Proteomic analysis of pathways involved in estrogen-induced growth and apoptosis of breast cancer cells.

Background: Estrogen is a known growth promoter for estrogen receptor (ER)-positive breast cancer cells. Paradoxically, in breast cancer cells that have been chronically deprived of estrogen stimulation, re-introduction of the hormone can induce apoptosis.

Methodology/principal Findings: Here, we sought to identify signaling networks that are triggered by estradiol (E2) in isogenic MCF-7 breast cancer cells that undergo apoptosis (MCF-7:5C) versus cells that proliferate upon exposure to E2 (MCF-7).

dbOGAP - an integrated bioinformatics resource for protein O-GlcNAcylation.

Background: Protein O-GlcNAcylation (or O-GlcNAc-ylation) is an O-linked glycosylation involving the transfer of β-N-acetylglucosamine to the hydroxyl group of serine or threonine residues of proteins. Growing evidences suggest that protein O-GlcNAcylation is common and is analogous to phosphorylation in modulating broad ranges of biological processes. However, compared to phosphorylation, the amount of protein O-GlcNAcylation data is relatively limited and its annotation in databases is scarce.

Omics-based molecular target and biomarker identification.

Genomic, proteomic, and other omic-based approaches are now broadly used in biomedical research to facilitate the understanding of disease mechanisms and identification of molecular targets and biomarkers for therapeutic and diagnostic development. While the Omics technologies and bioinformatics tools for analyzing Omics data are rapidly advancing, the functional analysis and interpretation of the data remain challenging due to the inherent nature of the generally long workflows of Omics experiments. We adopt a strategy that emphasizes the use of curated knowledge resources coupled with expert-guided examination and interpretation of Omics data for the selection of potential molecular targets.

Document classification for mining host pathogen protein-protein interactions.

Objective: Scientific findings regarding human pathogens and their host responses are buried in the growing volume of biomedical literature and there is an urgent need to mine information pertaining to pathogenesis-related proteins especially host pathogen protein-protein interactions (HP-PPIs) from literature.

Methods: In this paper, we report our exploration of developing an automated system to identify MEDLINE abstracts referring to HP-PPIs. An annotated corpus consisting of 1360 MEDLINE abstracts was generated.

Support vector machine-based mucin-type o-linked glycosylation site prediction using enhanced sequence feature encoding.

Glycosylation is a common and complex protein post-translational modification (PTM). In particular, mucin-type O-linked glycosylation is abundant and plays important biological functions. The number of determined glycosylation sites is still small and there remains the need of accurate computational prediction for annotation and functional understanding of proteins.

Integrated Bioinformatics for Radiation-Induced Pathway Analysis from Proteomics and Microarray Data.

Functional analysis and interpretation of large-scale proteomics and gene expression data require effective use of bioinformatics tools and public knowledge resources coupled with expert-guided examination. An integrated bioinformatics approach was used to analyze cellular pathways in response to ionizing radiation. ATM, or ataxia-telangiectasia mutated , a serine-threonine protein kinase, plays critical roles in radiation responses, including cell cycle arrest and DNA repair.

BioTagger-GM: a gene/protein name recognition system.

Objectives: Biomedical named entity recognition (BNER) is a critical component in automated systems that mine biomedical knowledge in free text. Among different types of entities in the domain, gene/protein would be the most studied one for BNER. Our goal is to develop a gene/protein name recognition system BioTagger-GM that exploits rich information in terminology sources using powerful machine learning frameworks and system combination.

Protein name tagging guidelines: lessons learned.

Interest in information extraction from the biomedical literature is motivated by the need to speed up the creation of structured databases representing the latest scientific knowledge about specific objects, such as proteins and genes. This paper addresses the issue of a lack of standard definition of the problem of protein name tagging. We describe the lessons learned in developing a set of guidelines and present the first set of inter-coder results, viewed as an upper bound on system performance.

A comparison study on algorithms of detecting long forms for short forms in biomedical text.

Motivation: With more and more research dedicated to literature mining in the biomedical domain, more and more systems are available for people to choose from when building literature mining applications. In this study, we focus on one specific kind of literature mining task, i.e.

Framework for a protein ontology.

Biomedical ontologies are emerging as critical tools in genomic and proteomic research, where complex data in disparate resources need to be integrated. A number of ontologies describe properties that can be attributed to proteins. For example, protein functions are described by the Gene Ontology (GO) and human diseases by SNOMED CT or ICD10.

Integration of bioinformatics resources for functional analysis of gene expression and proteomic data.

In the post-genome era, researchers are systematically tackling gene functions and complex regulatory processes by studying organisms on a global scale; however, a major challenge lies in the voluminous, complex, and dynamic data being maintained in heterogeneous sources, especially from proteomics experiments. Advanced computational methods are needed for integration, mining, comparative analysis, and functional interpretation of high-throughput proteomic data. In the first part of this review, we discuss aspects of data integration important for capturing all data relevant to functional analysis.

Computational analysis and identification of amino acid sites in dengue E proteins relevant to development of diagnostics and vaccines.

We have identified 72 completely conserved amino acid residues in the E protein of major groups of the Flavivirus genus by computational analyses. In the dengue species we have identified 12 highly conserved sequence regions, 186 negatively selected sites, and many dengue serotype-specific negatively selected sites. The flavivirus-conserved sites included residues involved in forming six disulfide bonds crucial for the structural integrity of the protein, the fusion motif involved in viral infectivity, and the interface residues of the oligomers.

Comparative Bioinformatics Analyses and Profiling of Lysosome-Related Organelle Proteomes.

Complete and accurate profiling of cellular organelle proteomes, while challenging, is important for the understanding of detailed cellular processes at the organelle level. Mass spectrometry technologies coupled with bioinformatics analysis provide an effective approach for protein identification and functional interpretation of organelle proteomes. In this study, we have compiled human organelle reference datasets from large-scale proteomic studies and protein databases for 7 lysosome-related organelles (LROs), as well as the endoplasmic reticulum and mitochondria, for comparative organelle proteome analysis.

Proteomic and bioinformatic characterization of the biogenesis and function of melanosomes.

Melanin, which is responsible for virtually all visible skin, hair, and eye pigmentation in humans, is synthesized, deposited, and distributed in subcellular organelles termed melanosomes. A comprehensive determination of the protein composition of this organelle has been obstructed by the melanin present. Here, we report a novel method of removing melanin that includes in-solution digestion and immobilized metal affinity chromatography (IMAC).

Dependence network modeling for biomarker identification.

Motivation: Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data.

Results: Three MS and two gene microarray datasets are studied.

Substring selection for biomedical document classification.

Motivation: Attribute selection is a critical step in development of document classification systems. As a standard practice, words are stemmed and the most informative ones are used as attributes in classification. Owing to high complexity of biomedical terminology, general-purpose stemming algorithms are often conservative and could also remove informative stems.

Quantitative assessment of dictionary-based protein named entity tagging.

Objective: Natural language processing (NLP) approaches have been explored to manage and mine information recorded in biological literature. A critical step for biological literature mining is biological named entity tagging (BNET) that identifies names mentioned in text and normalizes them with entries in biological databases. The aim of this study was to provide quantitative assessment of the complexity of BNET on protein entities through BioThesaurus, a thesaurus of gene/protein names for UniProt knowledgebase (UniProtKB) entries that was acquired using online resources.

[caCORE: core architecture of bioinformation on cancer research in America].

A critical factor in the advancement of biomedical research is the ease with which data can be integrated, redistributed and analyzed both within and across domains. This paper summarizes the Biomedical Information Core Infrastructure built by National Cancer Institute Center for Bioinformatics in America (NCICB). The main product from the Core Infrastructure is caCORE--cancer Common Ontologic Reference Environment, which is the infrastructure backbone supporting data management and application development at NCICB.

BioThesaurus: a web-based thesaurus of protein and gene names.

Unlabelled: BioThesaurus is a web-based system designed to map a comprehensive collection of protein and gene names to protein entries in the UniProt Knowledgebase. Currently covering more than two million proteins, BioThesaurus consists of over 2.8 million names extracted from multiple molecular biological databases according to the database cross-references in iProClass.

DynGO: a tool for visualizing and mining of Gene Ontology and its associations.

Background: A large volume of data and information about genes and gene products has been stored in various molecular biology databases. A major challenge for knowledge discovery using these databases is to identify related genes and gene products in disparate databases. The development of Gene Ontology (GO) as a common vocabulary for annotation allows integrated queries across multiple databases and identification of semantically related genes and gene products (i.

[Progress in National Institutes of Health roadmap initiatives].

National Institutes of Health (NIH) released the biomedical research project NIH Roadmap Initiatives, including 3 themes, new pathways to discovery, research teams of the future, and re-engineering the clinical research enterprise. The purpose of the project is to catalyze to transform our new scientific knowledge into tangible benefits for people. Now, mostly of the project have begin to carry into practice.

iProLINK: an integrated protein resource for literature mining.

The exponential growth of large-scale molecular sequence data and of the PubMed scientific literature has prompted active research in biological literature mining and information extraction to facilitate genome/proteome annotation and improve the quality of biological databases. Motivated by the promise of text mining methodologies, but at the same time, the lack of adequate curated data for training and benchmarking, the Protein Information Resource (PIR) has developed a resource for protein literature mining--iProLINK (integrated Protein Literature INformation and Knowledge). As PIR focuses its effort on the curation of the UniProt protein sequence database, the goal of iProLINK is to provide curated data sources that can be utilized for text mining research in the areas of bibliography mapping, annotation extraction, protein named entity recognition, and protein ontology development.

PIRSF: family classification system at the Protein Information Resource.

The Protein Information Resource (PIR) is an integrated public resource of protein informatics. To facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors, PIR has extended its superfamily concept and developed the SuperFamily (PIRSF) classification system. Based on the evolutionary relationships of whole proteins, this classification system allows annotation of both specific biological and generic biochemical functions.

The Protein Information Resource.

The Protein Information Resource (PIR) is an integrated public resource of protein informatics that supports genomic and proteomic research and scientific discovery. PIR maintains the Protein Sequence Database (PSD), an annotated protein database containing over 283 000 sequences covering the entire taxonomic range. Family classification is used for sensitive identification, consistent annotation, and detection of annotation errors.

Complex 5' genomic structure of the human prolactin receptor: multiple alternative exons 1 and promoter utilization.

Transcription of the prolactin receptor (PRLR) is under the control of multiple promoters. Following the recent demonstration of the human non-coding exon 1, hE1(N) (hE1(N1)) and the generic exon 1 hE1(3), we have identified their promoters and characterized four other novel human exons 1 (hE1(N2-5)) that are alternatively spliced to a common non-coding exon 2 in human tissues and breast cancer cells. Genomic regions containing these exons, and 5'-flanking and intronic sequences, were determined and their order was established in chromosome 5p14-13.