Seaching method of genome sequence data based on characteristic

Abstract

The invention relates to an approach of searching gene group sequence database based on characters. Approximate sequence is searched in database according to sequence statistic character. The search method is as follows: search approximate sequence according to distance of statistic character, namely, basic information of different species' sequence group serial data, including sequence's database login number, species name, chromosome number, original data, basic group composition character and base pair relativity character is memorized in database. For any a gene snippet submitted by client, its eigenvalue is computed according to client's request and distance between the eigenvalue and all corresponding eigenvalue in database is computed to compare approximate sequence; the most approximate sequence is arranged and displayed by distance.

Description

technical field [0001] The invention is a feature-based search method for a genome sequence database, more precisely, a method for searching approximate sequences within the scope of the database according to the statistical characteristics of the sequence. Background technique [0002] Along with the implementation and completion of the Human Genome Project (HGP), the Model Organisms Genome Project and the Microbial Genome Project (MGP) are also in progress. At present, the work on the structural genome has been basically completed, but for the study of the entire genome, obtaining the sequence is only the first step. Collect, organize, and retrieve these sequences like a bible, and analyze the sequence and the structure and function of the expressed protein The ultimate goal is to find out the rules and uncover the secrets of life. These efforts are the tasks of the so-called post-genome era. Bioinformatics is an emerging science that has emerg...

AI Technical Summary

Problems solved by technology

For small fragment sequences, this method can be said to be relatively good, but it cannot handle long sequence fragments, and the method of sequence alignment only considers the similarity of two sequences in alphabetical order, although the current theory believes that similar The sequences have similar structures and functions, but the results of this analysis make it possible for us to miss sequences that are functionally similar but not very similar in alphabetical order when we want to find sequences with similar functions

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