Dataset Information

Assessing the hodgepodge of non-mapped reads in bacterial transcriptomes: real or artifactual RNA chimeras?

ABSTRACT:

Background

RNA sequencing methods have already altered our view of the extent and complexity of bacterial and eukaryotic transcriptomes, revealing rare transcript isoforms (circular RNAs, RNA chimeras) that could play an important role in their biology.

Results

We performed an analysis of chimera formation by four different computational approaches, including a custom designed pipeline, to study the transcriptomes of M. pneumoniae and P. aeruginosa, as well as mixtures of both. We found that rare transcript isoforms detected by conventional pipelines of analysis could be artifacts of the experimental procedure used in the library preparation, and that they are protocol-dependent.

Conclusion

By using a customized pipeline we show that optimal library preparation protocol and the pipeline to analyze the results are crucial to identify real chimeric RNAs.

SUBMITTER: Llorens-Rico V

PROVIDER: S-EPMC4122791 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Publications

Assessing the hodgepodge of non-mapped reads in bacterial transcriptomes: real or artifactual RNA chimeras?

Lloréns-Rico Verónica V Serrano Luis L Lluch-Senar Maria M

BMC genomics 20140729

<h4>Background</h4>RNA sequencing methods have already altered our view of the extent and complexity of bacterial and eukaryotic transcriptomes, revealing rare transcript isoforms (circular RNAs, RNA chimeras) that could play an important role in their biology.<h4>Results</h4>We performed an analysis of chimera formation by four different computational approaches, including a custom designed pipeline, to study the transcriptomes of M. pneumoniae and P. aeruginosa, as well as mixtures of both. We ...[more]

PMID: 25070459

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Project description:BackgroundRNA sequencing (RNA-seq) is a powerful tool for genome-wide expression profiling of biological samples with the advantage of high-throughput and high resolution. There are many existing algorithms nowadays for quantifying expression levels and detecting differential gene expression, but none of them takes the misaligned reads that are mapped to non-exonic regions into account. We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.ResultsWe implemented our novel XBSeq algorithm and evaluated it by using a set of simulated expression datasets under different conditions, using a combination of negative binomial and Poisson distributions with parameters derived from real RNA-seq data. We compared the performance of our method with other commonly used differential expression analysis algorithms. We also evaluated the changes in true and false positive rates with variations in biological replicates, differential fold changes, and expression levels in non-exonic regions. We also tested the algorithm on a set of real RNA-seq data where the common and different detection results from different algorithms were reported.ConclusionsIn this paper, we proposed a novel XBSeq, a differential expression analysis algorithm for RNA-seq data that takes non-exonic mapped reads into consideration. When background noise is at baseline level, the performance of XBSeq and DESeq are mostly equivalent. However, our method surpasses DESeq and other algorithms with the increase of non-exonic mapped reads. Only in very low read count condition XBSeq had a slightly higher false discovery rate, which may be improved by adjusting the background noise effect in this situation. Taken together, by considering non-exonic mapped reads, XBSeq can provide accurate expression measurement and thus detect differential expressed genes even in noisy conditions.

Dataset Information

Assessing the hodgepodge of non-mapped reads in bacterial transcriptomes: real or artifactual RNA chimeras?

Background

Results

Conclusion

Publications

Assessing the hodgepodge of non-mapped reads in bacterial transcriptomes: real or artifactual RNA chimeras?

Similar Datasets

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