Protein ontology development using OWL

To efficiently represent the protein annotation framework and to integrate all the existing data representations into a standardized protein data specification for the bioinformatics community, the protein ontology need to be represented in a format that not enforce semantic constraints on protein data, but can also facilitate reasoning tasks on protein data using semantic query algebra. This motivates the representation of Protein Ontology (PO) Model in Web Ontology Language (OWL). In this paper we briefly discuss the usage of OWL in achieving the objectives of Protein Ontology Project. We provide a brief overview of Protein Ontology (PO) to start with. In the later sections discuss why OWL was an ideal choice for PO Development

Protein ontology: Vocabulary for protein data

These Huge amounts of Protein Structure Data make it difficult to create explanatory and predictive models that are consistent with huge volume of data. Difficulty increase when large variety of heterogeneous approaches gathers data from multiple perspectives. In order to facilitate computational processing data, it is especially critical to develop standardized structured data representation model formats for proteomics data. In this paper we describe a Protein Ontology Model for integrating protein databases and deduce a structured vocabulary for understanding process of protein synthesis completely. Proposed Protein Ontology Model provides biologists and scientists with a description of sequence, structure and functions of protein and also provides interpretation of various factors on final protein structure conformation. The Structured Vocabulary for Protein Data, describing Protein Ontology is composed of various Type Definitions for Protein Entry Details, Sequence and Structural Information of Proteins, Structural Domain Family of Protein, Cellular Function of Protein, Chemical Bonds present in the Protein, and External Constraints deciding final protein conformation. The Proposed Ontology Model will provide easier ways to predict and understand proteins

OWL, proteins and data integration

In this paper, we propose an approach to integrate protein information from various data sources by defining a Protein Ontology. Protein Ontology provides the technical and scientific infrastructure and knowledge to allow description and analysis of relationships between various proteins. Protein Ontology uses relevant protein data sources of information like PDB, SCOP, and OMIM. Protein Ontology describes: Protein Sequence and Structure Information, Protein Folding Process, Cellular Functions of Proteins, Molecular Bindings internal and external to Proteins, and Constraints affecting the Final Protein Conformation. Details about Protein Ontology are available online at http://www.proteinontology.info/

Ontology-based Knowledge Representation for Protein Data

The advances in information and communication technologies coupled with increased knowledge about genes and proteins have opened new perspectives for study of protein complexes. There is a growing need to integrate the knowledge about various protein complexes for effective disease prevention mechanisms, individualized medicines and treatments and other accepts of healthcare. In this paper we propose a protein ontology that handles the following computational challenges in the area proteomics and systems biology in general: (1) it provides more accurate interpretations and associations as conclusions are based on data and semantics. (2) It makes it possible to study relationships among proteins, protein folding, behaviour of protein under various environments, and most importantly cellular function of protein. This protein ontology is a unified terminology description integrating various protein database schemas and provides a easier way to predict and understand proteins

Protein Ontology Project: 2006 updates

Protein Ontology (PO) is a means of formalizing protein data and knowledge; protein ontology includes concepts or terms relevant to the domain, definitions of concepts, and defined relationships between the concepts. PO integrates protein data formats and provides a structured and unified vocabulary to represent protein synthesis concepts. PO provides integration of heterogeneous protein and biological data sources. This paper discusses the updates that happened to the Protein Ontology Project since it was last presented at the Data Mining 2005 Conference

A PACS alternative for transmitting DICOM images in a high latency environment

Picture Archiving and Communication System(PACS) is responsible for storing Digital Imaging and Communication in Medicine (DICOM) images fromradiology modalities into its database, images takes a lot of time to transfer to remote location through WAN due to large file size and slow transfer protocol. A PACS alternative system has been developed which performs basic functions of a generic PACS. Images directly from modalities are large in size by default transfer syntax of these images is Endian Explicit syntax. Changing this transfer syntax to lossless JPEG 2000 decreases the file size and because of lossless compression quality of image is still same as original image. These compressed images are then copied into Network Attached Storage working as PACS alternative. A series of test conducted in lab with multiple transfer protocol on Network Attached Storage (NAS) to find out which transfer protocol is faster under moderate speed and high latency network

Current status of biomedical ontologies: developments in 2007

The goal of this paper is to survey existing biomedical ontologies and their developments in 2007. This paper discusses features of biomedical ontologies that allow true information integration in biomedical domain. The paper is a compilation of several biomedical ontologies like Gene Ontology, Protein Ontology, etc. that have developed serving primarily the purposes of information extraction from on-line biomedical literature and databases

Advances in protein ontology project

Advances in proteomics and protein expression techniques have lead to the elucidation of large amounts of protein data. Various data mining algorithms and mathematical models provide methods for analyzing this data; however, there are two issues that need to be addressed: (1) the need for standards for defining protein data description and exchange formats so they can be exchanged across the World Wide Web, and also read into data mining software in a consistent format and (2) eliminating errors which arise with the data integration methodologies for complex queries. Protein Ontology is designed to meet these needs by providing a structured protein data specification for Protein Data Representation. Protein Ontology is a standard for representing protein data in a way that helps in defining data integration and data mining models for Protein Structure and Function. In this paper we summarize the structure of Protein Ontology we developed earlier, its current applications to various protein families, and its future development

Ontological foundation for protein data models

In this paper, we proposed a Protein Ontology to integrate protein data and information from various Protein Data Sources. Protein Ontology provides the technical and scientific infrastructure and knowledge to allow description and analysis of relationships between various proteins. Protein Ontology uses relevant protein data sources of information like PDB, SCOP, and OMIM. Protein Ontology describes: Protein Sequence and Structure Information, Protein Folding Process, Cellular Functions of Proteins, Molecular Bindings internal and external to Proteins, and Constraints affecting the Final Protein Conformation. We also created a database of 10 Major Prion Proteins available in various Protein data sources, based on the vocabulary provided by Protein Ontology. Details about Protein Ontology are available online athttp://www.proteinontology.info/

Ontology algebra for composition of protein data sources

These Huge amounts of Protein Structure Data make it difficult to create explanatory and predictive models that are consistent with huge volume of data. Difficulty increase when large variety of heterogeneous approaches gathers data from multiple perspectives. In order to facilitate computational processing data, it is especially critical to develop standardized structured data representation model formats for proteomics data. In this paper we describe a Protein Ontology Model for integrating protein databases and deduce a structured vocabulary for understanding process of protein synthesis completely. Proposed Protein Ontology Model provides biologists and scientists with a description of sequence, structure and functions of protein and also provides interpretation of various factors on final protein structure conformation. The Structured Vocabulary for Protein Data, describing Protein Ontology is composed of various Type Definitions for Protein Entry Details, Sequence and Structural Information of Proteins, Structural Domain Family of Protein, Cellular Function of Protein, Chemical Bonds present in the Protein, and External Constraints deciding final protein conformation. The Proposed Ontology Model will provide easier ways to predict and understand proteins