Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets
Abstract Background Methods to read out naturally occurring or experimentally introduced nucleic acid modifications are emerging as powerful tools to study dynamic cellular processes. The recovery, quantification and interpretation of such events in high-throughput sequencing datasets demands specia...
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doaj-39292a6c04f248eeb835ced54223a3592020-11-25T03:33:18ZengBMCBMC Bioinformatics1471-21052019-05-0120111610.1186/s12859-019-2849-7Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasetsTobias Neumann0Veronika A. Herzog1Matthias Muhar2Arndt von Haeseler3Johannes Zuber4Stefan L. Ameres5Philipp Rescheneder6Research Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna BioCenter (VBC)Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA)Research Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna BioCenter (VBC)Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of ViennaResearch Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna BioCenter (VBC)Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA)Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of ViennaAbstract Background Methods to read out naturally occurring or experimentally introduced nucleic acid modifications are emerging as powerful tools to study dynamic cellular processes. The recovery, quantification and interpretation of such events in high-throughput sequencing datasets demands specialized bioinformatics approaches. Results Here, we present Digital Unmasking of Nucleotide conversions in K-mers (DUNK), a data analysis pipeline enabling the quantification of nucleotide conversions in high-throughput sequencing datasets. We demonstrate using experimentally generated and simulated datasets that DUNK allows constant mapping rates irrespective of nucleotide-conversion rates, promotes the recovery of multimapping reads and employs Single Nucleotide Polymorphism (SNP) masking to uncouple true SNPs from nucleotide conversions to facilitate a robust and sensitive quantification of nucleotide-conversions. As a first application, we implement this strategy as SLAM-DUNK for the analysis of SLAMseq profiles, in which 4-thiouridine-labeled transcripts are detected based on T > C conversions. SLAM-DUNK provides both raw counts of nucleotide-conversion containing reads as well as a base-content and read coverage normalized approach for estimating the fractions of labeled transcripts as readout. Conclusion Beyond providing a readily accessible tool for analyzing SLAMseq and related time-resolved RNA sequencing methods (TimeLapse-seq, TUC-seq), DUNK establishes a broadly applicable strategy for quantifying nucleotide conversions.http://link.springer.com/article/10.1186/s12859-019-2849-7MappingEpitranscriptomicsNext generation sequencingHigh-throughput sequencing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tobias Neumann Veronika A. Herzog Matthias Muhar Arndt von Haeseler Johannes Zuber Stefan L. Ameres Philipp Rescheneder |
spellingShingle |
Tobias Neumann Veronika A. Herzog Matthias Muhar Arndt von Haeseler Johannes Zuber Stefan L. Ameres Philipp Rescheneder Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets BMC Bioinformatics Mapping Epitranscriptomics Next generation sequencing High-throughput sequencing |
author_facet |
Tobias Neumann Veronika A. Herzog Matthias Muhar Arndt von Haeseler Johannes Zuber Stefan L. Ameres Philipp Rescheneder |
author_sort |
Tobias Neumann |
title |
Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets |
title_short |
Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets |
title_full |
Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets |
title_fullStr |
Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets |
title_full_unstemmed |
Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets |
title_sort |
quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2019-05-01 |
description |
Abstract Background Methods to read out naturally occurring or experimentally introduced nucleic acid modifications are emerging as powerful tools to study dynamic cellular processes. The recovery, quantification and interpretation of such events in high-throughput sequencing datasets demands specialized bioinformatics approaches. Results Here, we present Digital Unmasking of Nucleotide conversions in K-mers (DUNK), a data analysis pipeline enabling the quantification of nucleotide conversions in high-throughput sequencing datasets. We demonstrate using experimentally generated and simulated datasets that DUNK allows constant mapping rates irrespective of nucleotide-conversion rates, promotes the recovery of multimapping reads and employs Single Nucleotide Polymorphism (SNP) masking to uncouple true SNPs from nucleotide conversions to facilitate a robust and sensitive quantification of nucleotide-conversions. As a first application, we implement this strategy as SLAM-DUNK for the analysis of SLAMseq profiles, in which 4-thiouridine-labeled transcripts are detected based on T > C conversions. SLAM-DUNK provides both raw counts of nucleotide-conversion containing reads as well as a base-content and read coverage normalized approach for estimating the fractions of labeled transcripts as readout. Conclusion Beyond providing a readily accessible tool for analyzing SLAMseq and related time-resolved RNA sequencing methods (TimeLapse-seq, TUC-seq), DUNK establishes a broadly applicable strategy for quantifying nucleotide conversions. |
topic |
Mapping Epitranscriptomics Next generation sequencing High-throughput sequencing |
url |
http://link.springer.com/article/10.1186/s12859-019-2849-7 |
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