Strategies to improve usability and preserve accuracy in biological sequence databases
Artikel i vetenskaplig tidskrift, 2016
Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identifications of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate post-submission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.
Functional prediction
Standards
Sequencing
Annotation
Databases
Författare
Johan Bengtsson-Palme
Göteborgs universitet
Fredrik Boulund
Chalmers, Matematiska vetenskaper, Matematisk statistik
Göteborgs universitet
Robert Edström
Student vid Chalmers
Amir Feizi
Chalmers, Biologi och bioteknik, Systembiologi
Anna Johnning
Chalmers, Matematiska vetenskaper, Matematisk statistik
Göteborgs universitet
Viktor Jonsson
Göteborgs universitet
Chalmers, Matematiska vetenskaper, Matematisk statistik
Fredrik Karlsson
Chalmers, Biologi och bioteknik, Systembiologi
C. Pal
Göteborgs universitet
Mariana Buongermino Pereira
Chalmers, Matematiska vetenskaper, Matematisk statistik
Göteborgs universitet
Anna Rehammar
Göteborgs universitet
Chalmers, Matematiska vetenskaper, Matematisk statistik
José Sánchez
Chalmers, Matematiska vetenskaper
Göteborgs universitet
Kemal Sanli
Göteborgs universitet
Kaisa Thorell
Karolinska Institutet
Proteomics
1615-9853 (ISSN) 1615-9861 (eISSN)
Vol. 16 18 2454-2460Ämneskategorier
Annan biologi
Bioinformatik och systembiologi
Datavetenskap (datalogi)
DOI
10.1002/pmic.201600034
PubMed
27528420