Metagenomic data mining in oil spill studies

Since the 2010 Deepwater Horizon oil well blowout in the Gulf of Mexico, there has been renewed interest in the biology of oil-degrading bacterial communities. One of the research focuses of our group is to use nucleic acid sequencing-based approaches to investigate the potential of indigenous bacteria to naturally metabolize hydrocarbons in the context of a spill event. Over the past several years, the throughput of modern sequencing technology platform instruments has dramatically increased with lower costs for sequencing and data generation but higher costs for data computation and downstream processing. This high throughput of data production has put massive pressure on existing computing and storage infrastructure and also significantly increased the complexity of sequence data analysis. Many research organizations are willing to incorporate nucleic acid sequencing technology into their R&D pipelines but can be discouraged and overwhelmed by the high costs of computing infrastructure and data analysis. This chapter will address good practices, methodology and key concepts in the analysis of shotgun metagenomic sequencing data obtained from laboratory experiments simulating conditions relevant to oil spills into natural ecosystems. Particular emphasis will be put on bioinformatics and data mining methodology to process sequencing data into meaningful biological information.