Compressing Fragment-Ion Data for Proteomics Search
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Solution Overview
Problem
Current peptide search algorithms in shotgun proteomics face significant memory overhead and computational challenges due to large search spaces, leading to increased search times and memory consumption, especially with the use of unrestricted peptide search methods.
Innovation Solution
A custom data structure, referred to as the Compact Fragment-Ion Index Representation (CFIR), is employed to compress repetitive and redundant information in fragment-ion data, reducing memory footprint and entropy, and using a running counter technique for encoding, allowing for efficient indexing and search operations without the need for decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If unrestricted peptide search methods are used, then peptide identification rates increase, but search times increase due to massively increased number of spectral comparisons
Solution Approach 1:
The patent applies preliminary action by performing database filtration methods (peptide precursor mass filtering, sequence-tagging, and shared-peak counting) before the main peptide search process. This pre-processing reduces the search space to only relevant entries, allowing unrestricted search methods to be applied efficiently to a smaller subset of data, thereby maintaining high peptide identification rates while reducing overall search time
2Loss of time
If database filtration methods are used to reduce search space, then search time decreases, but memory read/write and caching overheads increase
Solution Approach 1:
The patent applies parameter changes by transforming the data representation format from traditional uncompressed structures to a compressed format that stores fragment-ion data more efficiently. This transformation changes the physical storage parameters (reducing memory footprint by 50% or more) while maintaining the logical data structure needed for filtration operations, thereby reducing memory overhead without sacrificing search time efficiency
3Ease of operation
If traditional uncompressed index structures are used, then search operations are simple, but memory consumption is very high
Solution Approach 1:
The patent transforms the index structure from uncompressed to compressed format, changing the storage parameters while maintaining search functionality. The compressed structure achieves 50% or more reduction in memory consumption by eliminating redundant information, while the search operations remain equally simple because the compressed format preserves the essential data relationships needed for efficient querying
4Loss of information
If fragment-ion data is stored in detail, then data completeness is maintained, but index size becomes very large
Solution Approach 1:
The patent applies parameter changes by implementing a compressed data structure that transforms the storage parameters of fragment-ion data. The compression technique eliminates redundancy while preserving all essential information needed for peptide search operations, achieving 50% or more reduction in index size without loss of data completeness or search accuracy
Data Source
AI summary
Methods and systems for compressing data, such as ion-mass information data in mass spectrometry spectra, to reduce index size are provided. Data in an index, such as fragment-ion data in a fragment-ion index, can be transformed for reduction of entropy and then encoded using a running counter technique to compress repetitive and redundant information in the index.