Welcome to NanoCount v1.0.0.post6 documentation

NanoCount

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NanoCount estimates transcripts abundance from Oxford Nanopore direct-RNA sequencing datasets, using an expectation-maximization approach like RSEM, Kallisto, salmon, etc to handle the uncertainty of multi-mapping reads

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Quick start

Align reads

Reads should be aligned to a transcriptome reference using minimap2. We recommend using the -N 10 option to retain at least 10 secondary mappings.

minimap2 -t 4 -ax map-ont -p 0 -N 10 transcriptome.fa.gz reads.fastq.gz | samtools view -bh > aligned_reads.bam

Estimate transcripts abundance with NanoCount

NanoCount -i aligned_reads.bam -o transcript_counts.tsv

Detailed instructions

• Installation
• Command line Usage
• Python API Usage
• Input / Output formats

citation

The repository is archived at Zenodo. If you use NanoCount please cite as follow:

Josie Gleeson, Adrien Leger, Yair D J Prawer, Tracy A Lane, Paul J Harrison, Wilfried Haerty, Michael B Clark, Accurate expression quantification from nanopore direct RNA sequencing with NanoCount, Nucleic Acids Research, 2021;, gkab1129, https://doi.org/10.1093/nar/gkab1129

licence

MIT (https://mit-license.org/)

Copyright © 2020 Adrien Leger

Authors

• Adrien Leger / aleg@ebi.ac.uk / https://adrienleger.com

The package was inspired from https://github.com/jts/nanopore-rna-analysis by Jared Simpson

• Jared Simpson (@jts)