Publication
Title
Inferring the relation between transcriptional and posttranscriptional regulation from expression compendia
Author
Abstract
Background: Publicly available expression compendia that measure both mRNAs and sRNAs provide a promising resource to simultaneously infer the transcriptional and the posttranscriptional network. To maximally exploit the information contained in such compendia, we propose an analysis flow that combines publicly available expression compendia and sequence-based predictions to infer novel sRNA-target interactions and to reconstruct the relation between the sRNA and the transcriptional network. Results: We relied on module inference to construct modules of coexpressed genes (sRNAs). TFs and sRNAs were assigned to these modules using the state-of-the-art inference techniques LeMoNe and Context Likelihood of Relatedness (CLR). Combining these expressions with sequence-based sRNA-target interactions allowed us to predict 30 novel sRNA-target interactions comprising 14 sRNAs. Our results highlight the role of the posttranscriptional network in finetuning the transcriptional regulation, e.g. by intra-operonic regulation. Conclusion: In this work we show how strategies that combine expression information with sequence-based predictions can help unveiling the intricate interaction between the transcriptional and the posttranscriptional network in prokaryotic model systems.
Language
English
Source (journal)
BMC microbiology. - London
Publication
London : 2014
ISSN
1471-2180
DOI
10.1186/1471-2180-14-14
Volume/pages
14 (2014) , p. 1-14
Article Reference
14
ISI
000335260700001
Pubmed ID
24467879
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 31.10.2023
Last edited 01.11.2023
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