ArticleBMC microbiology2014
Inferring the relation between transcriptional and posttranscriptional regulation from expression compendia.
Article in BMC microbiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Using genome-wide expression compendia to study microorganisms.Computational and structural biotechnology journal · 2022Review
- Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.mSystems · 2020Article
- Unified feature association networks through integration of transcriptomic and proteomic data.PLoS computational biology · 2019Article
- Species-specific transcriptomic network inference of interspecies interactions.The ISME journal · 2018Article
- Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002.Nucleic acids research · 2016Article
- Experimental approaches to identify small RNAs and their diverse roles in bacteria--what we have learnt in one decade of MicA research.MicrobiologyOpen · 2015Review
- Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality.Life (Basel, Switzerland) · 2015Article
- Genome-wide analyses of small non-coding RNAs in streptococci.Frontiers in genetics · 2015Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPublicly 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.
resultsWe 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.
conclusionIn 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.
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