NLM DIR Seminar Schedule
UPCOMING SEMINARS
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April 8, 2025 Jaya Srivastava
Leveraging a deep learning model to assess the impact of regulatory variants on traits and diseases -
April 15, 2025 Pascal Mutz
TBD -
April 18, 2025 Valentina Boeva, Department of Computer Science, ETH Zurich
Decoding tumor heterogeneity: computational methods for scRNA-seq and spatial omics -
April 22, 2025 Stanley Liang
TBD -
April 29, 2025 MG Hirsch
TBD
RECENT SEMINARS
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April 1, 2025 Roman Kogay
Horizontal transfer of bacterial operons into eukaryote genomes -
March 25, 2025 Yifan Yang
Adversarial Manipulation and Data Memorization in Large Language Models for Medicine -
March 11, 2025 Sofya Garushyants
Tmn – bacterial anti-phage defense system -
March 4, 2025 Sanasar Babajanyan
Evolution of antivirus defense in prokaryotes depending on the environmental virus load -
Feb. 25, 2025 Zhizheng Wang
GeneAgent: Self-verification Language Agent for Gene Set Analysis using Domain Databases
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.