Transcript mapping based on dRNA-seq data
Please always quote using this URN: urn:nbn:de:bvb:20-opus-116663
- Background: RNA-seq and its variant differential RNA-seq (dRNA-seq) are today routine methods for transcriptome analysis in bacteria. While expression profiling and transcriptional start site prediction are standard tasks today, the problem of identifying transcriptional units in a genome-wide fashion is still not solved for prokaryotic systems. Results: We present RNASEG, an algorithm for the prediction of transcriptional units based on dRNA-seq data. A key feature of the algorithm is that, based on the data, it distinguishes betweenBackground: RNA-seq and its variant differential RNA-seq (dRNA-seq) are today routine methods for transcriptome analysis in bacteria. While expression profiling and transcriptional start site prediction are standard tasks today, the problem of identifying transcriptional units in a genome-wide fashion is still not solved for prokaryotic systems. Results: We present RNASEG, an algorithm for the prediction of transcriptional units based on dRNA-seq data. A key feature of the algorithm is that, based on the data, it distinguishes between transcribed and un-transcribed genomic segments. Furthermore, the program provides many different predictions in a single run, which can be used to infer the significance of transcriptional units in a consensus procedure. We show the performance of our method based on a well-studied dRNA-seq data set for Helicobacter pylori. Conclusions: With our algorithm it is possible to identify operons and 5'- and 3'-UTRs in an automated fashion. This alleviates the need for labour intensive manual inspection and enables large-scale studies in the area of comparative transcriptomics.…
Author: | Thorsten Bischler, Matthias Kopf, Bjoern Voss |
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URN: | urn:nbn:de:bvb:20-opus-116663 |
Document Type: | Journal article |
Faculties: | Medizinische Fakultät / Institut für Molekulare Infektionsbiologie |
Language: | English |
Parent Title (English): | BMC Bioinformatics |
ISSN: | 1471-2105 |
Year of Completion: | 2014 |
Volume: | 15 |
Issue: | 122 |
Source: | BMC Bioinformatics 2014, 15:122 doi:10.1186/1471-2105-15-122 |
DOI: | https://doi.org/10.1186/1471-2105-15-122 |
Pubmed Id: | https://pubmed.ncbi.nlm.nih.gov/24780064 |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Tag: | RNA-seq; differential; dynamic programming; model; reveals; segmentation; transcriptional start site; transcriptional uni; transcriptome |
Release Date: | 2015/08/03 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung |