Distributed accelerating method and system for open type protein identification

Abstract

The invention discloses a distributed accelerating method and system for open type protein identification. The distributed accelerating system for open type protein identification comprises a protein index establishing module, a spectrogram data preparation module, a query marking module and a result summarizing and output module. According to the distributed accelerating method and system for open type protein identification, cluster resources can be effectively utilized, a user is allowed to conduct protein identification on the premise that the types of enzyme digestion and grooming are not appointed in a large protein bank or any type of enzyme digestion and grooming is appointed, and protein map identification speed and analysis rate are effectively improved.

Description

technical field [0001] The invention relates to protein spectrum identification in the field of biological information, in particular to creation and retrieval of a distributed index of an open protein identification engine. Background technique [0002] On February 15, 2001, when "Nature" released the framework map of the human genome, it also published the news of the establishment of the Human Proteome Organization (HUPO). Scientists realized that understanding proteins -- the products of genes -- is crucial to truly unraveling the genetic mysteries of life. Proteomics studies the characteristics of proteins on a large scale, including protein expression levels, post-translational modification studies, and protein-protein interactions. In recent years, innovations in mass spectrometry have played a key role in promoting proteomics research. [0003] The qualitative and quantitative analysis of proteins using mass spectrometry data has become one of the core contents of ...

AI Technical Summary

Problems solved by technology

The parallel version usually adopts the simplest spectrum splitting strategy, that is, each node identifies a part of the spectrum, and the identification speed is still limited by a single machine

[0014] Obviously, with the rapid growth of large-scale protein databases, if factors such as post-translational modifications and enzyme cleavage specificity are considered, it will lead to an explosion of search space combinations, and the high-configuration internal memory of a single machine cannot effectively cope with the huge amount of open identification. Space requirements, greatly reducing the speed of database searches, while leading to an increase in false positive search results

[0015] In short, due to the limitation of storage space and computing power, the current protein identification system is difficult to effectively complete open identification, that is, support large protein library, non-specific digestion and arbitrary modification

In addition, most practical engines are still in stand-alone mode, which cannot effectively take advantage of resources such as clusters, so it is necessary to design and develop effective parallel systems

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