Evidence map›Paper›PMID 28706471›Full record

ArticleCEUR workshop proceedings2016

Scalable Text Mining Assisted Curation of Post-Translationally Modified Proteoforms in the Protein Ontology.

Karen E Ross, Darren A Natale, Cecilia Arighi, Sheng-Chih Chen, Hongzhan Huang, Gang Li, Jia Ren, Michael Wang, K Vijay-Shanker, Cathy H Wu

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In one paragraph

Article in CEUR workshop proceedings, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Karen E RossProtein Information Resource, Georgetown University Medical Center, Washington, DC, USA.
Darren A NataleProtein Information Resource, Georgetown University Medical Center, Washington, DC, USA.
Cecilia ArighiCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Sheng-Chih ChenCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Hongzhan HuangCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Gang LiCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Jia RenCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Michael WangCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
K Vijay-ShankerCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Cathy H WuCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.

Funding

Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in DelawareP20GM103446 · NIGMS · UNIVERSITY OF DELAWARE · PI Anjana Narayan Bhat · 2012 to 2026
$67.2M
PRO: A Protein Ontology in Open Biomedical OntologiesR01GM080646 · NIGMS · UNIVERSITY OF DELAWARE · PI WU, CATHY H. · 2007 to 2018
$8.9M
NIGMS NIH HHS P20 GM103446NIGMS NIH HHS R01 GM080646
6 · The paper itself

Abstract

The Protein Ontology (PRO) defines protein classes and their interrelationships from the family to the protein form (proteoform) level within and across species. One of the unique contributions of PRO is its representation of post-translationally modified (PTM) proteoforms. However, progress in adding PTM proteoform classes to PRO has been relatively slow due to the extensive manual curation effort required. Here we report an automated pipeline for creation of PTM proteoform classes that leverages two phosphorylation-focused text mining tools (RLIMS-P, which detects mentions of kinases, substrates, and phosphorylation sites, and eFIP, which detects phosphorylation-dependent protein-protein interactions (PPIs)) and our integrated PTM database, iPTMnet. By applying this pipeline, we obtained a set of ~820 substrate-site pairs that are suitable for automated PRO term generation with literature-based evidence attribution. Inclusion of these terms in PRO will increase PRO coverage of species-specific PTM proteoforms by 50%. Many of these new proteoforms also have associated kinase and/or PPI information. Finally, we show a phosphorylation network for the human and mouse peptidyl-prolyl cis-trans isomerase (PIN1/Pin1) derived from our dataset that demonstrates the biological complexity of the information we have extracted. Our approach addresses scalability in PRO curation and will be further expanded to advance PRO representation of phosphorylated proteoforms.

Indexed as

phosphorylationpost-translational modificationProtein Ontology (PRO)proteoformtext mining

Identifiers

PMID28706471
PMCPMC5504912

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.