Abstract
Students majoring in life sciences are seldom exposed to the use of bioinformatic tools in their regular coursework. Exponential growth of data in the biomedical research disciplines, including neuroscience, warrants the need for training undergraduate and graduate students in the use of bioinformatic and neuroinformatic tools. Two main objectives of this chapter are to provide an overview of important bioinformatic and neuroinformatic resources and to explain the usage of the commonly used bioinformatic data analysis tools. For each major tool, the theory behind the methodology is briefly described to enable the user to understand how the program works. The bioinformatic resources described in this article refer to the most commonly used tools and databases for sequence homology search, multiple sequence alignment, protein domain analysis, gene set enrichment analysis, pathway analysis, and interaction network analysis. In addition to the general-purpose tools and databases, a list of neuroinformatic specific resources is provided.
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References
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402
Bandrowski AE, Cachat J, Li Y, Müller HM, Sternberg PW, Ciccarese P, Clark T, Marenco L, Wang R, Astakhov V, Grethe JS, Martone ME (2012) A hybrid human and machine resource curation pipeline for the Neuroscience Information Framework. Database 2012:bas005
Bjaalie JG, Grillner S (2007) Global neuroinformatics: the International Neuroinformatics Coordinating Facility. J Neurosci 27:3613–3615
Borodovsky M, Lomsadze A (2011) Eukaryotic gene prediction using GeneMark.hmm-E and GeneMark-ES. Curr Protoc Bioinformatics Chapter 4:Unit 4.6.1–10
Caspi R, Altman T, Dreher K, Fulcher CA, Subhraveti P, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Mueller LA, Ong Q, Paley S, Pujar A, Shearer AG, Travers M, Weerasinghe D, Zhang P, Karp PD (2012) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 40:D742–D753
Dayhoff MO, Schwartz RM, Orcutt BC (1978) A model of evolutionary change in protein. Atlas Protein Seq Struct 5:345–352
Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797
Geschwind D (2004) GENSAT: a genomic resource for neuroscience research. Lancet Neurol 3:82
Gonnet GH, Cohen MA, Benner SA (1992) Exhaustive matching of the entire protein sequence database. Science 256:1443–1445
Gribskov M, McLachlan AD, Eisenberg D (1987) Profile analysis: detection of distantly related proteins. Proc Natl Acad Sci U S A 84:4355–4358
Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A 89:10915–10919
Huerta MF, Koslow SH, Leshner AI (1993) The Human Brain Project: an international resource. Trends Neurosci 16:436–438
Jones AR, Overly CC, Sunkin SM (2009) The Allen brain atlas: 5 years and beyond. Nat Rev Neurosci 10:821–828
Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Res 40:D109–D114
Kötter R (2004) Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Neuroinformatics 2:127–144
Marchler-Bauer A, Panchenko AR, Shoemaker BA, Thiessen PA, Geer LY, Bryant SH (2002) CDD: a database of conserved domain alignments with links to domain three-dimensional structure. Nucleic Acids Res 30:281–283
Pierleoni A, Martelli PL, Casadio R (2008) PredGPI: a GPI-anchor predictor. BMC Bioinformatics 23:392
Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, Heger A, Holm L, Sonnhammer EL, Eddy SR, Bateman A, Finn RD (2012) The Pfam protein families database. Nucleic Acids Res 40:D290–D301
Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–432
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550
Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680
Yoon BJ (2009) Hidden Markov models and their applications in biological sequence analysis. Curr Genomics 10:402–415
Zhang Z, Schwartz S, Wagner L, Miller W (2000) A greedy algorithm for aligning DNA sequences. J Comput Biol 7:203–214
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Guda, C. (2014). Bioinformatic Methods and Resources for Neuroscience Research. In: Xiong, H., Gendelman, H.E. (eds) Current Laboratory Methods in Neuroscience Research. Springer Protocols Handbooks. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8794-4_31
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DOI: https://doi.org/10.1007/978-1-4614-8794-4_31
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