eFIP: A Tool for Mining Functional Impact of Phosphorylation from Literature

Arighi, Cecilia N.; Siu, Amy Y.; Tudor, Catalina O.; Nchoutmboube, Jules; Wu, Cathy H.; Shanker, Vijay

doi:10.1007/978-1-60761-977-2_5

Cited by 10 publications

(11 citation statements)

References 28 publications

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“…Other text-mining advances include biological relationship extraction such as gene-disease interactions, 18 named entity recognition (gene/protein name, 19 organisms, 20 and diseases, 21 etc.). Current web-based text mining techniques for PTM information extraction include RLIMS-P, 22 RLIMS-P 2.0, 23 eFIP 24 and MinePhos, 25 RLIMS-P 22 is the¯rst tool for PTM information extraction. It is a rule-based system which utilizes shallow parsing technique and manually developed patterns to extract phosphorylation information (substrates, kinases and sites) from abstracts.…”

Section: Introductionmentioning

confidence: 99%

“…However, RLIMS-P 22 only extracts phosphorylation information, and its complex and restricted patterns limit its overall performance. eFIP 24 integrates various tools to extract protein phosphorylation information from abstracts, and aids scientists to quickly¯nd phosphorylation information from abstracts with a given protein (including site and kinase). MinePhos 25 combines several methods including enhanced RLIMS-P patterns and SVM to extract protein phosphorylation information from published literature.…”

Section: Introductionmentioning

confidence: 99%

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MPTM: A tool for mining protein post-translational modifications from literature

Sun

Wang

2017

J. Bioinform. Comput. Biol.

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Due to the importance of post-translational modi¯cations (PTMs) in human health and diseases, PTMs are regularly reported in the biomedical literature. However, the continuing and rapid pace of expansion of this literature brings a huge challenge for researchers and database curators. Therefore, there is a pressing need to aid them in identifying relevant PTM information more e±ciently by using a text mining system. So far, only a few web servers are available for mining information of a very limited number of PTMs, which are based on simple pattern matching or pre-de¯ned rules. In our work, in order to help researchers and database curators easily¯nd and retrieve PTM information from available text, we have developed a text mining tool called MPTM, which extracts and organizes valuable knowledge about 11 common PTMs from abstracts in PubMed by using relations extracted from dependency parse trees and a heuristic algorithm. It is the¯rst web server that provides literature mining service for hydroxylation, myristoylation and GPI-anchor. The tool is also used to¯nd new publications on PTMs from PubMed and uncovers potential PTM information by large-scale text analysis. MPTM analyzes text sentences to identify protein names including substrates and proteininteracting enzymes, and automatically associates them with the UniProtKB protein entry. To facilitate further investigation, it also retrieves PTM-related information, such as human diseases, Gene Ontology terms and organisms from the input text and related databases. In addition, an online database (MPTMDB) with extracted PTM information and a local MPTM Lite package are provided on the MPTM website. MPTM is freely available online at

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MPTM: A tool for mining protein post-translational modifications from literature

Sun

Wang

2017

J. Bioinform. Comput. Biol.

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“…More recently, the system has also been used to feed information to another text mining system, eFIP, to mine effects of phosphorylation events reported in literature [4,35]. To further enhance the utility of RLIMS-P, we currently aim at making the system generalizable for other PTM types and also applicable to full-text articles, which provides richer biological knowledge beyond abstracts [8].…”

Section: Protein Phosphorylation Iementioning

confidence: 97%

Text Mining of Protein Phosphorylation Information Using a Generalizable Rule-Based Approach

Torii

Arighi

Wang

et al. 2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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Literature-based annotation of protein phosphorylation is the focus of many biological databases, as phosphorylation is a global regulator of cellular activity. To speed up manual curation of phosphorylation information, text mining technology has been utilized. In this paper, we report our ongoing effort to enhance RLIMS-P, a rule-based information extraction (IE) system to identify protein phosphorylation information in scientific literature. Despite the high accuracy attained by RLIMS-P, the use of elaborated patterns and rules resulted in a substantial effort for system development and maintenance. To mitigate this challenge, we redesigned RLIMS-P and integrated new natural language processing (NLP) techniques. It has also been adapted to mine full-text articles and generalized to be able to exploit common features for different post-translational modifications (PTMs). The updated RLIMS-P (version 2.0) was evaluated on abstracts in the publicly available BioNLP GENIA event extraction (GE) corpus, and achieved F-scores of 0.92 and 0.96 for phosphorylation substrate and site, respectively. On a full-text corpus developed in-house, it achieved F-scores of 0.91 and 0.92 for substrate and site, and 0.88 for kinase. The system was applied to the PubMed Central (PMC) Open Access Subset, and promising results have been obtained in mining the full-text articles. RLIMS-P focuses on protein phosphorylation information, but its new design would be generalizable for other PTM types. RLIMS-P version 2.0 is available at: http://proteininformationresource.org/rlimsp/.

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“…This tool processes documents with a shallow parser and then extracts information by matching text with manually designed patterns. eFIP is a system produced by the developers of RLIMS-P which combines various tools to identify abstracts which provide information on the functional context of protein phosphorylation, such as protein interaction induced by phosphorylation [11]. Another tool, MinePhos, uses enhanced RLIMS-P patterns along with support vector machines (SVM) combined with dictionary lookup to identify the modified protein [12].…”

Section: Introductionmentioning

confidence: 99%

Application of text-mining for updating protein post-translational modification annotation in UniProtKB

Veuthey

Bridge

Gobeill³

et al. 2013

BMC Bioinformatics

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BackgroundThe annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB.ResultsThe procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments.ConclusionsThe information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.

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eFIP: A Tool for Mining Functional Impact of Phosphorylation from Literature

Cited by 10 publications

References 28 publications

MPTM: A tool for mining protein post-translational modifications from literature

MPTM: A tool for mining protein post-translational modifications from literature

Text Mining of Protein Phosphorylation Information Using a Generalizable Rule-Based Approach

Application of text-mining for updating protein post-translational modification annotation in UniProtKB

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