NLM IRP Seminar Schedule
UPCOMING SEMINARS
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May 14, 2024 Stanley Liang
Knowledge-driven Latent Diffusion For COVID-19 Pneumonia Radiology Pattern Synthesis -
May 21, 2024 Ziynet Kesimoglu
TBD -
May 23, 2024 Leslie Ronish
TBD -
May 28, 2024 Harutyun Saakyan
TBD -
May 30, 2024 Deepak Gupta
TBD
RECENT SEMINARS
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May 9, 2024 Pascal Mutz
The Riboviria protein structurome expands virus protein annotation and highlights protein relations -
May 2, 2024 OPEN
TBD -
April 30, 2024 Wenya Rowe
The conformal central charge of the spin-1/2 XX model derived from long-chain asymptotics -
April 25, 2024 Ermin Hodzic
Condition-Aware Cell Type Deconvolution of Bulk Tissues -
April 16, 2024 Jaya Srivastava
Regulatory plasticity of the human genome
Scheduled Seminars on March 31, 2023
Contact NLM_IRP_Seminar_Scheduling@mail.nih.gov with questions about this seminar.
Abstract:
Fast and accurate identification of pathogenic bacteria along with the identification of antibiotic resistance proteins is of paramount importance for patient treatments and public health. Once pathogenic bacteria causing infections are identified swiftly along with their antibiotic resistance proteins (if present), proper treatment can be administered, which can increase patients’ survival rate and minimize improper use of antibiotics. Trustworthy biomass estimates, on the other hand, are critical for microbial community structure analyses that arise in almost every microbiome study. To address these important issues, we have developed MiCId, a mass-spectrometry-based proteomics workflow for rapid identification of microorganisms and antibiotic resistance proteins and estimation of biomass.
In this talk I will demonstrate that MiCId’s workflow for pathogen identification has a sensitivity between 83.2% - 93.9% when the proportion of false discoveries controlled at the 5%. For the identification of antibiotic resistance proteins, MiCId’s workflow has a sensitivity value around 85% (with a lower bound at about 72%) and a precision greater than 95%. In addition to having high sensitivity and precision, MiCId’s workflow is fast and portable, making it a valuable tool for rapid identifications of bacteria as well as for detection of their antibiotic resistance proteins. It performs microorganismal identifications, protein identifications, sample biomass estimates, and antibiotic resistance protein identifications in 6−17 min per MS/MS experiment using computing resources that are available in most desktop and laptop computers.