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Bioinformatics and Its Relevance to Weed Science

Published online by Cambridge University Press:  20 January 2017

Ignacio M. Larrinua  and
Scott B. Belmar
Ignacio M. Larrinua*
Affiliation:
Information Management, Dow AgroSciences LLC, 9330 Zionsville Road, Indianapolis, IN 46268
Scott B. Belmar
Affiliation:
Information Management, Dow AgroSciences LLC, 9330 Zionsville Road, Indianapolis, IN 46268
*
Corresponding author's E-mail: [email protected]
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Abstract

An overview of bioinformatics is presented with an emphasis on describing a set of tools, databases, and ontologies useful to the weed science community. These tools can be used to identify genes whose product may be the target site of a herbicide or in the degradation pathway of a xenobiotic. They may identify receptors responsible for pathogen recognition or enzymes in the metabolic pathway for allelopathic compounds. Whatever the gene of interest, bioinformatics allows researchers to assemble complete or partial genes from expressed sequence tags (ESTs), complete cDNAs, or complete genomes and then translate them into their corresponding amino acid sequences. Similarity searches can be used to find other proteins with homology to the gene of interest, which can provide clues to its function from the annotation of these database hits. The use of protein domain databases can also provide insight into the functional capabilities of the protein in question and delineate those portions essential for activity. Enzyme Commission (EZ) numbers or gene ontology (GO) descriptors allow placement of the protein within the larger network context of a biological system. These capabilities allow the scientist to probe deeper into the function of their protein of interest to gain a novel understanding of the biology.

Keywords

BLASTCDDCn3DDeepViewEC numberEMBOSSESTFASTAGCGGO annotationmicroarrayInterProScanNCBIProteomePfamsequence similarity searchSMS2TIGRFAMUniProtwEMBOSS
Type
Symposium
Information
Weed Science , Volume 56 , Issue 2 , April 2008 , pp. 297 - 305
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. 1990. Basic local alignment search tool. J. Mol. Biol. 215:403–410.CrossRefGoogle ScholarPubMed
Anderson, J. V., Horvath, D. P., Chao, W. S., Foley, M. E., Hernandez, A. G., Thimmapuram, J., Liu, L., Gong, G. L., Band, Mark, Kim, R., and Mikel, M. A. 2007. Characterization of an EST database for the perennial weed leafy spurge: an important resource for weed biology research. Weed Sci. 55:193–203.CrossRefGoogle Scholar
Apweiler, R., Attwood, T. K., Bairoch, A., Bateman, A., Birney, E., Biswas, M., Bucher, P., Cerutti, L., Corpet, F., Croning, M. D., Durbin, R., Falquet, L., Fleischmann, W., Gouzy, J., Hermjakob, H., Hulo, N., Jonassen, I., Kahn, D., Kanapin, A., Karavidopoulou, Y., Lopez, R., Marx, B., Mulder, N. J., Oinn, T. M., Pagni, M., Servant, F., Sigrist, C. J., and Zdobnov, E. M. 2001. The InterPro database, an integrated documentation resource for protein families, domains, and functional sites. Nucleic Acids Res. 29:37–40.CrossRefGoogle ScholarPubMed
Attwood, T. K., Mitchell, A., Gaulton, A., Moulton, G., and Tabernero, L. 2006. The PRINTS protein fingerprint database: functional and evolutionary applications. in Dunn, M., Jorde, L., Little, P., and Subramaniam, A. Encyclopaedia of Genetics, Genomics, Proteomics and Bioinformatics. New York John Wiley & Sons.Google Scholar
Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., and Wheeler, D. L. 2006. GenBank. Nucleic Acids Res. 34:D16–D20.CrossRefGoogle ScholarPubMed
Chao, W. S., Horvath, D. P., Anderson, J. V., and Foley, M. P. 2005. Potential model weeds to study genomics, ecology, and physiology in the 21st century. Weed Sci. 53:929–937.CrossRefGoogle Scholar
Delcher, A. L., Harmon, D., Kasif, S., White, O., and Salzberg, S. L. 1999. Nucleic Acids Res. 27:4636–4641. Improved microbial gene identification with GLIMMER.CrossRefGoogle Scholar
Devereux, J., Haeberli, P., and Smithies, O. 1984. A comprehensive set of sequence analysis programs for the VAX. Nucleic Acids Res. 12:387–395.CrossRefGoogle ScholarPubMed
Ewing, B. and Green, P. 1998. Basecalling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8:186–194.CrossRefGoogle ScholarPubMed
Ewing, B., Hillier, L., Wendl, M., and Green, P. 1998. Basecalling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 8:175–185.CrossRefGoogle ScholarPubMed
Finn, R. D., Mistry, J., Schuster-Bockler, B., Griffiths-Jones, S., Hollich, V., Lassmann, T., Moxon, S., Marshall, M., Khanna, A., Durbin, R., Eddy, S. R., Sonnhammer, E. L., and Bateman, A. 2006. Pfam: clans, web tools and services. Nucleic Acids Res. 34:D247–D251.CrossRefGoogle ScholarPubMed
Frohman, M. A., Dush, M. K., and Martin, G. R. 1988. Rapid production of full-length cDNAs from rare transcripts: amplification using a single gene-specific oligonucleotide primer. Proc. Natl. Acad. Sci. USA. 85:8998–9002.CrossRefGoogle ScholarPubMed
The Gene Ontology Consortium 2000. Gene ontology: tool for the unification of biology. Nature Genet. 25:25–29.CrossRefGoogle Scholar
Guex, N. and Peitsch, M. C. 1997. SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis. 18:2714–2723.CrossRefGoogle ScholarPubMed
Haft, D. H., Selengut, J. D., and White, O. 2003. The TIGRFAMs database of protein families. Nucleic Acids Res. 31:371–373.CrossRefGoogle ScholarPubMed
Horvath, D. P., Anderson, J. V., Soto-Suarez, M., and Chao, W. S. 2006. Transcriptome analysis of leafy spurge (Euphorbia esula) crown buds during shifts in well defined phases of dormancy. Weed Sci. 54:824–827.CrossRefGoogle Scholar
Horvath, D. P., Soto, M., Jia, Y., Chao, W. S., and Anderson, J. V. 2005. Transcriptome analysis of paradormancy release in root buds of leafy spurge (Euphorbia esula). Weed Sci. 53:795–801.CrossRefGoogle Scholar
Hulo, N., Bairoch, A., Bulliard, V., Cerutti, L., De Castro, E., Langendijk-Genevaux, P. S., Pagni, M., and Sigrist, C. J. A. 2006. The PROSITE database. Nucleic Acids Res. 34:D227–D230.CrossRefGoogle ScholarPubMed
Iseli, C., Jongeneel, C. V., and Bucher, P. 1999. ESTScan: a program for detecting, evaluating, and reconstructing potential coding regions in EST sequences. Proc 7th ISMB. 138–148.Google Scholar
Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K. F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., and Hirakawa, M. 2006. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34:D354–D357.CrossRefGoogle ScholarPubMed
Lipman, D. J. and Pearson, W. R. 1985. Rapid and sensitive protein similarity searches. Science. 227 (4693):1435–1441.CrossRefGoogle ScholarPubMed
Lukashin, A. and Borodovsky, M. 1998. GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res. 26:1107–1115.CrossRefGoogle ScholarPubMed
Majoros, W. H., Pertea, M., and Salzberg, S. L. 2004. TigrScan and GlimmerHMM: two open-source ab initio eukaryotic gene-finders. Bioinformatics. 20:2878–2879.CrossRefGoogle ScholarPubMed
Marchler-Bauer, A., Anderson, J. B., Cherukuri, P. F., DeWeese-Scott, C., Geer, L. Y., Gwadz, M., He, S., Hurwitz, D. I., Jackson, J. D., Ke, Z., Lanczycki, C., Liebert, C. A., Liu, C., Lu, F., Marchler, G. H., Mullokandov, M., Shoemaker, B. A., Simonyan, V., Song, J. S., Thiessen, P. A., Yamashita, R. A., Yin, J. J., Zhang, D., and Bryant, S. H. 2005. CDD: a conserved domain database for protein classification. Nucleic Acids Res. 33:D192–D196.CrossRefGoogle ScholarPubMed
Okagaki, R. J. and Phillips, R. L. 2004. Maize DNA-sequencing strategies and genome organization. Genome Biol. 5:223.CrossRefGoogle ScholarPubMed
Rhee, S. Y., Dickerson, J., and Dong, X. 2006. Annu. Rev. Plant Biol. 57:335–360.CrossRefGoogle Scholar
Rice, P., Longden, I., and Bleasby, A. 2000. EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 16:276–277.CrossRefGoogle ScholarPubMed
Sarachu, M. and Colet, M. 2005. wEMBOSS: a web interface for EMBOSS. Bioinformatics. 21:540–541.CrossRefGoogle ScholarPubMed
Sonnhammer, E. L. L., Eddy, S. R., and Durbin, R. 1997. Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins. 28:405–420.3.0.CO;2-L>CrossRefGoogle ScholarPubMed
Stothard, P. 2000. The Sequence Manipulation Suite: JavaScript programs for analyzing and formatting protein and DNA sequences. Biotechniques. 28:1102–1104.CrossRefGoogle ScholarPubMed