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Algorithms accessible via Chinook

Last modified 2007-02-06 14:28

Algorithm support

Chinook is designed to be a dynamically changing environment. This list represents some of the algorithms that we have integrated into it. It is by no means comprehensive or a static list of services that are available

Alignment Algorithms

CLUSTAL W Thompson JD, Higgins DG, and 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(22):4673-80. (*)Conreal Berezikov, E., V. Guryev, R.H. Plasterk, and E. Cuppen. 2004. CONREAL: conserved regulatory elements anchored alignment algorithm for identification of transcription factor binding sites by phylogenetic footprinting. Genome Res 14: 170-178. DIALIGN 2 B. Morgenstern, K. Frech, A. Dress, and T. Werner. 1998. DIALIGN: Finding local similarities by multiple sequence alignment. Bioinformatics 14, 1998, 290-294. Lagan and MultiLagan Brudno, M., C.B. Do, G.M. Cooper, M.F. Kim, E. Davydov, E.D. Green, A. Sidow, and S. Batzoglou. 2003. LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomic DNA. Genome Res 13: 721-731. Mauve Darling, A., Mau, B., Blattner, F.R., and N. Perna. Mauve. Code available at
http://asap.ahabs.wisc.edu/mauve/index.php (*)Orca Wasserman et al. Unpublished Shuffle-Lagan Brudno, M., S. Malde, A. Poliakov, C.B. Do, O. Couronne, I. Dubchak, and S. Batzoglou. 2003b. Glocal alignment: finding rearrangements during alignment. Bioinformatics 19 Suppl 1: I54-I62. T-Coffee Notredame, C., D.G. Higgins, and J. Heringa. 2000. T-Coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol 302: 205-217.

Primer Prediction

Primer3 Rosen, S. and H.J. Skaletski. Primer3. Code available at
http://www-genome.wi.mit.edu/genome_software/other/primer3.html.

Motif Discovery

(*)ANN-Spec Workman, C.T. and G.D. Stormo. 2000. ANN-Spec: a method for discovering transcription factor binding sites with improved specificity. Pac Symp Biocomput: 467-478. ELPH Gibbs Sampler http://www.tigr.org/software/ELPH/index.shtml Gibbs Motif Sampler Thompson W, Rouchka EC, Lawrence CE. 2003. Gibbs Recursive Sampler: finding transcription factor binding sites. Nucleic Acids Res. 2003 Jul 1;31(13):3580-5. MEME Bailey TL, and C. Elkan. 1994. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol. 2:28-36 Motifsampler Thijs, G., M. Lescot, K. Marchal, S. Rombauts, B. De Moor, P. Rouze, and Y. Moreau. 2001. A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics (*)PromotorWise Birney. Unpublished. (*)RSAT oligo analysis van Helden, J. 2003. Regulatory sequence analysis tools. Nucleic Acids Res 31: 3593-3596. (*)STUBB Sinha, S., E. Van Nimwegen, and E.D. Siggia. 2003. A probabilistic method to detect regulatory modules. Bioinformatics 19 Suppl 1: I292-I301 Teiresius motif finder Rigoutsos, I. and A. Floratos. 1998. Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm. Bioinformatics 14: 55-67. (w)consensus Hertz, G.Z. and G.D. Stormo. 1999. Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics 15: 563-577. (*) - development in progress