Computational Analysis of glycoside hydrolase genes in metagenomic data from composting

Abstract

There is growing interest from industry sectors in substituting enzymes for chemical catalyzers in processes that occur under high temperature, pressure, salt concentration or metal concentration. One of the critical sectors is biofuel production from renewable biomass. Composting is a process that is particularly amenable to bioprospection of microbial products related to biomass degradation. The main objective of this work is to characterize the diversity and abundance of glycoside hydrolases (GHs) in different stages of the composting process used in the São Paulo Zoo, using as basic data the metagenome sequences generated under the FAPESP project number 11/50870-6. Specifically we plan to identifiy GH-coding genes in several samples; anlyze diversity and abundance of the main GH families; anlyze diversity and abundance of the microbes whose genome code for these GHs; verify which of the identified GHs are new with respect to known ones; and compare GH profiles of the various samples to each other and to other biomass degrading metagenomes. In order to achieve these objectives we will use 3rd party servers and software as well as build our own computational analysis pipeline and relational database.