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Strategies to improve usability and preserve accuracy in biological sequence databases

Journal article
Authors Johan Bengtsson-Palme
Fredrik Boulund
Robert Edström
Amir Feizi
Anna Johnning
Viktor Jonsson
Fredrik H. Karlsson
Chandan Pal
Mariana Buongermino Pereira
Anna Rehammar
José Sánchez
Kemal Sanli
Kaisa Thorell
Published in Proteomics
Volume 16
Issue 18
Pages 2454–2460
ISSN 1615-9853
Publication year 2016
Published at Department of Mathematical Sciences
Department of Mathematical Sciences, Mathematical Statistics
Institute of Biomedicine
Department of Biological and Environmental Sciences
Core Facilities, Bioinformatics
Institute of Biomedicine, Department of Infectious Medicine
Pages 2454–2460
Language en
Keywords Annotation, Databases, Functional prediction, Sequencing, Standards
Subject categories Databases, Bioinformatics and Systems Biology, Functional genomics


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.

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