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_12
Snippet: After count adjustment, the most dominant candidate taxon is selected as the most likely species or strain, respectively. In the final step of the first iteration, the selected species is used to retrieve strain-level data for the strain identification in the second iteration. Once more, we rely on the NCBI Taxonomy and infer all available strains for the candidate species via the nodes dump file. Next, strain proteins are automatically downloade.....
Document: After count adjustment, the most dominant candidate taxon is selected as the most likely species or strain, respectively. In the final step of the first iteration, the selected species is used to retrieve strain-level data for the strain identification in the second iteration. Once more, we rely on the NCBI Taxonomy and infer all available strains for the candidate species via the nodes dump file. Next, strain proteins are automatically downloaded from NCBI Protein using the NCBI Entrez API 54 in combination with the jsoup: Java HTML Parser 55 . This includes all available RefSeq as well as non-RefSeq sequences since the availability of curated strain material is often limited. Finally, the obtained protein sequences are merged into a single database and redundant entries are removed using seqkit rmdup 56 .
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