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Xenopus tropicalis Genome Re-Scaffolding and Re-Annotation Reach the Resolution Required for In Vivo ChIA-PET Analysis.

Authors :
Buisine, Nicolas
Ruan, Xiaoan
Bilesimo, Patrice
Grimaldi, Alexis
Alfama, Gladys
Ariyaratne, Pramila
Mulawadi, Fabianus
Chen, Jieqi
Sung, Wing-Kin
Liu, Edison T.
Demeneix, Barbara A.
Ruan, Yijun
Sachs, Laurent M.
Source :
PLoS ONE; 9/8/2015, Vol. 10 Issue 9, p1-27, 27p
Publication Year :
2015

Abstract

Genome-wide functional analyses require high-resolution genome assembly and annotation. We applied ChIA-PET to analyze gene regulatory networks, including 3D chromosome interactions, underlying thyroid hormone (TH) signaling in the frog Xenopus tropicalis. As the available versions of Xenopus tropicalis assembly and annotation lacked the resolution required for ChIA-PET we improve the genome assembly version 4.1 and annotations using data derived from the paired end tag (PET) sequencing technologies and approaches (e.g., DNA-PET [gPET], RNA-PET etc.). The large insert (~10Kb, ~17Kb) paired end DNA-PET with high throughput NGS sequencing not only significantly improved genome assembly quality, but also strongly reduced genome “fragmentation”, reducing total scaffold numbers by ~60%. Next, RNA-PET technology, designed and developed for the detection of full-length transcripts and fusion mRNA in whole transcriptome studies (ENCODE consortia), was applied to capture the 5' and 3' ends of transcripts. These amendments in assembly and annotation were essential prerequisites for the ChIA-PET analysis of TH transcription regulation. Their application revealed complex regulatory configurations of target genes and the structures of the regulatory networks underlying physiological responses. Our work allowed us to improve the quality of Xenopus tropicalis genomic resources, reaching the standard required for ChIA-PET analysis of transcriptional networks. We consider that the workflow proposed offers useful conceptual and methodological guidance and can readily be applied to other non-conventional models that have low-resolution genome data. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19326203
Volume :
10
Issue :
9
Database :
Complementary Index
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
109322823
Full Text :
https://doi.org/10.1371/journal.pone.0137526