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Bioinformatics Resources of the Korean Bioinformation Center (KOBIC) 원문보기

Genomics & informatics, v.8 no.4, 2010년, pp.165 - 169  

Lee, Byung-Wook (Korean Bioinformation Center (KOBIC), KRIBB) ,  Chu, In-Sun (Korean Bioinformation Center (KOBIC), KRIBB) ,  Kim, Nam-Shin (Korean Bioinformation Center (KOBIC), KRIBB) ,  Lee, Jin-Hyuk (Korean Bioinformation Center (KOBIC), KRIBB) ,  Kim, Seon-Yong (Korean Bioinformation Center (KOBIC), KRIBB) ,  Kim, Wan-Kyu (Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University) ,  Lee, Sang-Hyuk (Korean Bioinformation Center (KOBIC), KRIBB)

Abstract ▼ AI-Helper 아이콘AI-Helper

The Korean Bioinformation Center (KOBIC) is a national bioinformatics research center in Korea. We developed many bioinformatics algorithms and applications to facilitate the biological interpretation of OMICS data. Here we present an introduction to major bioinformatics resources of databases and t...

주제어

#bioinformatics programs   #databases   #web server   #KOBIC  

AI 본문요약
AI-Helper 아이콘 AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

제안 방법

  • LocaloDom is a curated database that contains TM topologies and TM helix numbers of known protein domains. It was constructed from Pfam domains combined with Swiss-Prot annotations and Phobius predictions. The Localizome server is a highly accurate and comprehensive information source for subcellular localization for soluble proteins as well as membrane proteins.
  • First, we integrated various resources systematically to deduce catalogs of disease-related genes, single nucleotide polymorphisms (SNPs), protein mutations and relevant drugs. Next, we carried out structure modeling and docking simulation for wild-type and mutant proteins to examine the structural and functional consequences of non-synonymous SNPs in the drug-related genes. Finally, we investigated the structural and biochemical properties relevant to drug binding such as the distribution of SNPs in proximal protein pockets, thermo-chemical stability, interactions with drugs and physico-chemical properties.
  • Using the known transcriptome sequence model such as RefSeq, NEUMA pre-computes the numbers of all possible gene-wise and isoform-wise informative reads: the former being sequences mapped to all mRNA isoforms of a single gene exclusively and the latter uniquely mapped to a single mRNA isoform. The results are used to estimate the effective length of genes and transcripts, taking experimental distributions of fragment size into consideration. We propose that NEUMA could make a standard method in quantifying gene transcript levels from RNA-Seq data.

이론/모형

  • , 2006) server predicts the transmembrane (TM) helix number and TM topology of a user-supplied eukaryotic protein and presents the result as an intuitive graphic representation. It utilizes hmmpfam to detect the presence of Pfam (Finn, et al., 2010) domains and a prediction algorithm, Phobius (Kall, et al., 2007), to predict the TM helices. The results are combined and checked against the TM topology rules stored in a protein domain database called LocaloDom.
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참고문헌 (20)

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  2. Barrell, D., Dimmer, E., Huntley, R.P., Binns, D., O'Donovan, C., and Apweiler, R. (2009). The GOA database in 2009--an integrated Gene Ontology Annotation resource. Nucl. Acids Res. 37, D396-403. 

    상세보기 crossref

  3. Burke, J., Davison, D., and Hide, W. (1999). d2_cluster: a validated method for clustering EST and full-length cDNAsequences. Genome Res. 9, 1135-1142. 

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  4. Cho, S., Jun, Y., Lee, S., Choi, H.S., Jung, S., Jang, Y., Park, C., Kim, S., and Kim, W. (2011). miRGator v2.0 : an integrated system for functional investigation of microRNAs. Nucl. Acids Res. 39, D158-162. 

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  5. Finn, R.D., Mistry, J., Tate, J., Coggill, P., Heger, A., Pollington, J.E., Gavin, O.L., Gunasekaran, P., Ceric, G., Forslund, K., Holm, L., Sonnhammer, E.L., Eddy, S.R., and Bateman, A. (2010). The Pfam protein families database. Nucl. Acids Res. 38, D211-222. 

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  6. Huang, X., and Madan, A. (1999). CAP3: A DNA sequence assembly program. Genome Res. 9, 868-877. 

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  7. Hunter, S., Apweiler, R., Attwood, T.K., Bairoch, A., Bateman, A., Binns, D., Bork, P., Das, U., Daugherty, L., Duquenne, L., Finn, R.D., Gough, J., Haft, D., Hulo, N., Kahn, D., Kelly, E., Laugraud, A., Letunic, I., Lonsdale, D., Lopez, R., Madera, M., Maslen, J., McAnulla, C., McDowall, J., Mistry, J., Mitchell, A., Mulder, N., Natale, D., Orengo, C., Quinn, A.F., Selengut, J.D., Sigrist, C.J., Thimma, M., Thomas, P.D., Valentin, F., Wilson, D., Wu, C.H., and Yeats, C. (2009). InterPro: the integrative protein signature database. Nucl. Acids Res. 37, D211-215. 

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  8. Kall, L., Krogh, A., and Sonnhammer, E.L. (2007). Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server. Nucl. Acids Res. 35, W429-432. 

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  9. Kelso, J., Visagie, J., Theiler, G., Christoffels, A., Bardien, S., Smedley, D., Otgaar, D., Greyling, G., Jongeneel, C.V., McCarthy, M.I., Hide, T., and Hide, W. (2003). eVOC: a controlled vocabulary for unifying gene expression data. Genome Res. 13, 1222-1230. 

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  10. Lee, B., and Lee, D. (2009). Protein comparison at the domain architecture level. BMC Bioinformatics 10 Suppl 15, S5. 

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  11. Lee, B., and Shin, G. (2009). CleanEST: a database of cleansed EST libraries. Nucl. Acids Res. 37, D686-689. 

    상세보기 crossref

  12. Lee, B., Hong, T., Byun, S.J., Woo, T., and Choi, Y.J. (2007). ESTpass: a web-based server for processing and annotating expressed sequence tag (EST) sequences. Nucl. Acids Res. 35, W159-162. 

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  13. Lee, B., Kim, T., Kim, S.K., Lee, K.H., and Lee, D. (2007). Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications. Nucl. Acids Res. 35, D47-50. 

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  14. Lee, S., Lee, B., Jang, I., Kim, S., and Bhak, J. (2006). Localizome: a server for identifying transmembrane topologies and TM helices of eukaryotic proteins utilizing domain information. Nucl. Acids Res. 34, W99-103. 

    상세보기 crossref

  15. Lee, S., Seo, C.H., Lim, B., Yang, J.O., Oh, J., Kim, M., Lee, B., and Kang, C. (2010). Accurate quantification of transcriptome from RNA-Seq data by effective length normalization. Nucl. Acids Res. 38, 1-10. 

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  16. Marguerat, S., and Bahler, J. (2010). RNA-seq: from technology to biology. Cell Mol. Life Sci. 67, 569-579. 

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  17. Pruitt, K.D., Tatusova, T., and Maglott, D.R. (2007). NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucl. Acids Res. 35, D61-65. 

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  18. Radeva, M., Hofmann, T., Altenberg, B., Mothes, H., Richter, K.K., Pool-Zobel, B., and Greulich, K.O. (2008). The database dbEST correctly predicts gene expression in colon cancer patients. Curr. Pharm. Biotechnol. 9, 510-515. 

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  19. Sequeira, E., McEntyre, J., and Lipman, D. (2001). PubMed Central decentralized. Nature 410, 740. 

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  20. Yang, J.O., Oh, S., Ko, G., Park, S.J., Kim, W.Y., Lee, B., and Lee, S. (2011). VnD: a structure-centric database of disease-related SNPs and drugs. Nucl. Acids Res. 39, D939-944. 

    상세보기 crossref
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