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Tuesday, August 1 • 15:20 - 15:40
Simulating microbial communities

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Today, a multitude of different sequencing technologies is available, all of which use different means to obtain the sequence information and therefore have different read lengths, parameters and specific error profiles. Since metagenomic studies involving whole-genome sequencing are still quite expensive, it is crucial to choose the right experimental setup and parameters. Testing the planned setup before doing the actual experiment can help a lot in saving money and designing a better experiment. To facilitate this, we developed an extendable and flexible simulation pipeline which is able to simulate arbitrary complex metagenomic data sets from just a 16S profile. We include read simulators for the most common sequencing technologies, support of multiple samples or communities as well as providing a ground truth for assemblers, binners and profilers which subsequently can be tested against. This pipeline was already successfully used in creating the data sets for the CAMI challenge (http://biorxiv.org/content/early/2017/01/09/099127). To prove the usefulness of such a pipeline beyond CAMI, we decided to create data sets aimed specifically at answering two questions: How do coverage/sequencing depth and the presence of closely related strains affect assembly quality? To answer these questions, we created a considerable number of small datasets with varying coverage and average nucleotide identity (ANI) values. The findings we made had been discussed in the metagenomics community before, but the extremely controlled environment of both experiments pinpoint the weaknesses of current metagenomic assemblers at very high and low coverages as well as in the presence of strains related with more than 97% ANI.


Tuesday August 1, 2017 15:20 - 15:40
Graduate School of Management Building, room 309 Volkhovskiy Pereulok, 3, St. Petersburg, Russia