PQL: Protein Query Language

被引:0
|
作者
Elfayoumy, Sherif [1 ]
Bathen, Paul [1 ]
机构
[1] Univ North Florida, Sch Comp, Jacksonville, FL 32224 USA
来源
2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 2 | 2012年
关键词
Proteomics; Bioinformatics; Query Languages; Protein Query Language;
D O I
10.1109/ICMLA.2012.217
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper introduces a Protein Query Language (PQL) for querying protein structures in an expressive yet concise manner. One of the objectives of the paper is to demonstrate how such a language would be beneficial to protein researchers to obtain in-depth protein data from a relational database without extensive SQL knowledge. The language features options such as limiting query results by key protein characteristics such as methyl donated hydrogen bond interactions, minimum and maximum phi and psi angles, repulsive forces, CH/Pi calculations, and other pertinent factors. A backend data model was designed to support storage and retrieval of protein primary and secondary sequences, atomic level data, as well as calculations on said data. A relational DBMS is used as the persistent storage backend, with every effort made to ensure transparent portability to most relational database systems. In addition, front end applications can be developed to support retrieving, transforming, and preprocessing of information from the Research Collaboratory for Structural Bioinformatics (RCSB) into the backend data repository. The new language and associated architecture allow users to load additional protein files from RCSB into the database, issue standard queries to download pertinent data in user-friendly formats including CSV files, issue non-standard queries against secondary structures via the protein query language, and run error detection routines against data in the database. Query results may include normalized or denormalized data, model and chain data, residue data, atom detail data, and primary as well as secondary structure data.
引用
收藏
页码:127 / 132
页数:6
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