Analysis of Strand-Specific RNA-Seq Data Using Machine Learning Reveals the Structures of Transcription Units in Clostridium thermocellum

Chou, Wen-Chi; Ma, Qin; Yang, Shihui; Cao, Sha; Klingeman, Dawn M.; Brown, Steven D.; Xu, Ying

doi:10.1093/nar/gkv177

Title: Analysis of Strand-Specific RNA-Seq Data Using Machine Learning Reveals the Structures of Transcription Units in Clostridium thermocellum

Journal Article · Thu Mar 12 00:00:00 EDT 2015 · Nucleic Acids Research

DOI:https://doi.org/10.1093/nar/gkv177· OSTI ID:1242033

Chou, Wen-Chi ^[1]; Ma, Qin ^[1]; Yang, Shihui ^[2]; Cao, Sha ^[3]; Klingeman, Dawn M. ^[4]; Brown, Steven D. ^[5]; Xu, Ying ^[6]

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)

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.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). BioEnergy Science Center (BESC)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC36-08GO28308; AC05-00OR22725

OSTI ID:: 1242033

Alternate ID(s):: OSTI ID: 1265796

Report Number(s):: NREL/JA-5100-64668

Journal Information:: Nucleic Acids Research, Vol. 43, Issue 10; Related Information: Nucleic Acids Research; ISSN 0305-1048

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 17 works

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