Sequence Data Analyzer Using Concatenation Strings to Eliminate Identifiers
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Solution Overview
Problem
The existing methods for handling paired-end read sequence data require large identifiers to link paired sequences, leading to increased data sizes and decreased computing efficiency, particularly with vast amounts of short reads where 4 billion patterns of identifiers result in significant data size and processing inefficiencies.
Innovation Solution
A sequence data analyzer is developed with a read dictionary preparation unit that creates concatenation strings from paired sequences, a query retrieval unit that searches for hit positions, and a sample reconstruction unit that extracts mate sequences from these dictionaries, eliminating the need for identifiers by using terminal characters and BW transformation to enhance data retrieval efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If identifiers are used to link paired read sequences, then pairing information can be handled, but data size increases and computing efficiency decreases
Solution Approach 1:
The invention extracts and eliminates the identifier component from the data structure, retaining only the essential pairing information through concatenation of read sequences. This removes the harmful element (identifiers consuming 4 bytes each) while preserving the useful function (linking paired reads).
Solution Approach 2:
The invention merges the left and right read sequences directly by concatenation, combining what was previously separate entities (identifier + sequence) into a single integrated structure. This eliminates the need for separate identifier storage and reduces overall data size.
2Reliability
If identifiers are used to link paired read sequences, then pairing information can be handled, but data size increases significantly
Solution Approach 1:
The invention extracts and removes the identifier portion from the data structure, keeping only the essential sequence data. This eliminates the harmful element (identifiers consuming 4 bytes each) while preserving the useful function (linking paired reads).
Solution Approach 2:
The invention merges the left and right read sequences directly by concatenation, combining what was previously separate entities (identifier + sequence) into a single integrated structure. This eliminates the need for separate identifier storage and reduces overall data size.
3Measurement precision
If conventional mapping methods are used, then genome mapping can be performed, but processing time increases
Solution Approach 1:
The invention performs preliminary concatenation of read sequences with terminal characters during data preparation, creating a pre-processed structure that enables faster querying. This preliminary organization of data allows subsequent mapping operations to execute more efficiently without sacrificing accuracy.
Data Source
AI summary
A sequence data analyzer comprising: a read dictionary preparation unit creating a read sequence dictionary based on a concatenation string, the concatenation string constituted of a pair of a left sequence and a right sequence, which are obtained by sequencing a sample DNA fragment respectively from the left and right ends, and connecting characters connecting these sequences together; and a sample reconstruction unit extracting, as a sample sequence, a string up to a terminal character positioned in the string of a hit position of a query sequence in the read sequence dictionary, and extracting, as a mate sequence, the left sequence or right sequence until the appearance of a terminal character on the side where the hit position doesn't exist in the sample sequence.