Nm-Nano

Nm-Nano: A framework for predicting 2´-O-Methylation (Nm) Sites in Nanopore RNA Sequencing Data

Getting Started and pre-requisites

The following softwares and modules should be installed before using Nm-Nano

python 3.6.10

minimpa2 (https://github.com/lh3/minimap2)

Nanopolish (https://github.com/jts/nanopolish)

samtools (http://www.htslib.org/)

numpy 1.18.1

pandas 1.0.1

sklearn 0.22.2.post1

tensorflow 2.0.0

keras 2.3.1 (using Tensorflow backend)

Running Nm-Nano:

In order to run Nm-Nano, the user has to do the following:

1- Ensure that BED file that highlights the Nm modified locations on the whole genome is in the same path where main.py file exists:

2- Run the following python command:

python main.py -r ref.fa -f reads.fastq

Where the Nm-Nano framework needs the following two inputs files when running it:

• A reference Genome file (ref.fa)
• The fastq reads file (reads.fastq)

Note:

• The user should enter the BED file name with the absolute path and extension

• The user should include the fast5 files folder (fast5_files) from which reads.fastq file was generated in the same path of main.py

• The default model used in Nm-Nano framework is the Xgboost model implemented in xgboost_test_split.py. However, the user can test the Random Forest (RF) with embedding model implemented in RF_embedding_test_split.py instead of xgboost by entering RF model file name with its extension when he/she is prompted to enter it while running main.py