• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.407-411
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• 2005

KUGI (Korean UniGene Information) database contains the annotation information of the cDNA sequences obtained from the disease samples prevalent in Korean. A total of about 157,000 5'-EST high throughput sequences collected from cDNA libraries of stomach, liver, and some cancer tissues or established cell lines from Korean patients were clustered to about 35,000 contigs. From each cluster a representative clone having the longest high quality sequence or the start codon was selected. We stored the sequences of the representative clones and the clustered contigs in the KUGI database together with their information analyzed by running Blast against RefSeq, human mRNA, and UniGene databases from NCBI. We provide a web-based search engine fur the KUGI database using two types of user interfaces: attribute-based search and similarity search of the sequences. For attribute-based search, we use DBMS technology while we use BLAST that supports various similarity search options. The search system allows not only multiple queries, but also various query types. The results are as follows: 1) information of clones and libraries, 2) accession keys, location on genome, gene ontology, and pathways to public databases, 3) links to external programs, and 4) sequence information of contig and 5'-end of clones. We believe that the KUGI database and search system may provide very useful information that can be used in the study for elucidating the causes of the disease that are prevalent in Korean.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.53-55
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• 2000

Xenie is the JAVA application software that integrates and represents 'gene to function'information of human gene. Xenie extracts data from several heterogeneous molecular biology databases and provides integrated information in human readable and machine usable way. We defined 7 semantic frame classes (Gene, Transcript, Polypeptide, Protein_complex, Isotype, Functional_object, and Cell) as a common schema for storing and integrating gene to function information and relationship. Each of 7 semantic frame classes has data fields that are supposed to store biological data like gene symbol, disease information, cofactors, and inhibitors, etc. By using these semantic classes, Xenie can show how many transcripts and polypeptide has been known and what the function of gene products is in General. In detail, Xenie provides functional information of given human gene in the fields of semantic objects that are storing integrated data from several databases (Brenda, GDB, Genecards, HGMD, HUGO, LocusLink, OMIM, PIR, and SWISS-PROT). Although Xenie provide fully readable form of XML document for human researchers, the main goal of Xenie system is providing integrated data for other bioinformatic application softwares. Technically, Xenie provides two kinds of output format. One is JAVA persistent object, the other is XML document, both of them have been known as the most favorite solution for data exchange. Additionally, UML designs of Xenie and DTD for 7 semantic frame classes are available for easy data binding to other bioinformatic application systems. Hopefully, Xenie's output can provide more detailed and integrated information in several bioinformatic systems like Gene chip, 2D gel, biopathway related systems. Furthermore, through data integration, Xenie can also make a way for other bioiformatic systems to ask 'function based query'that was originally impossible to be answered because of separatly stored data in heterogeneous databases.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.679-688
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• 2003

Motif databases are used in the function and structure prediction of proteins which appear on new and rapid release of raw data from genome sequencing projects. Recently, the frequency of use about these databases increases continuously. However, existing motif databases were developed and extended independently and were integrated mainly by using a web-based cross-reference, thus these databases have a heterogeneous search result problem, a complex query process problem and a duplicate database entry handling problem. Therefore, in this paper, we suppose physical motif resource integration and describe the integrated search method about a family-based protein prediction for solving above these problems. Finally, we estimate our implementation of the motif integration database and prediction system for predicting protein motifs.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.181-190
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• 2004

The docking and in silico ligand screening procedures can select small sets of lead -like candidates from large libraries of either commercially or synthetically available compounds; however, the vast number of such molecules make the potential size of this task enormous. To accelerate the discovery of drugs to inhibit several targets, we have exploited massively distributed computing to screen compound libraries virtually. The Korea@HOME project was launched in Feb. 2002, and one year later, more than 1200 PC's have been recruited. This has created a 31 -gigaflop machine that has already provided more than 1400 hours of CPU time. It has all owed databases of millions of compounds to be screened against protein targets in a matter of days. Now, the virtual screening software suitable for distributed environments is developed by BMD. It has been evaluated in terms of the accuracy of the scoring function and the search algorithm for the correct binding mode.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.41-49
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• 2008

In recent years, a life phenomenon using a workflow management tool in bioinformatics has been actively researched. Workflow management tool is the base which enables researchers to collaborate through the re-use and sharing of service, and a variety of workflow management tools including MyGrid project's Taverna, Kepler and BioWMS have been developed and used as the open source. This workflow management tool can model and automate different services in spatially-distant area in one working space based on the web service technology. Many tools and databases used in the bioinformatics are provided in the web services form and are used in the workflow management tool. In such the situation, the web services development and stable service offering for a sequence similarity search which is basically used in the bioinformatics can be essential in the bioinformatics field. In this paper, the similarity retrieval speed of biology sequence data was improved based on a Linux cluster, and the sequence similarity retrieval could be done for a short time by linking with the workflow management tool through developing it in the web services.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1333-1336
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• 2016

Oral cancer is one of the most prevalent cancers in India but the underlying mechanisms are minimally unraveled. Cancer research has immensely benefited from genome scale high throughput studies which have contributed to expanding the volume of data. Such datasets also exist for oral cancer genes but there has been no consolidated approach to integrate the data to reveal meaningful biological information. OrCanome is one of the largest and comprehensive, user-friendly databases of oral cancer. It features a compilation of over 900 genes dysregulated in oral cancer and provides detailed annotations of the genes, transcripts and proteins along with additional information encompassing expression, inhibitors, epitopes and pathways. The resource has been envisioned as a one-stop solution for genomic, transcriptomic and proteomic annotation of these genes and the integrated approach will facilitate the identification of potential biomarkers and therapeutic targets.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.243-251
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• 2004

Most programs of bioinformatics provide biochemists and biologists retrieve and analysis services of gene and protein database. As these services retrieve database for each arrival of user's request, it takes a long time and increases server's load and response time. In this paper. by utilizing database retrieval patterns of sequence alignment programs in bioinformatics, grouping method is proposed to share database retrieval between many requests. Carpool method is also proposed to reduce response time as well as to increase system expandability by combining new arriving requests with the previous on going requests. The performance of our two proposed schemes is verified by mathematic analysis and simulation.

• Genomics & Informatics
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• v.19 no.3
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• pp.27.1-27.4
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• 2021

Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks. To create the dataset, we selected more than 15,000 abstracts associated with articles previously curated for rice genes. We developed four dictionaries of gene and protein names associated with database identifiers. We used these dictionaries to annotate the dataset. We also annotated the dataset using pretrained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.89-94
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• 2006

Mass spectrometry (MS) is widely applied for high throughput proteomics analysis. When large-scale proteome analysis experiments are performed, it generates massive amount of data. To search these proteomics data against protein databases, fully automated database search algorithms, such as Mascot and SEQUEST are routinely employed. At present, it is critical to reduce false positives and false negatives during such analysis. In this review we have focused on aspects of automated protein identification using tandem mass spectrometry (MS/MS) spectra and validation of the protein identifications of two most common automated protein identification algorithms Mascot and SEQUEST.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.379-382
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• 2005

We demonstrate workflows in biological data retrieval and analysis using the DDBJ Web Service; specifically introduce a workflow for the analysis of proteins or proteomics data sets. The workflow mechanically extracts the gene whose protein structure and function are known from all the genes of a human genome in Ensembl (http://www.ensembl.org/) based on cross-references among Ensembl, Swiss-Prot (http://www.ebi.ac.uk/swissprot) and PDB (Protein Data Bank; http://www.wwpdb.org/). The workflow discovered ‘hidden’ linkages among databases. We will be able to integrate distributed and heterogeneous data systems into workflows, if they are provided based on standards for Web services.