Functional-Driven Single-Cell Genomics: Cellulose Degrading Mining
Bryan Rangel Alvarez2, Devin Doud, PhD1, and Tanja Woyke, PhD1; Microbial Genomics Program, DOE Joint Genome Institute1; School of Natural Sciences, University of California, Merced2
The “Great Plate-Count Anomaly” depicted the world’s skewed understanding of microbial metabolism and the rising gap between cultivated and uncultivated Bacteria. This gap reflects the limited amount of reference Phyla that are currently available. Cultured methods have long been implemented in accessing the genetic material from a diverse microbial community. Yet, these methods are limited in recovering precise genomes from the environmental species since not everything is known how to be cultivated. Single-cell genomics takes a culture independent approach, returning improved context for functional genes and pathways. By coupling single-cell genomics with an initial function-base selection, we aimed to create a method with high throughput and navigation within the microbial dark matter. We probed for organisms with specific cellulose decomposition function by sorting through their physical adherence to crystalline cellulose. With this method we aim to discover new life lineages. Within the candidate lineages we would probe for novel glycosidic degraders to augment bioenergy production.
The Discovery of Novel Phyla Through the Use of Existing Taxonomic Classifying Software
Cristhian A. Gutierrez2, Jeff Froula1, and Zhong Wang, PhD1; Joint Genome Institute1; School of Natural Sciences, University of California, Merced2
With the upcoming of metagenomics, many argue the credibility of the Tree of Life and our current method of taxonomic classification. Some, like Professor Didier Raoult from the University of the Mediterranean, outright argue that there is no universal tree.1 However, others say that the Tree allows us to analyze a physical representation of Evolution and ancient life2 and provides a means to classify and sort organisms. Placing organisms in hierarchical categories helps scientists understand unique physical features, biological function, and phylotypic genetic compositions. These taxonomic categories allow us to highlight similarities and differences amongst known organisms. By fully understanding the distinguishing factors between taxa, we hope to classify and best organize unknown organisms. Here, we present a preliminary method that we hope will provide the framework in classification of novel life. Using supervised classifying methods we aim to filter known genomes from metagenomic datasets— thus providing a list of candidate novel, unclassified genomes. This process will help you sort through large datasets and extract candidate genomes, which can be further tested and examined.