Strategies to improve usability and preserve accuracy in biological sequence databases
Journal article, 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.
Databases
Annotation
Sequencing
Standards
Functional prediction
Author
Johan Bengtsson-Palme
University of Gothenburg
Fredrik Boulund
Chalmers, Mathematical Sciences, Mathematical Statistics
University of Gothenburg
Robert Edström
Chalmers, Computer Science and Engineering (Chalmers)
Amir Feizi
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Anna Johnning
Chalmers, Mathematical Sciences, Mathematical Statistics
University of Gothenburg
Viktor Jonsson
Chalmers, Mathematical Sciences, Mathematical Statistics
University of Gothenburg
Fredrik Karlsson
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
C. Pal
University of Gothenburg
Mariana Buongermino Pereira
Chalmers, Mathematical Sciences, Mathematical Statistics
University of Gothenburg
Anna Rehammar
Chalmers, Mathematical Sciences, Mathematical Statistics
University of Gothenburg
José Sánchez
University of Gothenburg
Chalmers, Mathematical Sciences
Kemal Sanli
University of Gothenburg
Kaisa Thorell
Karolinska Institutet
Proteomics
1615-9853 (ISSN) 1615-9861 (eISSN)
Vol. 16 18 2454-2460Subject Categories (SSIF 2011)
Other Biological Topics
Bioinformatics and Systems Biology
Computer Science
DOI
10.1002/pmic.201600034
PubMed
27528420