Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets.

Autor: Neumann T; Research Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna BioCenter (VBC), 1030, Vienna, Austria. tobias.neumann@imp.ac.at., Herzog VA; Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr. Bohr-Gasse 3, VBC, 1030, Vienna, Austria., Muhar M; Research Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna BioCenter (VBC), 1030, Vienna, Austria., von Haeseler A; Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr. Bohrgasse 9, VBC, 1030, Vienna, Austria.; Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Waehringerstrasse 17, A-1090, Vienna, Austria., Zuber J; Research Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna BioCenter (VBC), 1030, Vienna, Austria.; Medical University of Vienna, VBC, 1030, Vienna, Austria., Ameres SL; Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr. Bohr-Gasse 3, VBC, 1030, Vienna, Austria., Rescheneder P; Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr. Bohrgasse 9, VBC, 1030, Vienna, Austria. philipp.rescheneder@gmail.com.
Jazyk: angličtina
Zdroj: BMC bioinformatics [BMC Bioinformatics] 2019 May 20; Vol. 20 (1), pp. 258. Date of Electronic Publication: 2019 May 20.
DOI: 10.1186/s12859-019-2849-7
Abstrakt: 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.
Databáze: MEDLINE
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