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

Functional prediction

Standards

Sequencing

Annotation

Author

Johan Bengtsson-Palme

University of Gothenburg

Fredrik Boulund

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

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

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

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

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

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 2454-2460

Subject Categories

Other Biological Topics

Bioinformatics and Systems Biology

Computer Science

DOI

10.1002/pmic.201600034

PubMed

27528420

More information

Latest update

2/21/2018