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RAMBO can cut indexing times for huge DNA databases
Rice University computer scientists are sending RAMBO to rescue genomic researchers who sometimes wait days or weeks for search results from enormous DNA databases.
DNA sequencing is so popular, genomic datasets are doubling in size every two years, and the tools to search the data haven t kept pace. Researchers who compare DNA across genomes or study the evolution of organisms like the virus that causes COVID-19 often wait weeks for software to index large, metagenomic databases, which get bigger every month and are now measured in petabytes.
RAMBO, which is short for repeated and merged bloom filter, is a new method that can cut indexing times for such databases from weeks to hours and search times from hours to seconds. Rice University computer scientists presented RAMBO last week at the Association for Computing Machinery data science conference SIGMOD 2021.
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Home > Press > Bioinformatics tool accurately tracks synthetic: DNA Computer scientists show benefits of bioinformatics with PlasmidHawk
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 of origin of a synthetic genetic sequence.
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Tommy LaVergne/Rice University
Abstract:
Tracking the origin of synthetic genetic code has never been simple, but it can be done through bioinformatic or, increasingly, deep learning computational approaches.
Bioinformatics tool accurately tracks synthetic: DNA Computer scientists show benefits of bioinformatics with PlasmidHawk
Houston, TX | Posted on February 26th, 2021
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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.