Cargando…

Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB

A constant influx of new data poses a challenge in keeping the annotation in biological databases current. Most biological databases contain significant quantities of textual annotation, which often contains the richest source of knowledge. Many databases reuse existing knowledge; during the curatio...

Descripción completa

Detalles Bibliográficos
Autores principales: Bell, Michael J., Collison, Matthew, Lord, Phillip
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797126/
https://www.ncbi.nlm.nih.gov/pubmed/24143170
http://dx.doi.org/10.1371/journal.pone.0075541
_version_ 1782287580179464192
author Bell, Michael J.
Collison, Matthew
Lord, Phillip
author_facet Bell, Michael J.
Collison, Matthew
Lord, Phillip
author_sort Bell, Michael J.
collection PubMed
description A constant influx of new data poses a challenge in keeping the annotation in biological databases current. Most biological databases contain significant quantities of textual annotation, which often contains the richest source of knowledge. Many databases reuse existing knowledge; during the curation process annotations are often propagated between entries. However, this is often not made explicit. Therefore, it can be hard, potentially impossible, for a reader to identify where an annotation originated from. Within this work we attempt to identify annotation provenance and track its subsequent propagation. Specifically, we exploit annotation reuse within the UniProt Knowledgebase (UniProtKB), at the level of individual sentences. We describe a visualisation approach for the provenance and propagation of sentences in UniProtKB which enables a large-scale statistical analysis. Initially levels of sentence reuse within UniProtKB were analysed, showing that reuse is heavily prevalent, which enables the tracking of provenance and propagation. By analysing sentences throughout UniProtKB, a number of interesting propagation patterns were identified, covering over [Image: see text] sentences. Over [Image: see text] sentences remain in the database after they have been removed from the entries where they originally occurred. Analysing a subset of these sentences suggest that approximately [Image: see text] are erroneous, whilst [Image: see text] appear to be inconsistent. These results suggest that being able to visualise sentence propagation and provenance can aid in the determination of the accuracy and quality of textual annotation. Source code and supplementary data are available from the authors website at http://homepages.cs.ncl.ac.uk/m.j.bell1/sentence_analysis/.
format Online
Article
Text
id pubmed-3797126
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-37971262013-10-18 Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB Bell, Michael J. Collison, Matthew Lord, Phillip PLoS One Research Article A constant influx of new data poses a challenge in keeping the annotation in biological databases current. Most biological databases contain significant quantities of textual annotation, which often contains the richest source of knowledge. Many databases reuse existing knowledge; during the curation process annotations are often propagated between entries. However, this is often not made explicit. Therefore, it can be hard, potentially impossible, for a reader to identify where an annotation originated from. Within this work we attempt to identify annotation provenance and track its subsequent propagation. Specifically, we exploit annotation reuse within the UniProt Knowledgebase (UniProtKB), at the level of individual sentences. We describe a visualisation approach for the provenance and propagation of sentences in UniProtKB which enables a large-scale statistical analysis. Initially levels of sentence reuse within UniProtKB were analysed, showing that reuse is heavily prevalent, which enables the tracking of provenance and propagation. By analysing sentences throughout UniProtKB, a number of interesting propagation patterns were identified, covering over [Image: see text] sentences. Over [Image: see text] sentences remain in the database after they have been removed from the entries where they originally occurred. Analysing a subset of these sentences suggest that approximately [Image: see text] are erroneous, whilst [Image: see text] appear to be inconsistent. These results suggest that being able to visualise sentence propagation and provenance can aid in the determination of the accuracy and quality of textual annotation. Source code and supplementary data are available from the authors website at http://homepages.cs.ncl.ac.uk/m.j.bell1/sentence_analysis/. Public Library of Science 2013-10-15 /pmc/articles/PMC3797126/ /pubmed/24143170 http://dx.doi.org/10.1371/journal.pone.0075541 Text en © 2013 Bell et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Bell, Michael J.
Collison, Matthew
Lord, Phillip
Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB
title Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB
title_full Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB
title_fullStr Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB
title_full_unstemmed Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB
title_short Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB
title_sort can inferred provenance and its visualisation be used to detect erroneous annotation? a case study using uniprotkb
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797126/
https://www.ncbi.nlm.nih.gov/pubmed/24143170
http://dx.doi.org/10.1371/journal.pone.0075541
work_keys_str_mv AT bellmichaelj caninferredprovenanceanditsvisualisationbeusedtodetecterroneousannotationacasestudyusinguniprotkb
AT collisonmatthew caninferredprovenanceanditsvisualisationbeusedtodetecterroneousannotationacasestudyusinguniprotkb
AT lordphillip caninferredprovenanceanditsvisualisationbeusedtodetecterroneousannotationacasestudyusinguniprotkb