tfclass-predict 1.1.5a0

tfclass_predict allows to estimate transcription factor bindingsites in the TFClass hierarchy.

TFClassPredict

Description

TFClassPredict predicts transcription factor binding sites (TFBSs) on the human genome accordingly to their DNA-binding domain encapsulated in 23 DBD-Classes defined by the TFClass Class-level. By leveraging the DNABERT model, TFClassPredict reached high performances.

Package Workflow Structure

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Installation

The package can be installed via pip:

pip install tfclass_predict

To use the package the usage of the precompiled data set is recommended as it shortens runtime drastically and can be on basically any system.

TFCP_precompiled

Alternatively, the human genome (v38) and the TFClassPredict (TFCP) model can be used directly.

HG38 from UCSC

TFCP_model

All downloads need to be unzipped so that the path to TFCP_precompiled directory or the path to the hg38.fa file and directory TFCP_model can be passed to the command-line tool or PredictionManager.

Usage

The tool can be used from the command line with the following parameters:

usage: tfclass_predict [-h] [--genome GENOME] [--precompiled PRECOMPILED] [--model MODEL] [--gpus GPUS] [--cpus CPUS] bed_file output_dir

tfclass_predict allows to estimate transcription factor bindingsites in the TFClass hierarchy. Please specifiy either the path to the precompiled (--precompiled) files or to the published
model (--model + --genome). If both are defined, the precompiled dataset is preferred!

positional arguments:
  bed_file              Path to bed file of ATAC-seq or other NGS experiment.
  output_dir            Path to output directory.

options:
  -h, --help            show this help message and exit
  --genome GENOME       Path to human genome reference (rec.: hg38) (.fa).
  --precompiled PRECOMPILED
                        Path to precompiled hg38 predictions (unzipped folder).
  --model MODEL         Path to TFClass model archive (unzipped folder).
  --gpus GPUS           Number of GPUs that should be used in parallel.
  --cpus CPUS           Number of CPUs that should be used at maximum in parallel. 

Or directly in python scripts:

from tfclass_predict import PredictionManager

bed_file = 'path_to_example/bed.bed'  # smaller bed file for testing
genome_file = "hg38.fa"
tfclass_model = "model/TFCP_model" #see Installation
tfclass_precompiled = "model/TFCP_precompiled" #see Installation
res_dir = "res"

# using the precompiled data set(recommended)
pred_manager = PredictionManager(bed_file, res_dir, precompiled=precompiled)
pred_manager.predict()
pred_manager.save_results()

# using genome and model
pred_manager = PredictionManager(bed_file, res_dir, genome=genome_file, tfcp_model=tfclass_model)
pred_manager.predict()
pred_manager.save_results()

In case the TFCP_model is used, it runs by default a mirrored strategy so that predictions are done in parallel on several GPUs if --gpus > 1. --cpus can be defined to use more than one core for parallel tokenization.

Further Documentation

Find more information about the API at ReadTheDocs.
(Currently outdated! - 26/05/21)