Literature DB >> 18566768

Discovering sequence motifs.

Timothy L Bailey1.   

Abstract

Sequence motif discovery algorithms are an important part of the computational biologist's toolkit. The purpose of motif discovery is to discover patterns in biopolymer (nucleotide or protein) sequences in order to better understand the structure and function of the molecules the sequences represent. This chapter provides an overview of the use of sequence motif discovery in biology and a general guide to the use of motif discovery algorithms. The chapter discusses the types of biological features that DNA and protein motifs can represent and their usefulness. It also defines what sequence motifs are, how they are represented, and general techniques for discovering them. The primary focus is on one aspect of motif discovery: discovering motifs in a set of unaligned DNA or protein sequences. Also presented are steps useful for checking the biological validity and investigating the function of sequence motifs using methods such as motif scanning--searching for matches to motifs in a given sequence or a database of sequences. A discussion of some limitations of motif discovery concludes the chapter.

Mesh:

Year:  2008        PMID: 18566768     DOI: 10.1007/978-1-60327-159-2_12

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  10 in total

1.  Identification of a novel type of WRKY transcription factor binding site in elicitor-responsive cis-sequences from Arabidopsis thaliana.

Authors:  Fabian Machens; Marlies Becker; Felix Umrath; Reinhard Hehl
Journal:  Plant Mol Biol       Date:  2013-10-09       Impact factor: 4.076

2.  The 'Tyranny of choices' in the ingestion-controlling network.

Authors:  Michael Myslobodsky
Journal:  Obes Facts       Date:  2009-12-04       Impact factor: 3.942

3.  Finding subtypes of transcription factor motif pairs with distinct regulatory roles.

Authors:  Abha Singh Bais; Naftali Kaminski; Panayiotis V Benos
Journal:  Nucleic Acids Res       Date:  2011-04-12       Impact factor: 16.971

4.  Mining Functional Elements in Messenger RNAs: Overview, Challenges, and Perspectives.

Authors:  Firoz Ahmed; Vagner A Benedito; Patrick Xuechun Zhao
Journal:  Front Plant Sci       Date:  2011-11-30       Impact factor: 5.753

5.  The XXmotif web server for eXhaustive, weight matriX-based motif discovery in nucleotide sequences.

Authors:  Sebastian Luehr; Holger Hartmann; Johannes Söding
Journal:  Nucleic Acids Res       Date:  2012-06-12       Impact factor: 16.971

6.  HOCOMOCO: a comprehensive collection of human transcription factor binding sites models.

Authors:  Ivan V Kulakovskiy; Yulia A Medvedeva; Ulf Schaefer; Artem S Kasianov; Ilya E Vorontsov; Vladimir B Bajic; Vsevolod J Makeev
Journal:  Nucleic Acids Res       Date:  2012-11-21       Impact factor: 16.971

7.  cWords - systematic microRNA regulatory motif discovery from mRNA expression data.

Authors:  Anders Jacobsen; Simon H Rasmussen; Anders Krogh
Journal:  Silence       Date:  2013-05-20

8.  Inference of self-regulated transcriptional networks by comparative genomics.

Authors:  Joseph P Cornish; Fialelei Matthews; Julien R Thomas; Ivan Erill
Journal:  Evol Bioinform Online       Date:  2012-08-06       Impact factor: 1.625

9.  Functional motifs in Escherichia coli NC101.

Authors:  Gholamreza Motalleb
Journal:  Int J Mol Cell Med       Date:  2013

10.  'In silico expression analysis', a novel PathoPlant web tool to identify abiotic and biotic stress conditions associated with specific cis-regulatory sequences.

Authors:  Julio C Bolívar; Fabian Machens; Yuri Brill; Artyom Romanov; Lorenz Bülow; Reinhard Hehl
Journal:  Database (Oxford)       Date:  2014-04-10       Impact factor: 3.451

  10 in total

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