Compound mass spectrum information batch retrieval method based on R language

Abstract

The invention provides a compound mass spectrum information batch retrieval method based on the R language. By conducing data cleaning, extracting position labels and keywords, establishing an appropriate data capture mode and adjusting the data arrangement form, compound mass spectrum information in external public databases such as Mona can be rapidly retrieved in batches, the search time is effectively shortened while the rapid batch local retrieval of hundreds of compounds is realized, and required information can be extracted according to actual application requirements to generate a summary report convenient to read.

Description

technical field [0001] The invention belongs to the field of computers, in particular to a batch retrieval method for compound mass spectrum information based on R language. Background technique [0002] With the rapid development of high-resolution mass spectrometry in recent years, a large number of public (including international co-constructed), commercial, and internal large-scale compound databases have emerged as the times require, and are widely used in life sciences, the environment, medicine, agriculture, and food science. all aspects of the research. Among them, the most commonly used public databases include Pubchem database, Chemspider database, Mona database, etc. The information of these databases comes from knowledge co-construction and sharing around the world. Retrieval, transfer and even understanding and application of comprehensive information. [0003] Take the Mona (Mass Bank of America) database as an example. This mass spectrometry database is a pu...

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Problems solved by technology

[0005] At present, the commonly used search methods for compound mass spectrometry databases and their defects include: (1) search one by one for the limited information of a single compound on the overseas website corresponding to the database (such as https: / / pubchem.ncbi.nlm.nih.gov / ; such as http: / / mona.fiehnlab.ucdavis.edu / )

Its disadvantages are: all the servers of the above-mentioned public databases are located abroad, and the search speed is greatly limited by the network speed, which often fails; only a single compound search is supported, the process is very cumbersome and the efficiency is extremely low; the rich experimental data cannot be re-deep to dig

Its disadvantages are: figure 1 As shown, the localization of this data structure solves the problem of limited retrieval of overseas databases, but it is completely unsuitable for manual reading, retrieval, and data mining.

If you still follow the traditional method, manually search one by one from overseas databases containing millions or even tens of millions of information, which is time-consuming, labor-intensive and inefficient, and it is even more impossible to directly read and search for source code data manually

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