Highly Bi-Connected Subgraphs for Computational Protein Function Annotation

2016 ◽  
pp. 573-584
Author(s):  
Jucheol Moon ◽  
Iddo Friedberg ◽  
Oliver Eulenstein
Keyword(s):  
Protein Function ◽  
Function Annotation ◽  
Protein Function Annotation ◽  
Connected Subgraphs

scholarly journals A combined approach for genome wide protein function annotation/prediction

2013 ◽  
Vol 11 (Suppl 1) ◽  
pp. S1 ◽  
Author(s):  
Alfredo Benso ◽  
Stefano Di Carlo ◽  
Hafeez ur Rehman ◽  
Gianfranco Politano ◽  
Alessandro Savino ◽  
...  
Keyword(s):  
Protein Function ◽  
Combined Approach ◽  
Function Annotation ◽  
Protein Function Annotation ◽  
Genome Wide

scholarly journals Protein function annotation using protein domain family resources

Methods ◽  
2016 ◽  
Vol 93 ◽  
pp. 24-34 ◽  
Author(s):  
Sayoni Das ◽  
Christine A. Orengo
Keyword(s):  
Protein Function ◽  
Protein Domain ◽  
Domain Family ◽  
Family Resources ◽  
Function Annotation ◽  
Protein Function Annotation ◽  
Protein Domain Family

scholarly journals The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications

BMC Genomics ◽  
2008 ◽  
Vol 9 (Suppl 2) ◽  
pp. S2 ◽  
Author(s):  
Inbal Halperin ◽  
Dariya S Glazer ◽  
Shirley Wu ◽  
Russ B Altman
Keyword(s):  
Protein Function ◽  
Function Annotation ◽  
Protein Function Annotation ◽  
Novel Applications

scholarly journals The development of PIPA: an integrated and automated pipeline for genome-wide protein function annotation

2008 ◽  
Vol 9 (1) ◽  
pp. 52 ◽  
Author(s):  
Chenggang Yu ◽  
Nela Zavaljevski ◽  
Valmik Desai ◽  
Seth Johnson ◽  
Fred J Stevens ◽  
...  
Keyword(s):  
Protein Function ◽  
Function Annotation ◽  
Protein Function Annotation ◽  
Genome Wide ◽  
Automated Pipeline

scholarly journals Improving Protein Function Annotation via Unsupervised Pre-training: Robustness, Efficiency, and Insights

2021 ◽  
Author(s):  
David Dohan ◽  
Andreea Gane ◽  
Maxwell L. Bileschi ◽  
David Belanger ◽  
Lucy Colwell
Keyword(s):  
Protein Function ◽  
Function Annotation ◽  
Protein Function Annotation

scholarly journals Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

2019 ◽  
Vol 21 (4) ◽  
pp. 1437-1447 ◽  
Author(s):  
Jiajun Hong ◽  
Yongchao Luo ◽  
Yang Zhang ◽  
Junbiao Ying ◽  
Weiwei Xue ◽  
...  
Keyword(s):  
Deep Learning ◽  
False Discovery Rate ◽  
Protein Function ◽  
Functional Annotation ◽  
De Novo ◽  
Learning Algorithm ◽  
Function Annotation ◽  
False Discovery ◽  
Protein Function Annotation ◽  
Annotation Accuracy

Abstract Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

scholarly journals Functional classification of CATH superfamilies: a domain-based approach for protein function annotation

2015 ◽  
Vol 31 (21) ◽  
pp. 3460-3467 ◽  
Author(s):  
Sayoni Das ◽  
David Lee ◽  
Ian Sillitoe ◽  
Natalie L. Dawson ◽  
Jonathan G. Lees ◽  
...  
Keyword(s):  
Protein Function ◽  
Functional Classification ◽  
Function Annotation ◽  
Protein Function Annotation

scholarly journals SwiftOrtho: a Fast, Memory-Efficient, Multiple Genome Orthology Classifier

2019 ◽  
Author(s):  
Xiao Hu ◽  
Iddo Friedberg
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Comparative Genomic ◽  
Analysis Tool ◽  
Bacterial Genomes ◽  
Function Annotation ◽  
Large Scale Data ◽  
Protein Function Annotation ◽  
Genome Analyses ◽  
Memory Efficient

AbstractIntroductionGene homology type classification is a requisite for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. A large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic datasets, these tools require high memory and CPU usage, typically available only in costly computational clusters. To address this problem, we developed a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data.ResultsIn our tests, SwiftOrtho is the only tool that completed orthology analysis of 1,760 bacterial genomes on a computer with only 4GB RAM. Using various standard orthology datasets, we also show that SwiftOrtho has a high accuracy. SwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low memory computers.Availabilityhttps://github.com/Rinoahu/SwiftOrtho

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