Schmal, M., Girod, C., Yaver, D., Mach, R., & Mach-Aigner, A. (2022). A bioinformatic-assisted workflow for genome-wide identification of ncRNAs. NAR Genomics and Bioinformatics, 4(3), lqac059. https://doi.org/10.1093/nargab/lqac059
E166-05-1 - Forschungsgruppe Synthetische Biologie und Molekulare Biotechnologie
NAR Genomics and Bioinformatics
Oxford University Press
ncRNA; next-generation sequencing
With the upcoming of affordable Next-Generation Sequencing technologies, the number of known non-protein coding RNAs increased drastically in recent years. Different types of non-coding RNAs (ncRNAs) emerged as key players in the regulation of gene expression on the RNA-RNA, RNA-DNA as well as RNA-protein level, ranging from involvement in chromatin remodeling and transcription regulation to post-transcriptional modifications. Prediction of ncRNAs involves the use of several bioinformatics tools and can be a daunting task for researchers. This led to the development of analysis pipelines such as UClncR and lncpipe. However, these pipelines are limited to datasets from human, mouse, zebrafish or fruit fly and are not able to analyze RNA sequencing data from other organisms. In this study, we developed the analysis pipeline Pinc (Pipeline for prediction of ncRNA) as an enhanced tool to predict ncRNAs based on sequencing data by removing transcripts that show protein-coding potential. Additionally, a feature for differential expression analysis of annotated genes as well as for identification of novel ncRNAs is implemented. Pinc uses Nextflow as a framework and is built with robust and well-established analysis tools. This will allow researchers to utilize sequencing data from every organism in order to reliably identify ncRNAs.
Christian Doppler Labor für optimierte Expression von Kohlenhydrat-aktiven Enzymen: CAZy (CDG Christian Doppler Forschungsgesellschaft)