Published June 30, 2023
| Version 1.0.0
Software
Open
NanopoReaTA - Nanopore Real-Time Transcriptional Analysis Tool
Creators
- 1. Institute of Human Genetics, University Medical Center Mainz, Anselm-Franz-von-Bentzel-Weg 3, 55128 Mainz, Germany
- 2. Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, 55128 Mainz, Germany
- 3. Institute of Human Genetics, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
Description
NanopoReaTA is an R shiny application that integrates both preprocessing and downstream analysis pipelines for RNA sequencing data from Oxford Nanopore Technologies (ONT) into a user-friendly interface. NanopoReaTA focuses on the analysis of (direct) cDNA and RNA-sequencing (cDNA, DRS) reads and guides you through the different steps up to final visualizations of results from i.e. differential expression or gene body coverage. Furthermore, NanopoReaTA can be run in real-time right after starting a run via MinKNOW, the sequencing application of ONT.
Files
NanopoReaTA-master.zip
Files
(8.3 MB)
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md5:906e756bf8e896cdc78486241c56e2a1
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Additional details
Related works
- Is published in
- 10.1101/2022.12.13.520220 (DOI)
References
- Anders, S., Reyes, A., & Huber, W. (2012). Detecting differential usage of exons from RNA-seq data. Genome Research, 22(10), 2008–2017. https://doi.org/10.1101/GR.133744.111
- Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34(18), 3094–3100. https://doi.org/10.1093/BIOINFORMATICS/BTY191
- Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923–930. https://doi.org/10.1093/BIOINFORMATICS/BTT656
- Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 1–21. https://doi.org/10.1186/S13059-014-0550-8/FIGURES/9
- Nowicka M, Robinson MD (2016). "DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics [version 2; referees: 2 approved]." F1000Research, 5(1356). doi: 10.12688/f1000research.8900.2, https://f1000research.com/articles/5-1356/v2.
- Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 2017 14:4, 14(4), 417–419. https://doi.org/10.1038/nmeth.4197
- Wang, L., Wang, S., & Li, W. (2012). RSeQC: quality control of RNA-seq experiments. Bioinformatics, 28(16), 2184–2185. https://doi.org/10.1093/BIOINFORMATICS/BTS356