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        "author": "Antonio Camargo, Vsevolod Sourkov",
        "author_email": "antoniop.camargo@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License (GPL)",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Software Development :: Libraries"
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        "description": "<p align=\"center\"><a href=\"https://rnasamba.lge.ibi.unicamp.br/\"><img src=\"https://raw.githubusercontent.com/apcamargo/RNAsamba/master/logo.png\" width=\"350rem\"></a></p>\n\n[![DOI](https://img.shields.io/badge/DOI-10.1093%2Fnargab%2Flqz024-red.svg)](https://doi.org/10.1093/nargab/lqz024)\n[![PyPI](https://img.shields.io/pypi/v/rnasamba.svg?label=PyPI&color=green)](https://pypi.python.org/pypi/rnasamba)\n[![Conda](https://img.shields.io/conda/vn/bioconda/rnasamba.svg?label=Conda&color=green)](https://anaconda.org/bioconda/rnasamba)\n[![PyPI downloads](https://img.shields.io/pypi/dm/rnasamba?label=PyPI%20downloads&color=blue)](https://pypi.python.org/pypi/rnasamba)\n[![Conda downloads](https://img.shields.io/conda/dn/bioconda/rnasamba.svg?label=Conda%20downloads&color=blue)](https://anaconda.org/bioconda/rnasamba)\n[![Docker pulls](https://img.shields.io/docker/pulls/antoniopcamargo/rnasamba.svg?label=Docker%20pulls&color=blue)](https://hub.docker.com/r/antoniopcamargo/rnasamba)\n\n\n- [Overview](#overview)\n- [Citation](#citation)\n- [Documentation](#documentation)\n- [Installation](#installation)\n- [Download the pre-trained models](#download-the-pre-trained-models)\n- [Usage](#usage)\n  - [`rnasamba train`](#rnasamba-train)\n  - [`rnasamba classify`](#rnasamba-classify)\n- [Examples](#examples)\n- [Using the Docker image](#using-the-docker-image)\n\n## Overview\n\nRNAsamba is a tool for computing the coding potential of RNA sequences using a neural network classification model. A description of the algorithm and benchmarks comparing RNAsamba to other tools can be found in our [article](#citation).\n\n## Citation\n\nIf you use RNAsamba in your work, please cite our paper:\n\n> Camargo, A. P., Sourkov, V., Pereira, G. A. G. & Carazzolle, M. F.. \"[RNAsamba: neural network-based assessment of the protein-coding potential of RNA sequences](https://academic.oup.com/nargab/article/2/1/lqz024/5701461)\" *NAR Genomics and Bioinformatics* **2**, lqz024 (2020).\n\n## Web version\n\nRNAsamba can be used through a minimal web interface that is freely available online at [https://rnasamba.lge.ibi.unicamp.br/](https://rnasamba.lge.ibi.unicamp.br/). The source code of the web app can be found at [https://github.com/apcamargo/rnasamba-webapp/](https://github.com/apcamargo/rnasamba-webapp/).\n\n## Documentation\n\nA complete documentation for RNAsamba can be found at [https://apcamargo.github.io/RNAsamba/](https://apcamargo.github.io/RNAsamba/).\n\n## Installation\n\nThere are two ways to install RNAsamba:\n\n- Using pip:\n\n```\npip install rnasamba\n```\n\n- Using conda:\n\n```\nconda install -c conda-forge -c bioconda rnasamba\n```\n\n## Download the pre-trained models\n\nWe provide two HDF5 files containing the weights of classification models trained with human trancript sequences. The first model (`full_length_weights.hdf5`) was trained exclusively with full-length transcripts and can be used in datasets comprised mostly or exclusively of complete transcript sequences. The second model (`partial_length_weights.hdf5`) was trained with both complete and truncated transcripts and is prefered in cases where there is a significant fraction of partial-length sequences, such as transcriptomes assembled using *de novo* approaches.\n\nBoth models achieves high classification performance in transcripts from a variety of different species (see [reference](#citation)).\n\nYou can download the files by executing the following commands:\n\n```\ncurl -O https://raw.githubusercontent.com/apcamargo/RNAsamba/master/data/full_length_weights.hdf5\ncurl -O https://raw.githubusercontent.com/apcamargo/RNAsamba/master/data/partial_length_weights.hdf5\n```\n\nIn case you want to train your own model, you can follow the steps shown in the [Examples](#examples) section.\n\n## Usage\n\nRNAsamba provides two commands: `rnasamba train` and `rnasamba classify`.\n\n### `rnasamba train`\n\n`rnasamba train` is the command for training a new classification model from a training dataset and saving the network weights into an HDF5 file. The user can specify the batch size (`--batch_size`) and the number of training epochs (`--epochs`). The user can also choose to activate early stopping (`--early_stopping`), which reduces training time and can help avoiding overfitting.\n\n```\nusage: rnasamba train [-h] [-s EARLY_STOPPING] [-b BATCH_SIZE] [-e EPOCHS]\n                      [-v {0,1,2,3}]\n                      output_file coding_file noncoding_file\n\nTrain a new classification model.\n\npositional arguments:\n  output_file           output HDF5 file containing weights of the newly\n                        trained RNAsamba network.\n  coding_file           input FASTA file containing sequences of protein-\n                        coding transcripts.\n  noncoding_file        input FASTA file containing sequences of noncoding\n                        transcripts.\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --version             show program's version number and exit\n  -s EARLY_STOPPING, --early_stopping EARLY_STOPPING\n                        number of epochs after lowest validation loss before\n                        stopping training (a fraction of 0.1 of the training\n                        set is set apart for validation and the model with the\n                        lowest validation loss will be saved). (default: 0)\n  -b BATCH_SIZE, --batch_size BATCH_SIZE\n                        number of samples per gradient update. (default: 128)\n  -e EPOCHS, --epochs EPOCHS\n                        number of epochs to train the model. (default: 40)\n  -v {0,1,2,3}, --verbose {0,1,2,3}\n                        print the progress of the training. 0 = silent, 1 =\n                        current step, 2 = progress bar, 3 = one line per\n                        epoch. (default: 0)\n```\n\n### `rnasamba classify`\n\n`rnasamba classify` is the command for computing the coding potential of transcripts contained in an input FASTA file and classifying them into coding or non-coding. Optionally, the user can specify an output FASTA file (`--protein_fasta`) in which RNAsamba will write the translated sequences of the predicted coding ORFs. If multiple weight files are provided, RNAsamba will ensemble their predictions into a single output.\n\n```\nusage: rnasamba classify [-h] [-p PROTEIN_FASTA] [-v {0,1}]\n                         output_file fasta_file weights [weights ...]\n\nClassify sequences from a input FASTA file.\n\npositional arguments:\n  output_file           output TSV file containing the results of the\n                        classification.\n  fasta_file            input FASTA file containing transcript sequences.\n  weights               input HDF5 file(s) containing weights of a trained\n                        RNAsamba network (if more than a file is provided, an\n                        ensembling of the models will be performed).\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --version             show program's version number and exit\n  -p PROTEIN_FASTA, --protein_fasta PROTEIN_FASTA\n                        output FASTA file containing translated sequences for\n                        the predicted coding ORFs. (default: None)\n  -v {0,1}, --verbose {0,1}\n                        print the progress of the classification. 0 = silent,\n                        1 = current step. (default: 0)\n```\n\n## Examples\n\n- Training a new classification model using *Mus musculus* data downloaded from GENCODE:\n\n```\nrnasamba train -v 2 mouse_model.hdf5 gencode.vM21.pc_transcripts.fa gencode.vM21.lncRNA_transcripts.fa\n```\n\n- Classifying sequences using our pre-trained model (`partial_length_weights.hdf5`) and saving the predicted proteins into a FASTA file:\n\n```\nrnasamba classify -p predicted_proteins.fa classification.tsv input.fa partial_length_weights.hdf5\nhead classification.tsv\n\nsequence_name\tcoding_score\tclassification\nENSMUST00000054910\t0.99022\tcoding\nENSMUST00000059648\t0.84718\tcoding\nENSMUST00000055537\t0.99713\tcoding\nENSMUST00000030975\t0.85189\tcoding\nENSMUST00000050754\t0.02638\tnoncoding\nENSMUST00000008011\t0.14949\tnoncoding\nENSMUST00000061643\t0.03456\tnoncoding\nENSMUST00000059704\t0.89232\tcoding\nENSMUST00000036304\t0.03782\tnoncoding\n```\n\n## Using the Docker image\n\n```\ndocker pull antoniopcamargo/rnasamba\n\n# Training example:\ndocker run -ti --rm -u $(id -u) -v \"$(pwd):/app\" antoniopcamargo/rnasamba train -v 2 mouse_model.hdf5 gencode.vM21.pc_transcripts.fa gencode.vM21.lncRNA_transcripts.fa\n\n# Classification example:\ndocker run -ti --rm -u $(id -u) -v \"$(pwd):/app\" antoniopcamargo/rnasamba classify -p predicted_proteins.fa classification.tsv input.fa full_length_weights.hdf5\n```\n\n\n",
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