2020 C-DEBI Virtual Annual Meeting Participants

Previous Virtual Meetings

2021 Virtual Meeting Series

Friday May 7, 2021 (10:30AM – 12:00PM Pacific Time)
New Microbial Physiology Techniques

Roland Hatzenpichler, Montana State University

Next-generation physiology: Why and how to measure microbial phenotypes under (close to) in situ conditions

We will discuss cutting-edge approaches to measure microbial phenotypes and metabolic activities under as close to in situ conditions as experimentally possible. We will focus on non-destructive techniques capable of observing individual bacterial and archaeal cells that can be followed up by additional methods to further characterize cells of interest. Specifically, we will discuss how substrate analog probing and stable isotope labeling techniques, in combination with fluorescence- or Raman microscopy-based observation of intact cells, can test hypotheses generated via metagenomics and lead us towards a deeper understanding of microbial ecophysiology. Maybe more excitingly, we will discuss how these techniques can be employed to turn traditional workflows upside down and design experiments in which the phenotype of a cell is examined first and the genotype second.

 

Monday April 5, 2021 (10:30AM – 12:00PM Pacific Time)
New Tools in Bioinformatics

Jake Weissman, University of Southern California

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator (gRodon) and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity (the EGGO database); extensions allow estimates from 16S sequences alone as well as weighted community estimates from metagenomes.

Ben Tully, University of Southern California

EukMetaSanity: A customizable workflow for the gene prediction and annotation of Eukaryotic genomes

The prediction of protein-coding regions in eukaryotic genomes requires multiple computationally and time intensive processes to accurately determine gene structure. Generally, eukaryotic gene prediction relies on a combination of transcript evidence, protein homology, and/or sequence signatures. EukMetaSanity incorporates the steps, repeat prediction, ab initio gene prediction, and evidence-based gene prediction into a single pipeline that can determine high-quality intron/exon boundaries for novel eukaryotic genomes. Along with an implementation that takes advantage of the distributed structure of high-performance computing clusters, EukMetaSanity has the ability to annotate 100-1,000s of eukaryotic genomes/MAGs/SAGs in short time-scales relative to current implementations and approaches. And while the need for this level of parallelization is not commonplace, recent marine eukaryotic MAG datasets suggest that practical solutions at scale will be necessary soon.


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