Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.
BMC Bioinformatics. 2010 Jun 16;11:328. doi: 10.1186/1471-2105-11-328.
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.
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.
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.
最小基序是一个蛋白质内的短肽序列，被其他蛋白质或分子识别。虽然现在有几个最小基序数据库，但它们并不完整。有报道称，许多最小基序在原始文献中尚未被注释，而全新的最小基序仍在每周发表。我们最近提出的最小基序的功能和序列语法使我们能够构建一个通用工具，方便从生物医学文献中对最小基序数据进行结构化注释和管理。
我们已经构建了最小基序注释的 MimoSA 应用程序。该应用程序支持 Minimotif Miner 数据库的管理、文献跟踪以及新最小基序的注释。MimoSA 使 MnM 数据库中的最小基序及其属性的可视化、组织、选择和编辑功能成为可能。对于文献部分，Mimosa 通过基于词相关性的免费可用机器学习方法提供论文状态跟踪和评分，以进行注释。论文评分算法也可以作为单独的程序 TextMine 使用。最小基序属性的表单驱动注释使新最小基序能够输入到 MnM 数据库中。几个支持功能提高了注释的效率。MimoSA 的分层架构通过分离论文评分、最小基序可视化和数据库管理的功能来实现可扩展性。MimoSA 很容易适应其他手动将文献整理到 MySQL 数据库中的注释工作。
MimoSA 是一个可扩展的应用程序，它方便了最小基序的注释，并与 Minimotif Miner 数据库集成。我们构建了 MimoSA，它将动态摘要评分与最小基序语法的高性能关系模型集成在一起。MimoSA 的 TextMine 是一种高效的论文评分算法，可以根据上下文动态地对论文进行排名。
