Linked Life Data

19844228

Source:http://linkedlifedata.com/resource/pubmed/id/19844228

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11 Suppl
pubmed:dateCreated
2009-10-21
pubmed:abstractText
Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1548-7105
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
6
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
S22-32
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Computation for ChIP-seq and RNA-seq studies.
pubmed:affiliation
Center for Advanced Computing Research, California Institute of Technology, Pasadena, California, USA.
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural