Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures.
Fiche publication
Date publication
janvier 2019
Journal
Frontiers in genetics
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MOTORINE Iouri, Dr MARCHAND Virginie
Tous les auteurs :
Schmidt L, Werner S, Kemmer T, Niebler S, Kristen M, Ayadi L, Johe P, Marchand V, Schirmeister T, Motorin Y, Hildebrandt A, Schmidt B, Helm M
Lien Pubmed
Résumé
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.
Mots clés
Galaxy platform, RNA modifications, RT signature, Watson–Crick face, m1A, machine learning
Référence
Front Genet. 2019 ;10:876