miRBench 1.0.0

A collection of datasets and predictors for benchmarking miRNA target site prediction algorithms

miRNA target site prediction Benchmarks

Installation

miRBench package can be easily installed using pip:

pip install miRBench

Default installation allows access to the datasets. To use predictors and encoders, you need to install additional dependencies.

Dependencies for predictors and encoders

To use miRBench with predictors and encoders, install the following dependencies:

• numpy
• biopython
• viennarna
• torch
• tensorflow
• typing-extensions

To install the miRBench package with all dependencies into a virtual environment, you can use the following commands:

python3.8 -m venv mirbench_venv
source mirbench_venv/bin/activate
pip install miRBench
pip install numpy==1.24.3 biopython==1.83 viennarna==2.7.0 torch==1.9.0 tensorflow==2.13.1 typing-extensions==4.5.0

Note: This instalation is for running predictors on the CPU. If you want to use GPU, you need to install version of torch and tensorflow with GPU support.

Examples

Get all available datasets

from miRBench.dataset import list_datasets

list_datasets()

['AGO2_CLASH_Hejret2023',
 'AGO2_eCLIP_Klimentova2022',
 'AGO2_eCLIP_Manakov2022']

Not all datasets are available with all splits and ratios. To get available splits and ratios, use the full option.

list_datasets(full=True)

{'AGO2_CLASH_Hejret2023': {'splits': {
      'train': {'ratios': ['10']},
      'test': {'ratios': ['1', '10', '100']}}},
 'AGO2_eCLIP_Klimentova2022': {'splits': {
      'test': {'ratios': ['1', '10', '100']}}},
 'AGO2_eCLIP_Manakov2022': {'splits': {
      'train': {'ratios': ['1', '10', '100']},
      'test': {'ratios': ['1', '10', '100']}}}
}

Get dataset

from miRBench.dataset import get_dataset_df

dataset_name = "AGO2_CLASH_Hejret2023"
df = get_dataset_df(dataset_name, split="test", ratio="1")
df.head()

	noncodingRNA	gene	label
0	TCCGAGCCTGGGTCTCCCTCTT	GGGTTTAGGGAAGGAGGTTCGGAGACAGGGAGCCAAGGCCTCTGTC...	1
1	TGCGGGGCTAGGGCTAACAGCA	GCTTCCCAAGTTAGGTTAGTGATGTGAAATGCTCCTGTCCCTGGCC...	1
2	CCCACTGCCCCAGGTGCTGCTGG	TCTTTCCAAAATTGTCCAGCAGCTTGAATGAGGCAGTGACAATTCT...	1
3	TGAGGGGCAGAGAGCGAGACTTT	CAGAACTGGGATTCAAGCGAGGTCTGGCCCCTCAGTCTGTGGCTTT...	1
4	CAAAGTGCTGTTCGTGCAGGTAG	TTTTTTCCCTTAGGACTCTGCACTTTATAGAATGTTGTAAAACAGA...	1

If you want to get just a path to the dataset, use the get_dataset_path function:

from miRBench.dataset import get_dataset_path

dataset_path = get_dataset_path(dataset_name, split="test", ratio="1")
dataset_path

/home/user/.miRBench/datasets/13909173/AGO2_CLASH_Hejret2023/1/test/dataset.tsv

Get all available tools

from miRBench.predictor import list_predictors

list_predictors()

['CnnMirTarget_Zheng2020',
 'RNACofold',
 'miRNA_CNN_Hejret2023',
 'miRBind_Klimentova2022',
 'TargetNet_Min2021',
 'Seed8mer',
 'Seed7mer',
 'Seed6mer',
 'Seed6merBulgeOrMismatch',
 'TargetScanCnn_McGeary2019',
 'InteractionAwareModel_Yang2024']

Encode dataset

from miRBench.encoder import get_encoder

tool = 'miRBind_Klimentova2022'
encoder = get_encoder(tool)

input = encoder(df)

Get predictions

from miRBench.predictor import get_predictor

predictor = get_predictor(tool)

predictions = predictor(input)
predictions[:10]

array([0.6899161 , 0.15220629, 0.07301956, 0.43757868, 0.34360734,
       0.20519172, 0.0955029 , 0.79298246, 0.14150576, 0.05329492],
      dtype=float32)