Microbes are the most abundant life form in our planet. However, due to the limitation of traditional sequencing technology, the majority of the microbial populations cannot be sequenced and studied until the 21st century. A novel sequencing technology, metagenomics, solves this problem by sequencing every organism in the environments at the same time. Theoretically metagenomics is able to get every DNA piece from all microbes in the environments, but analysis of genomic sequences becomes a problem since the sequences from hundreds or even thousands of species were mixed up. Furthermore, the introduction of several other next generation sequencing methods, including metatranscriptomics, metaproteomics, and metabolomics, which have the potential of understanding the functional profiles of the microbial species, create more hurdles for data analysis due to their short read lengths. I would like to propose a computational approach that is able to integrate multiple omics data to draw a more complete picture of the microbial populations. The proposed method will first classify the metagenomics data to recover the genomes of the individual organisms and then map other omics data against the individual genomes to investigate the roles that the organisms play in specific environments. This also grants us the ability to examine the inter-species interaction by looking into the functional profiles of each microbe and allows scientists to answer the two most important questions regarding to any environmental studies: “who are there” and “what are they doing.” I plan to apply this method to identify more unknown and uncultivated species and how they interact with their environments from published meta-omics datasets.
|Effective start/end date||3/1/18 → 2/28/19|
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