resources in 2010

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38 000 000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de).

CCancer: a bird's eye view on gene lists reported in cancer-related studies

CCancer is an automatically collected database of gene lists, which were reported mostly by experimental studies in various biological and clinical contexts. At the moment, the database covers 3369 gene lists extracted from 2644 papers published in ∼80 peer-reviewed journals. As input, CCancer accepts a gene list. An enrichment analyses is implemented to generate, as output, a highly informative survey over recently published studies that report gene lists, which significantly intersect with the query gene list. A report on gene pairs from the input list which were frequently reported together by other biological studies is also provided. CCancer is freely available at http://mips.helmholtz-muenchen.de/proj/ccancer.

R spider: a network-based analysis of gene lists by combining signaling and metabolic pathways from Reactome and KEGG databases

R spider is a web-based tool for the analysis of a gene list using the systematic knowledge of core pathways and reactions in human biology accumulated in the Reactome and KEGG databases. R spider implements a network-based statistical framework, which provides a global understanding of gene relations in the supplied gene list, and fully exploits the Reactome and KEGG knowledge bases. R spider provides a user-friendly dialog-driven web interface for several model organisms and supports most available gene identifiers. R spider is freely available at http://mips.helmholtz-muenchen.de/proj/rspider.

Proteomics: challenges, techniques and possibilities to overcome biological sample complexity

Proteomics is the large-scale study of the structure and function of proteins in complex biological sample. Such an approach has the potential value to understand the complex nature of the organism. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. Advances in protein fractionation and labeling techniques have improved protein identification to include the least abundant proteins. In addition, proteomics has been complemented by the analysis of posttranslational modifications and techniques for the quantitative comparison of different proteomes. However, the major limitation of proteomic investigations remains the complexity of biological structures and physiological processes, rendering the path of exploration paved with various difficulties and pitfalls. The quantity of data that is acquired with new techniques places new challenges on data processing and analysis. This article provides a brief overview of currently available proteomic techniques and their applications, followed by detailed description of advantages and technical challenges. Some solutions to circumvent technical difficulties are proposed.

GeneSet2miRNA: finding the signature of cooperative miRNA activities in the gene lists

GeneSet2miRNA is the first web-based tool which is able to identify whether or not a gene list has a signature of miRNA-regulatory activity. As input, GeneSet2miRNA accepts a list of genes. As output, a list of miRNA-regulatory models is provided. A miRNA-regulatory model is a group of miRNAs (single, pair, triplet or quadruplet) that is predicted to regulate a significant subset of genes from the submitted list. GeneSet2miRNA provides a user friendly dialog-driven web page submission available for several model organisms. GeneSet2miRNA is freely available at http://mips.helmholtz-muenchen.de/proj/gene2mir/.