Linked Life Data

20134431

Source:http://linkedlifedata.com/resource/pubmed/id/20134431

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-2-5
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1750-2799
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
323-34
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Using Trawler_standalone to discover overrepresented motifs in DNA and RNA sequences derived from various experiments including chromatin immunoprecipitation.
pubmed:affiliation
Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't