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SampComp (version 1.0.0rc3.post2+galaxy1)
First condition files
First condition files 0
Second condition files
Second condition files 0
Advanced parameters
Advanced parameters 0

What it does

Nanocompore identifies differences in ONT nanopore sequencing raw signal corresponding to RNA modifications by comparing 2 samples

Nanocompore compares 2 ONT nanopore direct RNA sequencing datasets from different experimental conditions expected to have a significant impact on RNA modifications. It is recommended to have at least 2 replicates per condition. For example one can use a control condition with a significantly reduced number of modifications such as a cell line for which a modification writing enzyme was knocked-down or knocked-out. Alternatively, on a smaller scale transcripts of interests could be synthesized in-vitro.

SampComp provides a very flexible analysis framework with a few mandatory options and many optional parameters.

First, SampComp parses the sample eventalign collapse files and then the observed results are piled-up per reference at position level. In a second time, positions are compared using various statistical methods and the statistics are stored in a shelve DBM database containing the results for all positions with sufficient coverage.


SampComp requires sample files obtained with NanopolishComp EventalignCollapse as explained before (see data preparation) for both the control and the experimental conditions. 2 conditions are expected and at least 2 replicates per conditions are highly recommended.

A transcriptome FASTA reference file is required to extract kmer sequences during the analyses. The reference has to be the same as the one used at the mapping step.

Optionally, a BED file containing the genome annotations corresponding to the transcriptome fasta file can be provided. In that case Nanocompore will also convert the transcript coordinates into the genome space.


The database object returned by Sampcomp is a Python GDBM object database indexed by reference id and can be be used with SampCompDB.


More information are available on the project website and github.