NestedMICA as an ab initio protein motif discovery tool. Background: Discovering overrepresented patterns in amino acid sequences is an important step in protein functional element identification. We adapted and extended NestedMICA, an ab initio motif finder originally developed for finding transcription binding site motifs, to find short protein signals, and compared its performance with another popular protein motif finder, MEME. NestedMICA, an open source protein motif discovery tool written in Java, is driven by a Monte Carlo technique called Nested Sampling. It uses multi-class sequence background models to represent different ”uninteresting” parts of sequences that do not contain motifs of interest. In order to assess NestedMICA as a protein motif finder, we have tested it on synthetic datasets produced by spiking instances of known motifs into a randomly selected set of protein sequences. NestedMICA was also tested using a biologically-authentic test set, where we evaluated its performance with respect to varying sequence length.

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  1. Luo, Jia-wei; Wang, Ting: Motif discovery using an immune genetic algorithm (2010)
  2. Piipari, Matias; Down, Thomas A.; Hubbard, Tim J. P.: Metamotifs - a generative model for building families of nucleotide position weight matrices (2010) ioport
  3. Da Piedade, Isabelle; Tang, Man-Hung Eric; Elemento, Olivier: DISPARE: discriminative pattern refinement for position weight matrices (2009) ioport
  4. Dogruel, Mutlu; Down, Thomas A.; Hubbard, Tim J. P.: Nestedmica as an ab initio protein motif discovery tool (2008) ioport