PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine lea...

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Autores principales:	Daniel Griffith, Alex S Holehouse
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	machine learning high-throughput methods proteomics bioinformatics functional annotation Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/eea3c378655a42d68e392633fc89bbb7
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spelling	oai:doaj.org-article:eea3c378655a42d68e392633fc89bbb72021-12-01T10:58:22ZPARROT is a flexible recurrent neural network framework for analysis of large protein datasets10.7554/eLife.705762050-084Xe70576https://doaj.org/article/eea3c378655a42d68e392633fc89bbb72021-09-01T00:00:00Zhttps://elifesciences.org/articles/70576https://doaj.org/toc/2050-084XThe rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.Daniel GriffithAlex S HolehouseeLife Sciences Publications Ltdarticlemachine learninghigh-throughput methodsproteomicsbioinformaticsfunctional annotationMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning high-throughput methods proteomics bioinformatics functional annotation Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	machine learning high-throughput methods proteomics bioinformatics functional annotation Medicine R Science Q Biology (General) QH301-705.5 Daniel Griffith Alex S Holehouse PARROT is a flexible recurrent neural network framework for analysis of large protein datasets
description	The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.
format	article
author	Daniel Griffith Alex S Holehouse
author_facet	Daniel Griffith Alex S Holehouse
author_sort	Daniel Griffith
title	PARROT is a flexible recurrent neural network framework for analysis of large protein datasets
title_short	PARROT is a flexible recurrent neural network framework for analysis of large protein datasets
title_full	PARROT is a flexible recurrent neural network framework for analysis of large protein datasets
title_fullStr	PARROT is a flexible recurrent neural network framework for analysis of large protein datasets
title_full_unstemmed	PARROT is a flexible recurrent neural network framework for analysis of large protein datasets
title_sort	parrot is a flexible recurrent neural network framework for analysis of large protein datasets
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/eea3c378655a42d68e392633fc89bbb7
work_keys_str_mv	AT danielgriffith parrotisaflexiblerecurrentneuralnetworkframeworkforanalysisoflargeproteindatasets AT alexsholehouse parrotisaflexiblerecurrentneuralnetworkframeworkforanalysisoflargeproteindatasets
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PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

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