Prokaryotic Super Program Advisory Committee DOE Joint Genome Institute, Walnut Creek, CA, March 27, 2013

Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction

This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of four supervised machine learning algorithms, K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), to accurately classify Enterococcus species. This study involved a comprehensive dataset of 410 strains, generating 1640 individual spectra through on-plate and off-plate protein extraction methods. Although the commercial database correctly identified 76.9% of the strains, machine learning classifiers demonstrated superior performance (accuracy 0.991). In the RF model, top informative peaks played a significant role in the classification. Whole-genome sequencing showed that the most informative peaks are biomarkers connected to proteins, which are essential for understanding bacterial classification and evolution. The integration of MALDI-TOF MS and machine learning provides a rapid and accurate method for identifying Enterococcus species, improving healthcare and food safety.

EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways

Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user's gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

A New Genomics-Driven Taxonomy of Bacteria and Archaea: Are We There Yet?

Taxonomy is often criticized for being too conservative and too slow and having limited relevance because it has not taken into consideration the latest methods and findings. Yet the cumulative work product of its practitioners underpins contemporary microbiology and serves as a principal means of shaping and referencing knowledge. Using methods drawn from the field of exploratory data analysis, this minireview examines the current state of the field as it transitions from a taxonomy based on 16S rRNA gene sequences to one based on whole-genome sequences and tests the validity of some commonly held beliefs.

Fifteen years of physiological proteo(geno)mics with (marine) environmental bacteria

Environmental bacteria play a central role in the Earth's elemental cycles and represent a mostly untapped reservoir for novel metabolic capacities and biocatalysts. Over the last 15 years, the author's laboratory has focused on three major switches in the breakdown of organic carbon defined by the abundance and recalcitrance of the substrates: carbohydrates and amino acids by aerobic heterotrophs, fermentation end products by sulphate reducers and anaerobic degradation of aromatic compounds and hydrocarbons by denitrifiers and sulphate reducers. As these bacteria are novel isolates mostly not accessibly by molecular genetics, genomics combined with differential proteomics was early on applied to obtain molecular-functional insights into degradation pathways, catabolic and regulatory networks, as well as mechanisms and strategies for adapting to changing environmental conditions. This review provides some background on research motivations and briefly summarizes insights into studied model organisms, e.g. "Aromatoleum aromaticum" EbN1, Desulfobacula toluolica Tol2 and Phaeobacter inhibens DSM 17395.