Sparsely correlated hidden Markov models with application to genome-wide location studies

10.1093/bioinformatics/btt012Bioinformatics295533-541BOIN

Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT

10.1002/0471250953.bi0815s39Current Protocols in BioinformaticsSUPPL.39

A chloroform-assisted protein isolation method followed by capillary NanoLC-MS identify estrogen-regulated proteins from MCF7 cells

10.4172/jpb.1000142Journal of Proteomics and Bioinformatics37212-22

A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics

The protein inference problem represents a major challenge in shotgun proteomics. In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results

Managing information quality in e-Science using semantic Web technology

We outline a framework for managing information quality (IQ) in e-Science, using ontologies, semantic annotation of resources, and data bindings. Scientists define the quality characteristics that are of importance in their particular domain by extending an OWL DL IQ ontology, which classifies and organises these domain-specific quality characteristics within an overall quality management framework. RDF is used to annotate data resources, with reference to IQ indicators defined in the ontology. Data bindings — again defined in RDF — are used to represent mappings between data elements (e.g. defined in XML Schemas) and the IQ ontology. As a practical illustration of our approach, we present a case study from the domain of proteomics

A.: Managing Information Quality in e-Science Using Semantic Web Technology

Abstract. We outline a framework for managing information quality (IQ) in e-Science, using ontologies, semantic annotation of resources, and data bindings. Scientists define the quality characteristics that are of importance in their particular domain by extending an OWL DL IQ ontology, which classifies and organises these domain-specific quality characteristics within an overall quality management framework. RDF is used to annotate data resources, with reference to IQ indicators defined in the ontology. Data bindings — again defined in RDF — are used to represent mappings between data elements (e.g. defined in XML Schemas) and the IQ ontology. As a practical illustration of our approach, we present a case study from the domain of proteomics.

SUMOylation pathway in Trypanosoma cruzi: Functional characterization and proteomic analysis of target proteins

10.1074/mcp.M110.007369Molecular and Cellular Proteomics1012-MCPO