TIDD
TIDD (Tool for Increasing Discriminability of Database search results) is a universal post-processing software tool to improve the confidence of peptide identifications in shotgun proteomics. It works with results from any database search engine, including newly developed ones, without needing search engine-specific optimization.
Key features of TIDD:
1. Universality: TIDD calculates universal features to assess the quality of peptide-spectrum matches (PSMs), making it compatible with search engine results.
2. Flexibility: Besides universal features, TIDD allows the incorporation of additional features provided by search engines or users.
3. Performance: Despite relying on universal features, TIDD demonstrates similar or better performance compared to Percolator, a popular post-processing tool, in terms of peptide identification.
4. Improved identification: TIDD identified 10.23-38.95% more PSMs than target-decoy estimation for MSFragger, a search engine not supported by Percolator.
5. User-friendly interface: TIDD offers a simple graphical user interface for ease of use.
Topic
Proteomics;Proteomics experiment;Small molecules;Machine learning
Detail
Operation: Target-Decoy;Tag-based peptide identification;de Novo sequencing
Software interface: Command-line interface. graphical user interface
Language: Java,R
License: Not stated
Cost: Free of charge
Version name: -
Credit: National Research Foundation of Korea, Institute of Information & communications Technology Planning & Evaluation (IITP), Korea government (MSIT).
Input: -
Output: -
Contact: -
Collection: -
Maturity: -
Publications
- TIDD: tool-independent and data-dependent machine learning for peptide identification.
- Li H, et al. TIDD: tool-independent and data-dependent machine learning for peptide identification. TIDD: tool-independent and data-dependent machine learning for peptide identification. 2022; 23:109. doi: 10.1186/s12859-022-04640-y
- https://doi.org/10.1186/S12859-022-04640-Y
- PMID: 35354356
- PMC: PMC8969291
Download and documentation
Documentation: https://docs.google.com/document/d/168oGySS15xrobeqTY54_QLMgWQofnPIApx6ma5ntGa8/edit
Home page: https://github.com/HanyangBISLab/TIDD.git
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