MetaTrinity: Enabling Fast Metagenomic Classification via Seed Counting and Edit Distance Approximation
CoRR(2023)
摘要
Metagenomics, the study of genome sequences of diverse organisms cohabiting
in a shared environment, has experienced significant advancements across
various medical and biological fields. Metagenomic analysis is crucial, for
instance, in clinical applications such as infectious disease screening and the
diagnosis and early detection of diseases such as cancer. A key task in
metagenomics is to determine the species present in a sample and their relative
abundances. Currently, the field is dominated by either alignment-based tools,
which offer high accuracy but are computationally expensive, or alignment-free
tools, which are fast but lack the needed accuracy for many applications. In
response to this dichotomy, we introduce MetaTrinity, a tool based on
heuristics, to achieve a fundamental improvement in accuracy-runtime tradeoff
over existing methods. We benchmark MetaTrinity against two leading metagenomic
classifiers, each representing different ends of the performance-accuracy
spectrum. On one end, Kraken2, a tool optimized for performance, shows modest
accuracy yet a rapid runtime. The other end of the spectrum is governed by
Metalign, a tool optimized for accuracy. Our evaluations show that MetaTrinity
achieves an accuracy comparable to Metalign while gaining a 4x speedup without
any loss in accuracy. This directly equates to a fourfold improvement in
runtime-accuracy tradeoff. Compared to Kraken2, MetaTrinity requires a 5x
longer runtime yet delivers a 17x improvement in accuracy. This demonstrates a
3.4x enhancement in the accuracy-runtime tradeoff for MetaTrinity. This dual
comparison positions MetaTrinity as a broadly applicable solution for
metagenomic classification, combining advantages of both ends of the spectrum:
speed and accuracy. MetaTrinity is publicly available at
https://github.com/CMU-SAFARI/MetaTrinity.
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关键词
metafast metagenomic classification,seed counting
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