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Advances in metagenomic sequencing have provided an unprecedented view of the microbial world, but untangling the web of microbe interdependencies and the complex relationship between microbiome and host is a major challenge in biology. New statistical methods are needed to analyze metagenomic data and infer these relationships. Focusing on amplicon sequencing data, we present methods for leveraging phylogenetic information in deep neural network models and for transfer learning from large data repositories. This approach is demonstrated in experiments using data from the Earth Microbiome Project (EMP) and a dataset of 1500 samples from Waimea Valley on the island of Oahu, Hawaii.
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AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite
Abstract We introduce AGAMEMNON ( https://github.com/ivlachos/agamemnon ) for the acquisition of microbial abundances from shotgun metagenomics and metatranscriptomic samples, single-microbe sequencing experiments, or sequenced host samples. AGAMEMNON delivers accurate abundances at genus, species, and strain resolution. It incorporates a time and space-efficient indexing scheme for fast pattern matching, enabling indexing and analysis of vast datasets with widely available computational resources. Host-specific modules provide exceptional accuracy for microbial abundance quantification from tissue RNA/DNA sequencing, enabling the expansion of experiments lacking metagenomic/metatranscriptomic analyses. AGAMEMNON provides an R-Shiny application, permitting performance of investigations and visualizations from a graphics interface.
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- PAR ID:
- 10322401
- Date Published:
- Journal Name:
- Genome Biology
- Volume:
- 23
- Issue:
- 1
- ISSN:
- 1474-760X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1186/s13059-022-02610-4
