scholarly journals NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity

2020 ◽  
Author(s):  
Meet Barot ◽  
Vladimir Gligorijevic ◽  
Kyunghyun Cho ◽  
Richard Bonneau
Keyword(s):  
Biological Networks ◽  
Protein Function ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Single Species ◽  
Function Prediction ◽  
Alignment Algorithm ◽  
Network Information ◽  
Ppi Networks ◽  
Multiple Species

Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to proteome and biological network functional annotation use sequence similarity to transfer knowledge between species. These similarity-based approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular or organismal context for meaningful function prediction. In order to supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, the majority of these methods are tied to a network for a single species, and many species lack biological networks. In this work, we integrate sequence and network information across multiple species by applying an IsoRank-derived network alignment algorithm to create a meta-network profile of the proteins of multiple species. We then use this integrated multispecies meta-network as input features to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and more diverse examples from multiple organisms, and consequently leads to significant improvements in function prediction performance. Further, we evaluate our approach in a setting in which an organism's PPI network is left out, using other organisms' network information and sequence homology in order to make predictions for the left-out organism, to simulate cases in which a newly sequenced species has no network information available.

scholarly journals NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity

2021 ◽  
Author(s):  
Meet Barot ◽  
Vladimir Gligorijević ◽  
Kyunghyun Cho ◽  
Richard Bonneau
Keyword(s):  
Biological Networks ◽  
Protein Function ◽  
Functional Annotation ◽  
Sequence Similarity ◽  
Function Prediction ◽  
Supplementary Information ◽  
Learning Sequence ◽  
Network Information ◽  
Ppi Networks ◽  
Multiple Species

Abstract Motivation Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to protein functional annotation use sequence similarity to transfer knowledge between species. These approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular context for meaningful prediction. To supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, most of these methods are tied to a network for a single species, and many species lack biological networks. Results In this work, we integrate sequence and network information across multiple species by computing IsoRank similarity scores to create a meta-network profile of the proteins of multiple species. We use this integrated multispecies meta-network as input to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and consequently leads to significant improvements in function prediction performance compared to two network-based methods, a deep learning sequence-based method, and the BLAST annotation method used in the Critial Assessment of Functional Annotation. We are able to demonstrate that our approach performs well even in cases where a species has no network information available: when an organism’s PPI network is left out we can use our multi-species method to make predictions for the left-out organism with good performance. Availability The code is freely available at https://github.com/nowittynamesleft/NetQuilt Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiaogen Zhou ◽  
Wei Xiong ◽  
Yang Wang ◽  
Jihong Guan
Keyword(s):  
Protein Function ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Network Reconstruction ◽  
Function Prediction ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Performance Differences ◽  
Global Similarity ◽  
Better Than

Over the past decades, massive amounts of protein-protein interaction (PPI) data have been accumulated due to the advancement of high-throughput technologies, and but data quality issues (noise or incompleteness) of PPI have been still affecting protein function prediction accuracy based on PPI networks. Although two main strategies of network reconstruction and edge enrichment have been reported on the effectiveness of boosting the prediction performance in numerous literature studies, there still lack comparative studies of the performance differences between network reconstruction and edge enrichment. Inspired by the question, this study first uses three protein similarity metrics (local, global and sequence) for network reconstruction and edge enrichment in PPI networks, and then evaluates the performance differences of network reconstruction, edge enrichment and the original networks on two real PPI datasets. The experimental results demonstrate that edge enrichment work better than both network reconstruction and original networks. Moreover, for the edge enrichment of PPI networks, the sequence similarity outperformes both local and global similarity. In summary, our study can help biologists select suitable pre-processing schemes and achieve better protein function prediction for PPI networks.

scholarly journals Integration of relational and hierarchical network information for protein function prediction

2008 ◽  
Vol 9 (1) ◽  
pp. 350 ◽  
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

Modules in Biological Networks

2013 ◽  
pp. 637-663
Author(s):  
Bing Zhang ◽  
Zhiao Shi
Keyword(s):  
Biological Networks ◽  
Protein Complex ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Single Gene ◽  
Biological Systems ◽  
Function Prediction ◽  
Diverse Group ◽  
Biological Studies ◽  
Gene Level

One of the most prominent properties of networks representing complex systems is modularity. Network-based module identification has captured the attention of a diverse group of scientists from various domains and a variety of methods have been developed. The ability to decompose complex biological systems into modules allows the use of modules rather than individual genes as units in biological studies. A modular view is shaping research methods in biology. Module-based approaches have found broad applications in protein complex identification, protein function prediction, protein expression prediction, as well as disease studies. Compared to single gene-level analyses, module-level analyses offer higher robustness and sensitivity. More importantly, module-level analyses can lead to a better understanding of the design and organization of complex biological systems.

scholarly journals Phylo-PFP: improved automated protein function prediction using phylogenetic distance of distantly related sequences

2018 ◽  
Vol 35 (5) ◽  
pp. 753-759 ◽  
Author(s):  
Aashish Jain ◽  
Daisuke Kihara
Keyword(s):  
Protein Function ◽  
Transfer Functions ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Query Protein ◽  
Function Prediction ◽  
Homology Search ◽  
Supplementary Information ◽  
Phylogenetic Distance

Abstract Motivation Function annotation of proteins is fundamental in contemporary biology across fields including genomics, molecular biology, biochemistry, systems biology and bioinformatics. Function prediction is indispensable in providing clues for interpreting omics-scale data as well as in assisting biologists to build hypotheses for designing experiments. As sequencing genomes is now routine due to the rapid advancement of sequencing technologies, computational protein function prediction methods have become increasingly important. A conventional method of annotating a protein sequence is to transfer functions from top hits of a homology search; however, this approach has substantial short comings including a low coverage in genome annotation. Results Here we have developed Phylo-PFP, a new sequence-based protein function prediction method, which mines functional information from a broad range of similar sequences, including those with a low sequence similarity identified by a PSI-BLAST search. To evaluate functional similarity between identified sequences and the query protein more accurately, Phylo-PFP reranks retrieved sequences by considering their phylogenetic distance. Compared to the Phylo-PFP’s predecessor, PFP, which was among the top ranked methods in the second round of the Critical Assessment of Functional Annotation (CAFA2), Phylo-PFP demonstrated substantial improvement in prediction accuracy. Phylo-PFP was further shown to outperform prediction programs to date that were ranked top in CAFA2. Availability and implementation Phylo-PFP web server is available for at http://kiharalab.org/phylo_pfp.php. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals INGA: protein function prediction combining interaction networks, domain assignments and sequence similarity

2015 ◽  
Vol 43 (W1) ◽  
pp. W134-W140 ◽  
Author(s):  
Damiano Piovesan ◽  
Manuel Giollo ◽  
Emanuela Leonardi ◽  
Carlo Ferrari ◽  
Silvio C.E. Tosatto
Keyword(s):  
Protein Function ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Interaction Networks

FUNCTION PREDICTION OF UNCHARACTERIZED PROTEINS

2007 ◽  
Vol 05 (01) ◽  
pp. 1-30 ◽  
Author(s):  
TROY HAWKINS ◽  
DAISUKE KIHARA
Keyword(s):  
Protein Function ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Biological Research ◽  
Special Focus ◽  
Genomic Context ◽  
Uncharacterized Protein ◽  
Genomics And Proteomics ◽  
Proteomics Experiment

Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.

scholarly journals Improving protein function prediction using domain and protein complexes in PPI networks

2014 ◽  
Vol 8 (1) ◽  
pp. 35 ◽  
Author(s):  
Wei Peng ◽  
Jianxin Wang ◽  
Juan Cai ◽  
Lu Chen ◽  
Min Li ◽  
...  
Keyword(s):  
Protein Function ◽  
Protein Complexes ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Ppi Networks

scholarly journals Wei2GO: weighted sequence similarity-based protein function prediction

2020 ◽  
Author(s):  
Maarten J.M.F Reijnders
Keyword(s):  
Gene Ontology ◽  
Open Source ◽  
Protein Function ◽  
Large Scale ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Computational Time ◽  
Web Servers ◽  
Weighted Sequence

AbstractBackgroundProtein function prediction is an important part of bioinformatics and genomics studies. There are many different predictors available, however most of these are in the form of web-servers instead of open-source locally installable versions. Such local versions are necessary to perform large scale genomics studies due to the presence of limitations imposed by web servers such as queues, prediction speed, and updatability of databases.MethodsThis paper describes Wei2GO: a weighted sequence similarity and python-based open-source protein function prediction software. It uses DIAMOND and HMMScan sequence alignment searches against the UniProtKB and Pfam databases respectively, transfers Gene Ontology terms from the reference protein to the query protein, and uses a weighing algorithm to calculate a score for the Gene Ontology annotations.ResultsWei2GO is compared against the Argot2 and Argot2.5 web servers, which use a similar concept, and DeepGOPlus which acts as a reference. Wei2GO shows an increase in performance according to precision and recall curves, Fmax scores, and Smin scores for biological process and molecular function ontologies. Computational time compared to Argot2 and Argot2.5 is decreased from several hours to several minutes.AvailabilityWei2GO is written in Python 3, and can be found at https://gitlab.com/mreijnders/Wei2GO

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