Dataset Information

Transcript mapping based on dRNA-seq data.

ABSTRACT: 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 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.

SUBMITTER: Bischler T

PROVIDER: S-EPMC4016656 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transcript mapping based on dRNA-seq data.

Bischler Thorsten T Kopf Matthias M Voß Björn B

BMC bioinformatics 20140429

<h4>Background</h4>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.<h4>Results</h4>We present RNAseg, an algorithm for the prediction of transcriptional units based on dRNA-seq data. A key feature of the algorithm ...[more]

PMID: 24780064

Similar Datasets

Project description:BACKGROUND: Differential RNA sequencing (dRNA-seq) is a high-throughput screening technique designed to examine the architecture of bacterial operons in general and the precise position of transcription start sites (TSS) in particular. Hitherto, dRNA-seq data were analyzed by visualizing the sequencing reads mapped to the reference genome and manually annotating reliable positions. This is very labor intensive and, due to the subjectivity, biased. RESULTS: Here, we present TSSAR, a tool for automated de novo TSS annotation from dRNA-seq data that respects the statistics of dRNA-seq libraries. TSSAR uses the premise that the number of sequencing reads starting at a certain genomic position within a transcriptional active region follows a Poisson distribution with a parameter that depends on the local strength of expression. The differences of two dRNA-seq library counts thus follow a Skellam distribution. This provides a statistical basis to identify significantly enriched primary transcripts.We assessed the performance by analyzing a publicly available dRNA-seq data set using TSSAR and two simple approaches that utilize user-defined score cutoffs. We evaluated the power of reproducing the manual TSS annotation. Furthermore, the same data set was used to reproduce 74 experimentally validated TSS in H. pylori from reliable techniques such as RACE or primer extension. Both analyses showed that TSSAR outperforms the static cutoff-dependent approaches. CONCLUSIONS: Having an automated and efficient tool for analyzing dRNA-seq data facilitates the use of the dRNA-seq technique and promotes its application to more sophisticated analysis. For instance, monitoring the plasticity and dynamics of the transcriptomal architecture triggered by different stimuli and growth conditions becomes possible.The main asset of a novel tool for dRNA-seq analysis that reaches out to a broad user community is usability. As such, we provide TSSAR both as intuitive RESTful Web service ( http://rna.tbi.univie.ac.at/TSSAR) together with a set of post-processing and analysis tools, as well as a stand-alone version for use in high-throughput dRNA-seq data analysis pipelines.

Project description:The most prominent RNA modification - N6-methyladenosine (m6A) - affects gene regulation and cancer progression. The extent and effect of m6A on long non-coding RNAs (lncRNAs) is, however, still not clear. The most established method for m6A detection is methylated RNA immunoprecipitation and sequencing (MeRIP-seq). However, Oxford Nanopore Technologies recently developed direct RNA-seq (dRNA-seq) method, allowing m6A identification at higher resolution and in its native form. We performed whole transcriptome sequencing of the glioblastoma cell line U87-MG with both MeRIP-seq and dRNA-seq. For MeRIP-seq, m6A peaks were identified using nf-core/chipseq, and for dRNA-seq - EpiNano pipeline. MeRIP-seq analysis revealed 5086 lncRNAs transcripts, while dRNA-seq identified 336 lncRNAs transcripts from which 556 and 198 were found to be m6A modified, respectively. While 24 lncRNAs with m6A overlapped between two methods. Gliovis database analysis revealed that the expression of the major part of identified overlapping lncRNAs was associated with glioma grade or patient survival prognosis. We found that the frequency of m6A occurrence in lncRNAs varied more than 9-fold throughout the provided list of 24 modified lncRNAs. The highest m6A frequency was detected in MIR1915HG, THAP9-AS1, MALAT1, NORAD1, and NEAT1 (49-88nt), while MIR99AHG, SNHG3, LOXL1-AS1, ILF3-DT showed the lowest m6A frequency (445-261nt). Taken together, (1) we provide a high accuracy list of 24 m6A modified lncRNAs of U87-MG cells; (2) we conclude that MeRIP-seq is more suitable for an initial m6A screening study, due to its higher lncRNA coverage, whereas dRNA-seq is most useful when more in-depth analysis of m6A quantity and precise location is of interest.Abbreviations: (dRNA-seq) direct RNA-seq, (GBM) glioblastoma, (LGG) low-grade glioma, (lncRNAs) long non-coding RNAs, (m6A) N6-methyladenosine, (MeRIP-seq) methylated RNA immunoprecipitation and sequencing, (ncRNA) non-coding RNA, (ONT) Oxford Nanopore Technologi; Lietuvos Mokslo Taryba.

Dataset Information

Transcript mapping based on dRNA-seq data.

Publications

Transcript mapping based on dRNA-seq data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets