Researchers developed a new contamination detection tool to establish reproducibility in the identification and analysis of microbiomes in challenging, low abundance samples.
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IMAGE: The Rice University computer science lab of Todd Treangen challenged and beat deep learning in a test to see if a new bioinformatics approach effectively tracks the lab. view more
Credit: Tommy LaVergne/Rice University
HOUSTON - (Feb. 26, 2021) - Tracking the origin of synthetic genetic code has never been simple, but it can be done through bioinformatic or, increasingly, deep learning computational approaches.
Though the latter gets the lion s share of attention, new research by computer scientist Todd Treangen of Rice University s Brown School of Engineering is focused on whether sequence alignment and pan-genome-based methods can outperform recent deep learning approaches in this area.