NLM DIR Seminar Schedule
UPCOMING SEMINARS
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July 15, 2025 Noam Rotenberg
Cell phenotypes in the biomedical literature: a systematic analysis and the NLM CellLink text mining corpus
RECENT SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 1, 2025 Yoshitaka Inoue
Graph-Aware Interpretable Drug Response Prediction and LLM-Driven Multi-Agent Drug-Target Interaction Prediction -
June 10, 2025 Aleksandra Foerster
Interactions at pre-bonding distances and bond formation for open p-shell atoms: a step toward biomolecular interaction modeling using electrostatics -
June 3, 2025 MG Hirsch
Interactions among subclones and immunity controls melanoma progression -
May 29, 2025 Harutyun Sahakyan
In silico evolution of globular protein folds from random sequences
Scheduled Seminars on Feb. 2, 2023
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Microorganism Classification and Identification (MiCId) is a workflow that uses high resolution tandem mass spectrometry data to rapid and accurate identifications/classifications of microbes in complex samples. MiCId identifies microorganisms by using reliably identified peptides as supporting evidence. The rapid identification/classification of microorganisms in the pipeline is built upon a peptide-centric database that allows for the fast retrieval of peptides and taxonomical information. The identification strategy employs a hierarchical approach starting with phylum level and then descending one level at a time.
To make data analysis accessible to a broader range of users, a graphical user interface (GUI) for MiCId’s workflow has been developed. The GUI allows user to simplify the user’s routine tasks: constructing peptide-centric microorganismal databases, constructing antibiotic resistance protein databases, performing microorganism identification along with the identification of proteins and antibiotic resistance proteins, and estimating sample’s microorganismal-biomass composition. The graphical user interface enables fast and automated data analysis and offers tools to visualize and export results.