NetGO: improving large-scale protein function prediction with massive network information

Abstract Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Based on our GOLabeler—a state-of-the-art method for the third critical assessment of functional annotation (CAFA3), in this paper we propose NetGO, a web server that is able to further improve the performance of the large-scale AFP by incorporating massive protein-protein network information. Specifically, the advantages of NetGO are threefold in using network information: (i) NetGO relies on a powerful learning to rank framework from machine learning to effectively integrate both sequence and network information of proteins; (ii) NetGO uses the massive network information of all species (>2000) in STRING (other than only some specific species) and (iii) NetGO still can use network information to annotate a protein by homology transfer, even if it is not contained in STRING. Separating training and testing data with the same time-delayed settings of CAFA, we comprehensively examined the performance of NetGO. Experimental results have clearly demonstrated that NetGO significantly outperforms GOLabeler and other competing methods. The NetGO web server is freely available at http://issubmission.sjtu.edu.cn/netgo/.

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NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information

10.1101/439554 ◽

2018 ◽

Author(s):

Ronghui You ◽

Shuwei Yao ◽

Xiaodi Huang ◽

Fengzhu Sun ◽

Hiroshi Mamitsuka ◽

...

Keyword(s):

Protein Function ◽

Large Scale ◽

Protein Function Prediction ◽

Learning To Rank ◽

Performance Comparison ◽

Function Prediction ◽

Sequence Information ◽

Network Information ◽

Species Specific ◽

Go Terms

AbstractAutomated function prediction (AFP) of proteins is of great significance in biology. In essence, AFP is a large-scale multi-label classification over pairs of proteins and GO terms. Existing AFP approaches, however, have their limitations on both sides of proteins and GO terms. Using various sequence information and the robust learning to rank (LTR) framework, we have developed GOLabeler, a state-of-the-art approach of CAFA3, which overcomes the limitation of the GO term side, such as imbalanced GO terms. Unfortunately, for the protein side issue, available abundant protein information, except for sequences, have not been effectively used for large-scale AFP in CAFA. We propose NetGO that is able to improve large-scale AFP with massive network information. The novelties of NetGO have threefold in using network information: 1) the powerful LTR framework of NetGO efficiently and effectively integrates both sequence and network information, which can easily make large-scale AFP; 2) NetGO can use whole and massive network information of all species (>2000) in STRING (other than only high confidence links and/or some specific species); and 3) NetGO can still use network information to annotate a protein by homology transfer even if it is not covered in STRING. Under numerous experimental settings, we examined the performance of NetGO, such as general performance comparison, species-specific prediction, and prediction on difficult proteins, by using training and test data separated by time-delayed settings of CAFA. Experimental results have clearly demonstrated that NetGO outperforms GOLabeler, DeepGO, and other compared baseline methods significantly. In addition, several interesting findings from our experiments on NetGO would be useful for future AFP research.

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GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank

Bioinformatics ◽

10.1093/bioinformatics/bty130 ◽

2018 ◽

Vol 34 (14) ◽

pp. 2465-2473 ◽

Cited By ~ 40

Author(s):

Ronghui You ◽

Zihan Zhang ◽

Yi Xiong ◽

Fengzhu Sun ◽

Hiroshi Mamitsuka ◽

...

Keyword(s):

Protein Function ◽

Large Scale ◽

Protein Function Prediction ◽

Learning To Rank ◽

Function Prediction

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GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank

10.1101/145763 ◽

2017 ◽

Author(s):

Ronghui You ◽

Zihan Zhang ◽

Yi Xiong ◽

Fengzhu Sun ◽

Hiroshi Mamitsuka ◽

...

Keyword(s):

Protein Function ◽

Large Scale ◽

Protein Function Prediction ◽

Learning To Rank ◽

Classification Problem ◽

Function Prediction ◽

New Paradigm ◽

Effective Manner ◽

Homology Information ◽

Significant Performance

AbstractMotivation: Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only ¡1% of more than 70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multi-label classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input). Furthermore, homology-based SAFP tools are competitive in AFP competitions, while they do not necessarily work well for so-calleddifficultproteins, which have ¡60% sequence identity to proteins with annotations already. Thus, the vital and challenging problem now is to develop a method for SAFP, particularly for difficult proteins.Methods: The key of this method is to extract not only homology information but also diverse, deep-rooted information/evidence from sequence inputs and integrate them into a predictor in an efficient and also effective manner. We propose GOLabeler, which integrates five component classifiers, trained from different features, including GO term frequency, sequence alignment, amino acid trigram, domains and motifs, and biophysical properties, etc., in the framework of learning to rank (LTR), a new paradigm of machine learning, especially powerful for multi-label classification.Results: The empirical results obtained by examining GOLabeler extensively and thoroughly by using large-scale datasets revealed numerous favorable aspects of GOLabeler, including significant performance advantage over state-of-the-art AFP methods.Contact:[email protected]

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Integration of relational and hierarchical network information for protein function prediction

BMC Bioinformatics ◽

10.1186/1471-2105-9-350 ◽

2008 ◽

Vol 9 (1) ◽

pp. 350 ◽

Cited By ~ 28

Author(s):

Xiaoyu Jiang ◽

Naoki Nariai ◽

Martin Steffen ◽

Simon Kasif ◽

Eric D Kolaczyk

Keyword(s):

Protein Function ◽

Protein Function Prediction ◽

Function Prediction ◽

Hierarchical Network ◽

Network Information

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Enhancing protein function prediction with taxonomic constraints – The Argot2.5 web server

Methods ◽

10.1016/j.ymeth.2015.08.021 ◽

2016 ◽

Vol 93 ◽

pp. 15-23 ◽

Cited By ~ 19

Author(s):

Enrico Lavezzo ◽

Marco Falda ◽

Paolo Fontana ◽

Luca Bianco ◽

Stefano Toppo

Keyword(s):

Protein Function ◽

Protein Function Prediction ◽

Web Server ◽

Function Prediction

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deepNF: Deep network fusion for protein function prediction

10.1101/223339 ◽

2017 ◽

Cited By ~ 2

Author(s):

Vladimir Gligorijević ◽

Meet Barot ◽

Richard Bonneau

Keyword(s):

Protein Function ◽

Large Scale ◽

Protein Function Prediction ◽

Predictive Performance ◽

Substantial Improvement ◽

Function Prediction ◽

Interaction Networks ◽

Highly Nonlinear ◽

High Level ◽

String Networks

AbstractThe prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provide a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that cannot capture complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity.AvailabilitydeepNF is freely available at: https://github.com/VGligorijevic/deepNF

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A large-scale evaluation of computational protein function prediction

Nature Methods ◽

10.1038/nmeth.2340 ◽

2013 ◽

Vol 10 (3) ◽

pp. 221-227 ◽

Cited By ~ 521

Author(s):

Predrag Radivojac ◽

Wyatt T Clark ◽

Tal Ronnen Oron ◽

Alexandra M Schnoes ◽

Tobias Wittkop ◽

...

Keyword(s):

Protein Function ◽

Large Scale ◽

Protein Function Prediction ◽

Function Prediction ◽

Scale Evaluation

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SIFTER search: a web server for accurate phylogeny-based protein function prediction

Nucleic Acids Research ◽

10.1093/nar/gkv461 ◽

2015 ◽

Vol 43 (W1) ◽

pp. W141-W147 ◽

Cited By ~ 25

Author(s):

Sayed M. Sahraeian ◽

Kevin R. Luo ◽

Steven E. Brenner

Keyword(s):

Protein Function ◽

Protein Function Prediction ◽

Web Server ◽

Function Prediction

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A look back at the quality of Protein Function Prediction tools in CAFA

10.7287/peerj.preprints.27161 ◽

2018 ◽

Author(s):

Morteza Pourreza Shahri ◽

Madhusudan Srinivasan ◽

Diane Bimczok ◽

Upulee Kanewala ◽

Indika Kahanda

Keyword(s):

Protein Function ◽

Large Scale ◽

Computational Models ◽

Protein Function Prediction ◽

Function Prediction ◽

Test Case ◽

Test Cases ◽

Metamorphic Testing ◽

Main Challenge ◽

Scale Experiment

The Critical Assessment of protein Function Annotation algorithms (CAFA) is a large-scale experiment for assessing the computational models for automated function prediction (AFP). The models presented in CAFA have shown excellent promise in terms of prediction accuracy, but quality assurance has been paid relatively less attention. The main challenge associated with conducting systematic testing on AFP software is the lack of a test oracle, which determines passing or failing of a test case; unfortunately, the exact expected outcomes are not well defined for the AFP task. Thus, AFP tools face the oracle problem. Metamorphic testing (MT) is a technique used to test programs that face the oracle problem using metamorphic relations (MRs). A MR determines whether a test has passed or failed by specifying how the output should change according to a specific change made to the input. In this work, we use MT to test nine CAFA2 AFP tools by defining a set of MRs that apply input transformations at the protein-level. According to our initial testing, we observe that several tools fail all the test cases and two tools pass all the test cases on different GO ontologies.

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Large-scale protein function prediction using heterogeneous ensembles

F1000Research ◽

10.12688/f1000research.16415.1 ◽

2018 ◽

Vol 7 ◽

pp. 1577 ◽

Cited By ~ 4

Author(s):

Linhua Wang ◽

Jeffrey Law ◽

Shiv D. Kale ◽

T. M. Murali ◽

Gaurav Pandey

Keyword(s):

Gene Ontology ◽

Logistic Regression ◽

Protein Function ◽

Large Scale ◽

Protein Function Prediction ◽

Ensemble Methods ◽

Function Prediction ◽

Data Type ◽

Heterogeneous Ensemble ◽

The Ideal

Heterogeneous ensembles are an effective approach in scenarios where the ideal data type and/or individual predictor are unclear for a given problem. These ensembles have shown promise for protein function prediction (PFP), but their ability to improve PFP at a large scale is unclear. The overall goal of this study is to critically assess this ability of a variety of heterogeneous ensemble methods across a multitude of functional terms, proteins and organisms. Our results show that these methods, especially Stacking using Logistic Regression, indeed produce more accurate predictions for a variety of Gene Ontology terms differing in size and specificity. To enable the application of these methods to other related problems, we have publicly shared the HPC-enabled code underlying this work as LargeGOPred (https://github.com/GauravPandeyLab/LargeGOPred).

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