ArticleGenome biology2017
MetaGen: reference-free learning with multiple metagenomic samples.
Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Landscape of Genetic Content in the Gut and Oral Human Microbiome.Cell host & microbe · 2019Pooled it
- Binning Metagenomic Contigs Using Contig Embedding and Decomposed Tetranucleotide Frequency.Biology · 2024Article
- Human disease prediction from microbiome data by multiple feature fusion and deep learning.iScience · 2022Article
- Dicer-like proteins influence Arabidopsis root microbiota independent of RNA-directed DNA methylation.Microbiome · 2021Article
- Genome diversification in globally distributed novel marine Proteobacteria is linked to environmental adaptation.The ISME journal · 2020Article
- MetaBMF: a scalable binning algorithm for large-scale reference-free metagenomic studies.Bioinformatics (Oxford, England) · 2020Article
- MicroPro: using metagenomic unmapped reads to provide insights into human microbiota and disease associations.Genome biology · 2019Article
- IdentifyingFrontiers in microbiology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
A major goal of metagenomics is to identify and study the entire collection of microbial species in a set of targeted samples. We describe a statistical metagenomic algorithm that simultaneously identifies microbial species and estimates their abundances without using reference genomes. As a trade-off, we require multiple metagenomic samples, usually ≥10 samples, to get highly accurate binning results. Compared to reference-free methods based primarily on k-mer distributions or coverage information, the proposed approach achieves a higher species binning accuracy and is particularly powerful when sequencing coverage is low. We demonstrated the performance of this new method through both simulation and real metagenomic studies. The MetaGen software is available at https://github.com/BioAlgs/MetaGen .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.