Evidence map›Paper›PMID 30805646›Full record

ArticleDatabase : the journal of biological databases and curation2019

PIRSitePredict for protein functional site prediction using position-specific rules.

Chuming Chen, Qinghua Wang, Hongzhan Huang, Cholanayakanahalli R Vinayaka, John S Garavelli, Cecilia N Arighi, Darren A Natale, Cathy H Wu

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. InterPro in 2022.Nucleic acids research · 2023
    Article
  4. Article
  5. 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

8 authors.

Chuming ChenCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Qinghua WangCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Hongzhan HuangCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Cholanayakanahalli R VinayakaProtein Information Resource, Georgetown University Medical Center, Washington, DC, USA.
John S GaravelliCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Cecilia N ArighiCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Darren A NataleProtein Information Resource, Georgetown University Medical Center, Washington, DC, 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
UniProt - Text Mining and Large Language models to Enhance Functional Annotation in UniProt of Protein Variants Associated with Alzheimer's DiseaseU24HG007822 · NHGRI · GEORGETOWN UNIVERSITY · PI Alex Bateman, Alan James Bridge · 2018 to 2026
$53.0M
UniProt: A centralized protein sequence and function resourceU41HG007822 · NHGRI · EUROPEAN MOLECULAR BIOLOGY LABORATORY · PI BATEMAN, ALEX · 2014 to 2017
$21.9M
NHGRI NIH HHS U24 HG007822NHGRI NIH HHS U41 HG007822NIGMS NIH HHS P20 GM103446
6 · The paper itself

Abstract

Methods focused on predicting 'global' annotations for proteins (such as molecular function, biological process and presence of domains or membership in a family) have reached a relatively mature stage. Methods to provide fine-grained 'local' annotation of functional sites (at the level of individual amino acid) are now coming to the forefront, especially in light of the rapid accumulation of genetic variant data. We have developed a computational method and workflow that predicts functional sites within proteins using position-specific conditional template annotation rules (namely PIR Site Rules or PIRSRs for short). Such rules are curated through review of known protein structural and other experimental data by structural biologists and are used to generate high-quality annotations for the UniProt Knowledgebase (UniProtKB) unreviewed section. To share the PIRSR functional site prediction method with the broader scientific community, we have streamlined our workflow and developed a stand-alone Java software package named PIRSitePredict. We demonstrate the use of PIRSitePredict for functional annotation of de novo assembled genome/transcriptome by annotating uncharacterized proteins from Trinity RNA-seq assembly of embryonic transcriptomes of the following three cartilaginous fishes: Leucoraja erinacea (Little Skate), Scyliorhinus canicula (Small-spotted Catshark) and Callorhinchus milii (Elephant Shark). On average about 1200 lines of annotations were predicted for each species.

Indexed as

Databases, ProteinMolecular Sequence AnnotationAmino Acid SequenceAnimalsEmbryo, NonmammalianFishesGenomeSoftwareTranscriptome

Identifiers

PMID30805646
PMCPMC6389862

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.