A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: A case study of lipase identification.

The growing adoption of enzymes as biocatalysts in various industries has accentuated the demand for acquiring access to the great natural diversity and, in the meantime, the advent and advancements of metagenomics and high-throughput sequencing technologies have offered an unprecedented opportunity to explore this extensive resource. Lipases, enzymes responsible for the biological turnover of lipids, are among the most commercialized biocatalysts with numerous applications in different domains and therefore are of high industrial value. The relatively costly and time-consuming wet-lab experimental pipelines commonly used for novel enzyme discovery, highlight the necessity of agile in silico approaches to keep pace with the exponential growth of available sequencing data. In the present study, an in-depth analysis of a tannery wastewater metagenome, including taxonomic and enzymatic profiling, was performed. Using sequence homology-based screening methods and supervised machine learning-based regression models aimed at prediction of lipases' pH and temperature optima, the metagenomic data set was screened for lipolytic enzymes, which led to the isolation of alkaline and highly thermophilic novel lipase. Moreover, MeTarEnz (metagenomic targeted enzyme miner) software was developed and made freely accessible (at https://cbb.ut.ac.ir/MeTarEnz) as a part of this study. MeTarEnz offers several functions to automate the process of targeted enzyme mining from high-throughput sequencing data. This study highlights the competence of computational approaches in exploring vast biodiversity within environmental niches, while providing a set of practical in silico tools as well as a generalized methodology to facilitate the sequence-based mining of biocatalysts.

CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data.

Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level. In this study, we introduce CAMAMED as a composition-aware mapping-based metagenomic data analysis pipeline. This pipeline can analyze metagenomic samples at both taxonomic and functional profiling levels. Using this pipeline, metagenome sequences can be mapped to non-redundant gene catalogs and the gene frequency in the samples are obtained. Due to the highly compositional nature of metagenomic data, the cumulative sum-scaling method is used at both taxa and gene levels for compositional data analysis in our pipeline. Additionally, by mapping the genes to the KEGG database, annotations related to each gene can be extracted at different functional levels such as KEGG ortholog groups, enzyme commission numbers and reactions. Furthermore, the pipeline enables the user to identify potential biomarkers in case-control metagenomic samples by investigating functional differences. The source code for this software is available from https://github.com/mhnb/camamed. Also, the ready to use Docker images are available at https://hub.docker.com.