Buchumschlag

Using Trawler_standalone to discover overrepresented motifs in DNA and RNA sequences derived from various experiments including chromatin immunoprecipitation

Weitere Versionen anzeigen (1)

Genome-wide location analysis has become a standard technology to unravel gene regulation networks. The accurate characterization of nucleotide signatures in sequences is key to uncovering the regulatory logic but remains a computational challenge. This protocol describes how to best characterize th...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Haudry, Yannick (VerfasserIn) , Ramialison, Mirana (VerfasserIn) , Wittbrodt, Joachim (VerfasserIn) , Ettwiller, Laurence (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 4 February 2010
In: Nature protocols
Year: 2010, Jahrgang: 5, Heft: 2, Pages: 323-334
ISSN:1750-2799
DOI:10.1038/nprot.2009.158
Online-Zugang:Verlag, Volltext: http://dx.doi.org/10.1038/nprot.2009.158
Verlag, Volltext: https://www.nature.com/nprot/journal/v5/n2/full/nprot.2009.158.html
Volltext
Verfasserangaben:Yannick Haudry, Mirana Ramialison, Benedict Paten, Joachim Wittbrodt, Laurence Ettwiller
Beschreibung
Zusammenfassung:Genome-wide location analysis has become a standard technology to unravel gene regulation networks. The accurate characterization of nucleotide signatures in sequences is key to uncovering the regulatory logic but remains a computational challenge. This protocol describes how to best characterize these signatures (motifs) using the new standalone version of Trawler, which was designed and optimized to analyze chromatin immunoprecipitation (ChIP) data sets. In particular, we describe the three main steps of Trawler_standalone (motif discovery, clustering and visualization) and discuss the appropriate parameters to be used in each step depending on the data set and the biological questions addressed. Compared to five other motif discovery programs, Trawler_standalone is in most cases the fastest algorithm to accurately predict the correct motifs especially for large data sets. Its running time ranges within few seconds to several minutes, depending on the size of the data set and the parameters used. This protocol is best suited for bioinformaticians seeking to use Trawler_standalone in a high-throughput manner.
Beschreibung:Gesehen am 17.05.2017
Beschreibung:Online Resource
ISSN:1750-2799
DOI:10.1038/nprot.2009.158

Ähnliche Einträge