title: MimoSA: a system for minimotif annotation.
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.
authors.affiliation: Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.
authors.forename: Jay
authors.initials: J
authors.lastname: Vyas
authors.forename: Ronald J
authors.initials: RJ
authors.lastname: Nowling
authors.forename: Thomas
authors.initials: T
authors.lastname: Meusburger
authors.forename: David
authors.initials: D
authors.lastname: Sargeant
authors.forename: Krishna
authors.initials: K
authors.lastname: Kadaveru
authors.forename: Michael R
authors.initials: MR
authors.lastname: Gryk
authors.forename: Vamsi
authors.initials: V
authors.lastname: Kundeti
authors.forename: Sanguthevar
authors.initials: S
authors.lastname: Rajasekaran
authors.forename: Martin R
authors.initials: MR
authors.lastname: Schiller
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000818:Animals; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods; D030562:Databases, Protein; D006801:Humans; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein
chemical_list: D011506:Proteins