By Laura Elnitski, Helen Piontkivska, Lonnie R. Welch

Mapping the genomic landscapes is without doubt one of the most enjoyable frontiers of technology. we've the chance to opposite engineer the blueprints and the regulate platforms of residing organisms. Computational instruments are key enablers within the decoding procedure. This publication offers an in-depth presentation of a few of the real computational biology ways to genomic series research. the 1st component of the booklet discusses tools for locating styles in DNA and RNA. this can be by means of the second one part that displays on equipment in quite a few methods, together with functionality, utilization and paradigms.

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Nucleic Acids Res 32: D91–D94. Sandve GK, Drabløs F. (2006) A survey of motif discovery methods in an integrated framework. Biol Direct 1: 11. Schneider TD, Stephens RM. (1990) Sequence logos: A new way to display consensus sequences. Nucleic Acids Res 18: 6097–6100. Sharon E, Lubliner S, Segal E, Stormo G. (2008) A feature-based approach to modeling protein–DNA interactions. PLoS Comput Biol 4: e1000154. Siddharthan R. (2008) PhyloGibbs-MP: Module prediction and discriminative motiffinding by Gibbs sampling.

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Please refer to the online version of the tutorial for details. December 16, 2010 18 16:54 9in x 6in Advances in Genomic Sequence Analysis and Pattern Discovery b1051-ch01 M. Piipari, T. A. Down and T. J. P. Hubbard Fig. 7. Evaluation of different sequence background model class counts with mevaluatebg (1st order Markov chain) with the STAT1 ChIP-seq peak sequences. The sequence likelihood is shown to level after four classes. 4. , 2010) (Fig. , 2004). 5. Motif overrepresentation analysis When interpreting the output of NestedMICA, it is important to note that the algorithm does not rank its output motifs relative to one another or predict hit positions for them.

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