Author: Mathias Kuhring; Joerg Doellinger; Andreas Nitsche; Thilo Muth; Bernhard Y. Renard
Title: An iterative and automated computational pipeline for untargeted strain-level identification using MS/MS spectra from pathogenic samples Document date: 2019_10_24
ID: k7hm3aow_24
Snippet: TaxIt demonstrates superior performance with respect to strain-level identification and computational expense. In summary, our approach can be used to unambiguously identify candidate organisms of all samples down to a low taxonomic level, with the minor exception of a tie for the bronchitis sample. In contrast, the unique-PSMs-based identification strategy is repeatedly deficient at the strain level or features highly ambivalent results. Further.....
Document: TaxIt demonstrates superior performance with respect to strain-level identification and computational expense. In summary, our approach can be used to unambiguously identify candidate organisms of all samples down to a low taxonomic level, with the minor exception of a tie for the bronchitis sample. In contrast, the unique-PSMs-based identification strategy is repeatedly deficient at the strain level or features highly ambivalent results. Furthermore, Pipasic frequently favors an incorrect strain or even an All rights reserved. No reuse allowed without permission.
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