Robust Accurate Identification and Biomass Estimates of Microorganisms via Tandem Mass Spectrometry
Gelio Alves, Yi-Kuo Yu National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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Yu Yi-Kuo (Presenter) National Center for Biotechnology Information
Presenter Bio: Trained as a theoretical physicist, Dr. Yi-Kuo Yu currently leads the
quantitative molecular biological physics group at the National Center for Biotechnology Information, NIH. His group investigates various biological problems at multiple levels of detail in order to gain quantitative understanding in biology. A major goal of his group is to foster a solid connection between medical research and fundamental scientific research.
Relevant Financial Disclosures
(within past 24 months)
No relevant financial relationship(s) to disclose.
Abstract
Rapid and accurate identification of microorganisms and their biomass
estimates are of extreme importance to public health and safety. Mass spectrometry
has become an important technique for these purposes. Previously
we published a microorganism identification method, MiCId v.12.26.2017, that
was shown to perform no worse than other workflows. We present in this talk
MiCId v.12.06.2018 that, in comparison with the earlier version v.12.26.2017,
provides an additional feature for relative biomass estimates, better interprets
analysis results, provides more accurate microorganism identifications,
and successfully reduces the number of false positives. This significant advance
is made possible by several new ingredients introduced: first, we apply
a modified expectation-maximization method to compute for each taxon
considered a prior probability, which can be used for relative biomass estimate;
second, we introduce a new concept called ownership, through which
the participation ratio is computed and used it as the number of taxa to
be kept within a cluster; third, based on confidently identified peptides, we
calculate for each taxon its degree of independence from the rest of taxa
considered to determine whether or not to split this taxon off the cluster.
Using a large dataset, we show that, in comparison with v.12.26.2017, version
v.12.06.2018 yields superior sensitivity and specificity. We also show that
MiCId v.12.06.2018 can estimate species biomass reasonably well. The new
MiCId v.12.06.2018 is freely available for download at https://www.ncbi.
nlm.nih.gov/CBBresearch/Yu/downloads.html.
= Discovery stage.
= Translation stage.
= Clinically available.
