Schmidt, Lukas and Werner, Stephan and Kemmer, Thomas and Niebler, Stefan and Kristen, Marco and Ayadi, Lilia and Johe, Patrick and Marchand, Virginie and Schirmeister, Tanja and Motorin, Yuri and Hildebrandt, Andreas and Schmidt, Bertil and Helm, Mark (2019) Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures. Frontiers in Genetics, 10. ISSN 1664-8021
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Abstract
Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide resolution across the mapped transcriptome. Further downstream modules include tools for visualization, machine learning, and modification calling. From the machine-learning module, quality assessment parameters are provided to gauge the suitability of the initial dataset for effective machine learning and modification calling. This output is useful to improve the experimental parameters for library preparation and sequencing. In summary, the automation of the bioinformatics workflow allows a faster turnaround of the optimization cycles in modification calling.
Item Type: | Article |
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Subjects: | Open Research Librarians > Medical Science |
Depositing User: | Unnamed user with email support@open.researchlibrarians.com |
Date Deposited: | 07 Feb 2023 13:06 |
Last Modified: | 02 Jan 2024 13:14 |
URI: | http://stm.e4journal.com/id/eprint/120 |