ArticleBiology direct2018
MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.
Article in Biology direct, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review.Biology · 2020Article
- Metalign: efficient alignment-based metagenomic profiling via containment min hash.Genome biology · 2020Article
- Environmental metagenome classification for constructing a microbiome fingerprint.Biology direct · 2019Article
- Fingerprinting cities: differentiating subway microbiome functionality.Biology direct · 2019Article
- Massive metagenomic data analysis using abundance-based machine learning.Biology direct · 2019Article
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6 authors.
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Abstract
backgroundMany methods have been developed for metagenomic sequence classification, and most of them depend heavily on genome sequences of the known organisms. A large portion of sequencing sequences may be classified as unknown, which greatly impairs our understanding of the whole sample.
resultHere we present MetaBinG2, a fast method for metagenomic sequence classification, especially for samples with a large number of unknown organisms. MetaBinG2 is based on sequence composition, and uses GPUs to accelerate its speed. A million 100 bp Illumina sequences can be classified in about 1 min on a computer with one GPU card. We evaluated MetaBinG2 by comparing it to multiple popular existing methods. We then applied MetaBinG2 to the dataset of MetaSUB Inter-City Challenge provided by CAMDA data analysis contest and compared community composition structures for environmental samples from different public places across cities.
conclusionCompared to existing methods, MetaBinG2 is fast and accurate, especially for those samples with significant proportions of unknown organisms. REVIEWERS: This article was reviewed by Drs. Eran Elhaik, Nicolas Rascovan, and Serghei Mangul.
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