MimoSA: a system for minimotif annotation

Authors

Jay Vyas, University of Connecticut Heath Center,Follow
Ronald J. Nowling, University of Connecticut Health CenterFollow
Thomas Meusburger, University of Nevada, Las VegasFollow
David P. Sargeant, University of Nevada, Las VegasFollow
Krishna Kadaveru, University of Connecticut Health CenterFollow
Michael R. Gryk, University of Connecticut Health CenterFollow
Vamsi Kundeti, University of Connecticut - StorrsFollow
Sanguthevar Rajasekaran, University of Connecticut - StorrsFollow
Martin Schiller, University of Nevada, Las VegasFollow

Document Type

Article

Publication Date

5-16-2010

Publication Title

BMC Bioinformatics

Volume

11

Issue

328

First page number:

1

Last page number:

10

Abstract

BACKGROUND:

Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature.

RESULTS:

We have built the MimoSA application for minimotif annotation. The application supports management of the Minimotif Miner database, literature tracking, and annotation of new minimotifs. MimoSA enables the visualization, organization, selection and editing functions of minimotifs and their attributes in the MnM database. For the literature components, Mimosa provides paper status tracking and scoring of papers for annotation through a freely available machine learning approach, which is based on word correlation. The paper scoring algorithm is also available as a separate program, TextMine. Form-driven annotation of minimotif attributes enables entry of new minimotifs into the MnM database. Several supporting features increase the efficiency of annotation. The layered architecture of MimoSA allows for extensibility by separating the functions of paper scoring, minimotif visualization, and database management. MimoSA is readily adaptable to other annotation efforts that manually curate literature into a MySQL database.

CONCLUSIONS:

MimoSA is an extensible application that facilitates minimotif annotation and integrates with the Minimotif Miner database. We have built MimoSA as an application that integrates dynamic abstract scoring with a high performance relational model of minimotif syntax. MimoSA's TextMine, an efficient paper-scoring algorithm, can be used to dynamically rank papers with respect to context.

Keywords

Algorithms; Amino Acid Motifs; Animals; Artificial intelligence; Data mining; Data Mining/Methods; Databases; Protein; Humans; MimoSA; Minimotifs; Protein binding; Proteins/Chemistry; Proteins/Metabolism; Proteins—Metabolism; Sequence Analysis; Protein

Disciplines

Biochemistry | Life Sciences | Molecular Biology | Structural Biology

Language

English

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 License.

Repository Citation

Vyas, J., Nowling, R. J., Meusburger, T., Sargeant, D. P., Kadaveru, K., Gryk, M. R., Kundeti, V., Rajasekaran, S., Schiller, M. (2010). MimoSA: a system for minimotif annotation. BMC Bioinformatics, 11(328), 1-10.
http://dx.doi.org/10.1186/1471-2105-11-328