XenMetaDB

The pesticides belong to a group of chemicals used worldwide as herbicides, insecticides etc. for controlling weeds, pests and diseases in crops. Positive aspect of pesticide application includes enhancement in crop/food productivity and significant decrease in vector-borne diseases. Though, significant amount of applied pesticides generally never reach their proposed target owing to their degradation, volatilization and leaching, which leads to serious ecological issues. In nature, pesticides or their degradation products are further transformed or degraded by other microorganisms or ultimately leads to total degradation by the microbial consortium. In this database, metagenomes from different crop rhizosphere are screened and further identified for their pesticides and other xenobiotics degrading properties. This database aim towards identification of functional genes, gene families and pathways which are involved in pesticide and xenobiotics degradation processes.

Furthermore, some microbial species which are naturally exposed to different kind of pollutions in their own habitat are competent to degrade a large variety of pollutants, including pesticides. These microbes represent a vast reservoir of enzymes for the identification of pollution and bioremediation. Most of these microbes occupy highly complex ecosystems like soils and are mostly unculturable. Analysis of soil metagenomic data will also help to retrieve biocatalysts from these samples and discovery of enzymes capable of acting on different pesticides. For instance, in a study on arctic soils, focus was given towards identification of microorganisms and their functional genes which are abundant and active while hydrocarbon degradation at cold temperature.

Datasets Source Databases: Metagenome datasets for Common Bean, Kodo Millet, Soybean, Sugarcane were taken from MG-RAST while those for Oat, Pea and Wheat were taken from EBI-Metagenomics database.

Approach: Different crop's rhizosphere metagenome/metatranscriptome datasets were downloaded from the EBI Metagenomics and MG-RAST (Metagenome Rapid Annotation using Subsystem Technology) database. Raw sequence reads were taken for each dataset and processed for further analysis. Each dataset was processed for QC check and initial preprocessing using MG-RAST server. For identification and annotation of proteins and other sequences, sequence similarity searches were done against different databases (Genbank, protein databases M5NR, SEED and Kyoto Encyclopedia of Genes and Genomes (KEGG)). For identification and functional characterization of diverse metabolic pathways associated with the the metabolism of aromatic compounds and xenobiotics biodegradation and metabolism, datasets were aligned against SEED Subsystems using MG-RAST pipeline (parameters: minimum alignment length - 15 and E-value cutoff - 1e-5). Further, for getting entire metabolic pathways information, global gene expression was annotated and further visualized with KEGGmapper. Level 2 and Level 3 SEED subsystem were applied for annotation. Level 3 of SEED subsystem corresponds to KEGG pathway. Furthermore, the Enzyme Commission (EC) number was identified for the sequences of corresponding enzymes. Pathways, transcripts and enzymes for the biological processes were identified in each dataset.