deepSRAMP

SRAMP is a popular mammalian m6A site predictor we previously developed (Nucleic Acids Res 2016). SRAMP has been totally cited by more than 570 papers (Google Scholar, 4-16, 2024) and represents the mostly used algorithm in this field. A large number of m6A sites were identified by the helps of SRAMP.

After ~8 years after its development, Now we released deepSRAMP (www.cuilab.cn/deepsramp) , which is designed based on a combined framework of transformer neural network and recurrent neural network by fusing the sequence and genomic position features. The results showed that SRAMP2 greatly outperforms its predecessor SRAMP with 15.0% increase of AUROC and 30.9% increase of AUPRC, and greatly outperforms other state-of-the-art m6A predictors (WHISTLE and DeepPromise) with average 16.1% and 18.3% increase of AUC and 43.9% and 46.4% increase of AUPRC, respectively.

Requirements

• torch
• pandas
• scikit-learn
• joblib
• tqdm
• matplotlib
• seaborn
• shap

Installation

1. Install conda and create a virtual enviroument named sramp with python installed.

conda create -y -n sramp python 
conda activate sramp

2. Clone this repo.

git clone https://github.com/zhfanrui/deepSRAMP.git

3. Install this package and dependencies.

cd deepSRAMP
pip install .

Usage

Inference

1. Download BLAST and Muscle.

sh setup_inference.sh

2. Prepare your query fasta and run

deepsramp predict \
--fasta /path/to/fasta.fa \
--db /path/to/database/hg38_mature \
--blast /path/to/blast/bin \
--model /path/to/model/full_400_mature.model \
--out ./result.csv

Train

1. Download GTF and FASTA files for training;

sh download.sh

2. See tutorials for training.

Pretrained Models

Model names follow the rule of {mode}_{half_window_size}_{target}_{extra}.model.

Model Name	Model Mode	Half Window Size	Window Size	Target	Extra
Main Models
full_400_ythdf.model	Full Model	400	801	YTHDF1/2	-
full_400_mature.model	Full Model	400	801	Mature transcripts	-
full_400_full.model	Full Model	400	801	Full transcripts	-
Other Model Examples
seqonly_400_mature.model	Sequence Features Only Model	400	801	Mature transcripts	-
genomeonly_400_mature.model	Genome Features Only Model	400	801	Mature transcripts	-
full_100_mature.model	Full Model	100	201	Mature transcripts	-
full_600_mature.model	Full Model	600	1201	Mature transcripts	-
full_400_mature_for_time.model	Full Model	400	801	Mature transcripts	For training time estimation
Single Model Examples
full_400_a549_single.model	Full Model	400	801	A549	deepSRAMP$_{single}$
full_400_a549_dp.model	-	500	1001	A549	DeepPromise
full_400_a549_single_random.model	Full Model	500	1001	A549	Selecting random transcript for training instead of max length trascript

Tutorials

The reproduction of figures in the paper can be found in ipynb files.

• Figure 1
• Figure 2
• Figure 3

Citation

[paper]

Performance

Cross Validation

Test on YTHDF1/2

Test on m6Aatlas