TandemMod

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TandemMod is a deep learning framework for detecting RNA modifications in nanopore direct RNA sequencing (DRS) data. It offers three modes: de novo training, transfer learning, and prediction. Researchers can train from scratch, fine-tune pre-trained models, or apply existing models for predictions. TandemMod achieves high accuracy and can be used to identify RNA modifications in various species and conditions. It provides a user-friendly solution for studying RNA modifications.

To use TandemMod, you can follow the instructions below. The detailed usage and run examples of the three modes are available in our document.

Environment configuration

The following modules are needed to run TandemMod.

module	version
minimap2	2.17-r941
python	3.7.12
h5py	3.7.0
statsmodels	0.10.0
joblib	0.16.0
scikit-learn	0.22
torch	1.9.1
guppy	6.1.5
tombo	1.5.1
ont_vbz_hdf_plugin	1.0.1
ont-fast5-api	4.1.1
numpy	1.19.5
scipy	1.7.0

You can install dependent modules manually or install them through yaml file. The entire installation will take about 10 minutes.

conda env create -f TandemMod.yaml

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Data preprocessing

Basecalling

Guppy is used for basecalling. Guppy, as well as the now deprecated Albacore and all other basecallers, uses files in fast5 format as input. In addition to basecalling, Guppy also performs filtering of low quality reads, clipping of Oxford Nanopore adapters.

guppy_basecaller -i demo/IVET/IVET_m6A -s demo/IVET/IVET_m6A_guppy --num_callers 40 --recursive --fast5_out --config rna_r9.4.1_70bps_hac.cfg

Multi-fast5 to single_fast5

If fast5 reads are stored at multi-reads format, ont_fast5_api is recommended to convert multi-fast5 reads to single-fast5 reads. Usually, the size of multi-reads fast5 file is about 200-300M. The ont_fast5_api is available on PyPI and can be installed via pip:

multi_to_single_fast5 -i demo/IVET/IVET_m6A_guppy -s demo/IVET/IVET_m6A_guppy_single --recursive

Resquiggle

The re-squiggle algorithm is the basis for the Tombo framework. The re-squiggle algorithm takes as input a read file (in FAST5 format) containing raw signal and associated base calls. The base calls are mapped to a genome or transcriptome reference and then the raw signal is assigned to the reference sequence based on an expected current level model.

tombo resquiggle --overwrite --basecall-group Basecall_1D_001 demo/IVET/IVET_m6A_guppy_single  demo/IVET_reference.fa --processes 40 --fit-global-scale --include-event-stdev

Extract features

minimap2 is used to map basecalled sequences to reference transcripts.

cat demo/IVET/IVET_m6A_guppy/pass/*.fastq >demo/IVET/IVET_m6A.fastq
minimap2 -ax map-ont demo/IVET_reference.fa demo/IVET/IVET_m6A.fastq >demo/IVET/IVET_m6A.sam

Extract signal files and features from resquiggled fast5 files using the following python scripts.

python scripts/extract_signal_from_fast5.py -p 40 --fast5 demo/IVET/IVET_m6A_guppy_single --reference demo/IVET_reference.fa --sam demo/IVET/IVET_m6A.sam --output demo/IVET/m6A.signal.tsv --clip 10
python scripts/extract_feature_from_signal.py  --signal_file demo/IVET/m6A.signal.tsv --clip 10 --output demo/IVET/m6A.feature.tsv --motif DRACH

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Three different modes has been developed: de novo training, transfer learning, and prediction. The mode of de novo training allows users to train the TandemMod model from scratch using their own datasets. In the prediction mode, users can apply a pre-trained or fine-tuned TandemMod model to identify modifications in their dataset. In the transfer learning mode, users can fine-tune a pre-trained TandemMod model using their own data. The execution time for each demonstration is estimated to be approximately 5-10 minutes on a "normal" desktop computer.

Train your own model

python scripts/TandemMod.py --run_mode train \
     --new_model demo/model/m6A.demo.IVET.pkl \
     --train_data_mod demo/IVET/m6A.train.feature.tsv \
     --train_data_unmod demo/IVET/unmod.train.feature.tsv \
     --test_data_mod demo/IVET/m6A.test.feature.tsv \
     --test_data_unmod demo/IVET/unmod.test.feature.tsv \
     --epoch 100

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Predict modifications using pretrained model.

python scripts/TandemMod.py --run_mode predict \
    --pretrained_model demo/model/m6A.demo.IVET.pkl \
    --feature_file demo/HEK293T/HEK293T.feature.tsv \
    --predict_result demo/HEK293T/HEK293T.prediction.tsv

Transfer learning to new modification.

In transfer learning mode, you can used a pretrained model to retrain the bottom layers to identify new modification.

python scripts/TandemMod.py --run_mode transfer \
    --pretrained_model demo/model/m6A.demo.IVET.pkl \
    --new_model demo/model/m7G.demo.ELIGOS.transfered_from_IVET_m6A.pkl \
    --train_data_mod demo/ELIGOS/m7G.train.feature.tsv \
    --train_data_unmod demo/ELIGOS/unmod.train.feature.tsv \
    --test_data_mod demo/ELIGOS/m7G.test.feature.tsv \
    --test_data_unmod demo/ELIGOS/unmod.test.feature.tsv \
     --epoch 100

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Contact

TandemMod is maintained by Yu lab. If you use TandemMod in your research, please cite *************************.