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