Researchers at Princeton University have developed computer algorithms that can detect biosynthetic gene clusters (BGCs) by analyzing and interpreting metagenomic sequencing data, which are made up of genetic sequences collected from the tissues or excretions of hundreds of human subjects.
The team first identified genes essential for the synthesis of a particular molecule or chemical of interest, then used computational algorithms to sort through metagenomic data for similar genetic sequences, grouping these sequence fragments together.
The researchers then assessed the prevalence of each group in the human population and used the grouped sequences to assemble full-length BGCs.
They validated this approach by studying whether they could detect BGCs involved in the synthesis of type II polyketides (the team discovered 13 such gene clusters).
From Princeton University
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Abstracts Copyright © 2019 SmithBucklin, Washington, DC, USA
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