Peptide Source Assignment via FDR-Based Database Prioritization
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Solution Overview
Problem
In bioinformatics, assigning a putative source to de novo peptide sequences without experimental confirmation is challenging due to the reliability and timeliness of data sources, and existing methods struggle to accurately identify the origin of peptides, especially those that may be cis- or trans-spliced.
Innovation Solution
A method that orders peptide source search steps based on increasing random hit rates, using databases like the expanded human proteome and non-endogenous proteome, and applies simulated random queries to determine false discovery rates, facilitating a query support data structure for improved peptide source assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple databases are searched to identify peptide sources, then the comprehensiveness of source identification is improved, but the complexity of the search process increases
Solution Approach 1:
The search process is segmented into multiple independent database searches (human proteome, non-endogenous proteome, etc.), each handled separately with its own random hit rate calculation. This segmentation allows comprehensive coverage while managing complexity through modular processing.
Solution Approach 2:
Random peptide sequences are generated in advance to calculate random hit rates for each database before actual peptide identification. This preliminary action enables systematic evaluation of each database's reliability without requiring experimental confirmation, establishing a framework for confident source assignment.
2Productivity
If search results are evaluated without considering false discovery rates, then the speed of analysis is improved, but the reliability of source assignment decreases
Solution Approach 1:
Random hit rates are calculated for each database using simulated random peptide sequences, providing feedback on the reliability of each search source. This feedback mechanism enables systematic evaluation of false discovery rates, allowing confident source assignment while maintaining efficient analysis through automated FDR-based filtering.
3Measurement precision
If experimental confirmation is required for source assignment, then the accuracy of peptide origin identification is improved, but the time required for analysis increases
Solution Approach 1:
Random peptide sequences are generated in advance to calculate random hit rates for each database before actual peptide identification. This preliminary action establishes reliable FDR benchmarks that enable accurate source assignment without requiring time-consuming experimental confirmation, as the framework is pre-configured with reliability metrics.
Solution Approach 2:
The mechanical process of experimental confirmation is replaced with computational FDR-based evaluation. By substituting physical experimentation with algorithmic assessment of random hit rates, the system achieves comparable or superior identification accuracy while dramatically reducing analysis time through in silico evaluation.
Data Source
AI summary
Methods and systems are described for optimizing search results through querying a plurality of databases according to false discovery, random hit rates are presented herein. Methods and systems adapted to assigning a putative source to a de novo peptide sequence and/or creating a workflow for performing said assignment are presented herein.