Analysis of Strand-Specific RNA-Seq Data Using Machine Learning Reveals the Structures of Transcription Units in Clostridium thermocellum
Abstract
Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-specific RNA-seq (ssRNA-seq) datasets collected under two experimental conditions to derive the genomic TU organization of Clostridium thermocellum using a machine-learning approach. Our method accurately predicted the genomic boundaries of individual TUs based on two sets of parameters measuring the RNA-seq expression patterns across the genome: expression-level continuity and variance. A total of 2590 distinct TUs are predicted based on the four RNA-seq datasets. Among the predicted TUs, 44% have multiple genes. We assessed our prediction method on an independent set of RNA-seq data with longer reads. The evaluation confirmed the high quality of the predicted TUs. Functional enrichment analyses on a selected subset of the predicted TUs revealed interesting biology. To demonstrate the generality of the prediction method, we have also applied the method to RNA-seq data collected on Escherichia coli and achieved high prediction accuracies. The TU prediction program named SeqTU is publicly available at https://code.google.com/p/seqtu/. We expect that the predicted TUs can serve as the baseline information for studying transcriptional and post-transcriptionalmore »
- Authors:
-
- Univ. of Georgia, Athens, GA (United States); BioEnergy Science Center, Oak Ridge, TN (United States)
- BioEnergy Science Center, Oak Ridge, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Univ. of Georgia, Athens, GA (United States)
- BioEnergy Science Center, Oak Ridge, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- BioEnergy Science Center, Oak Ridge, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States);
- Univ. of Georgia, Athens, GA (United States); BioEnergy Science Center, Oak Ridge, TN (United States); Jilin Univ., Changchun (China)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). BioEnergy Science Center (BESC)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1242033
- Alternate Identifier(s):
- OSTI ID: 1265796
- Report Number(s):
- NREL/JA-5100-64668
Journal ID: ISSN 0305-1048
- Grant/Contract Number:
- AC36-08GO28308; AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nucleic Acids Research
- Additional Journal Information:
- Journal Volume: 43; Journal Issue: 10; Related Information: Nucleic Acids Research; Journal ID: ISSN 0305-1048
- Publisher:
- Oxford University Press
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 09 BIOMASS FUELS; 59 BASIC BIOLOGICAL SCIENCES; transcription units (TU); bacterial genome
Citation Formats
Chou, Wen-Chi, Ma, Qin, Yang, Shihui, Cao, Sha, Klingeman, Dawn M., Brown, Steven D., and Xu, Ying. Analysis of Strand-Specific RNA-Seq Data Using Machine Learning Reveals the Structures of Transcription Units in Clostridium thermocellum. United States: N. p., 2015.
Web. doi:10.1093/nar/gkv177.
Chou, Wen-Chi, Ma, Qin, Yang, Shihui, Cao, Sha, Klingeman, Dawn M., Brown, Steven D., & Xu, Ying. Analysis of Strand-Specific RNA-Seq Data Using Machine Learning Reveals the Structures of Transcription Units in Clostridium thermocellum. United States. https://doi.org/10.1093/nar/gkv177
Chou, Wen-Chi, Ma, Qin, Yang, Shihui, Cao, Sha, Klingeman, Dawn M., Brown, Steven D., and Xu, Ying. Thu .
"Analysis of Strand-Specific RNA-Seq Data Using Machine Learning Reveals the Structures of Transcription Units in Clostridium thermocellum". United States. https://doi.org/10.1093/nar/gkv177. https://www.osti.gov/servlets/purl/1242033.
@article{osti_1242033,
title = {Analysis of Strand-Specific RNA-Seq Data Using Machine Learning Reveals the Structures of Transcription Units in Clostridium thermocellum},
author = {Chou, Wen-Chi and Ma, Qin and Yang, Shihui and Cao, Sha and Klingeman, Dawn M. and Brown, Steven D. and Xu, Ying},
abstractNote = {Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-specific RNA-seq (ssRNA-seq) datasets collected under two experimental conditions to derive the genomic TU organization of Clostridium thermocellum using a machine-learning approach. Our method accurately predicted the genomic boundaries of individual TUs based on two sets of parameters measuring the RNA-seq expression patterns across the genome: expression-level continuity and variance. A total of 2590 distinct TUs are predicted based on the four RNA-seq datasets. Among the predicted TUs, 44% have multiple genes. We assessed our prediction method on an independent set of RNA-seq data with longer reads. The evaluation confirmed the high quality of the predicted TUs. Functional enrichment analyses on a selected subset of the predicted TUs revealed interesting biology. To demonstrate the generality of the prediction method, we have also applied the method to RNA-seq data collected on Escherichia coli and achieved high prediction accuracies. The TU prediction program named SeqTU is publicly available at https://code.google.com/p/seqtu/. We expect that the predicted TUs can serve as the baseline information for studying transcriptional and post-transcriptional regulation in C. thermocellum and other bacteria.},
doi = {10.1093/nar/gkv177},
journal = {Nucleic Acids Research},
number = 10,
volume = 43,
place = {United States},
year = {Thu Mar 12 00:00:00 EDT 2015},
month = {Thu Mar 12 00:00:00 EDT 2015}
}
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The Listeria transcriptional landscape from saprophytism to virulence
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The transcription unit architecture of the Escherichia coli genome
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Mapping the Burkholderia cenocepacia niche response via high-throughput sequencing
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Mycoplasma hyopneumoniae Transcription Unit Organization: Genome Survey and Prediction
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Genome-wide operon prediction in Staphylococcus aureus
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Interruptions in gene expression drive highly expressed operons to the leading strand of DNA replication
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- Nucleic Acids Research, Vol. 33, Issue 10
ODB: a database of operons accumulating known operons across multiple genomes
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- Nucleic Acids Research, Vol. 34, Issue 90001
DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information
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DOOR: a database for prokaryotic operons
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OperonDB: a comprehensive database of predicted operons in microbial genomes
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Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists
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A new framework for identifying cis-regulatory motifs in prokaryotes
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RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more
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Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs
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Rapid, accurate, computational discovery of Rho-independent transcription terminators illuminates their relationship to DNA uptake
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journal, January 2011
- Pinto, A. C.; Melo-Barbosa, H. P.; Miyoshi, A.
- Genetics and Molecular Research, Vol. 10, Issue 3
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