Document Detail


Utilizing RNA-Seq data for de novo coexpression network inference.
MedLine Citation:
PMID:  22556371     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
MOTIVATION: RNA-Seq experiments have shown great potential for transcriptome profiling. While sequencing increases the level of biological detail, integrative data analysis is also important. One avenue is the construction of coexpression networks. Because the capacity of RNA-Seq data for network construction has not been previously evaluated, we constructed a coexpression network using striatal samples, derived its network properties and compared it with microarray-based networks.
RESULTS: The RNA-Seq coexpression network displayed scale-free, hierarchical network structure. We detected transcripts groups (modules) with correlated profiles; modules overlap distinct ontology categories. Neuroanatomical data from the Allen Brain Atlas reveal several modules with spatial colocalization. The network was compared with microarray-derived networks; correlations from RNA-Seq data were higher, likely because greater sensitivity and dynamic range. Higher correlations result in higher network connectivity, heterogeneity and centrality. For transcripts present across platforms, network structure appeared largely preserved. From this study, we present the first RNA-Seq data de novo network inference.
Authors:
Ovidiu D Iancu; Sunita Kawane; Daniel Bottomly; Robert Searles; Robert Hitzemann; Shannon McWeeney
Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2012-05-03
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  28     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2012 Jun 
Date Detail:
Created Date:  2012-06-12     Completed Date:  2013-01-31     Revised Date:  2014-03-19    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  1592-7     Citation Subset:  IM    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Animals
Gene Expression Profiling / methods*
Gene Regulatory Networks*
Male
Mice
Mice, Inbred C57BL
Mice, Inbred DBA
Oligonucleotide Array Sequence Analysis / methods
RNA / genetics
Sequence Analysis, RNA / methods*
Grant Support
ID/Acronym/Agency:
5P30CA069533-13/CA/NCI NIH HHS; 5UL1RR024140/RR/NCRR NIH HHS; AA010760/AA/NIAAA NIH HHS; AA011114/AA/NIAAA NIH HHS; AA013484/AA/NIAAA NIH HHS; DA005228/DA/NIDA NIH HHS; MH051372/MH/NIMH NIH HHS; P30 CA069533/CA/NCI NIH HHS; R01 DA005228/DA/NIDA NIH HHS; R01 MH051372/MH/NIMH NIH HHS; U01 AA013484/AA/NIAAA NIH HHS; UL1 TR000128/TR/NCATS NIH HHS
Chemical
Reg. No./Substance:
63231-63-0/RNA
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  SEQCHIP: a powerful method to integrate sequence and genotype data for the detection of rare variant...
Next Document:  Long-Term Ongoing Coagulopathy in Premature Infants With Respiratory Distress Syndrome.