Unknown

Dataset Information

0

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures.


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.

SUBMITTER: Schmidt L 

PROVIDER: S-EPMC6774277 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

altmetric image

Publications

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures.

Schmidt Lukas L   Werner Stephan S   Kemmer Thomas T   Niebler Stefan S   Kristen Marco M   Ayadi Lilia L   Johe Patrick P   Marchand Virginie V   Schirmeister Tanja T   Motorin Yuri Y   Hildebrandt Andreas A   Schmidt Bertil B   Helm Mark M  

Frontiers in genetics 20190925


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  ...[more]

Similar Datasets

| S-EPMC7144921 | biostudies-literature
2020-09-01 | GSE136411 | GEO
| S-EPMC9287691 | biostudies-literature
| S-EPMC6643048 | biostudies-literature
2022-05-20 | GSE203423 | GEO
| S-EPMC7787692 | biostudies-literature
| S-EPMC5343848 | biostudies-other
2023-06-01 | GSE193400 | GEO
| S-EPMC2745764 | biostudies-literature
| S-EPMC8314522 | biostudies-literature