HTAPP: High-throughput autonomous proteomic pipeline

被引:27
作者
Yu, Kebing [1 ]
Salomon, Arthur R. [1 ,2 ,3 ]
机构
[1] Brown Univ, Dept Chem, Providence, RI 02903 USA
[2] Brown Univ, Dept Mol Biol Cell Biol & Biochem, Providence, RI 02903 USA
[3] Brown Univ, Ctr Genom & Prote, Providence, RI 02903 USA
基金
美国国家卫生研究院;
关键词
Automation; Bioinformatics; Laboratory information management system; MS/MS database search; Peptide analysis; Relational database; MASS-SPECTROMETRY; PROTEIN IDENTIFICATION; PHOSPHOPROTEOMIC ANALYSIS; TANDEM; PLATFORM; DATABASE; PEPTIDE; PERFORMANCE; SEQUENCES;
D O I
10.1002/pmic.200900159
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Recent advances in the speed and sensitivity of mass spectrometers and in analytical methods, the exponential acceleration of computer processing speeds, and the availability of genomic databases from an array of species and protein information databases have led to a deluge of proteomic data. The development of a lab-based automated proteomic software platform for the automated collection, processing, storage, and visualization of expansive proteomic data sets is critically important. The high-throughput autonomous proteomic pipeline described here is designed from the ground up to provide critically important flexibility for diverse proteomic workflows and to streamline the total analysis of a complex proteomic sample. This tool is composed of a software that controls the acquisition of mass spectral data along with automation of post-acquisition tasks such as peptide quantification, clustered MS/MS spectral database searching, statistical validation, and data exploration within a user-configurable lab-based relational database. The software design of high-throughput autonomous proteomic pipeline focuses on accommodating diverse workflows and providing missing software functionality to a wide range of proteomic researchers to accelerate the extraction of biological meaning from immense proteomic data sets. Although individual software modules in our integrated technology platform may have some similarities to existing tools, the true novelty of the approach described here is in the synergistic and flexible combination of these tools to provide an integrated and efficient analysis of proteomic samples.
引用
收藏
页码:2113 / 2122
页数:10
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