Metagenomics-Toolkit

Metagenomics-Toolkit automates the scalable analysis of short and long metagenomic reads from Illumina and Oxford Nanopore Technology devices, providing standard and unique analytical capabilities for complex environmental metagenomes.

Key Features:

• Quality Control: Performs standard quality control on metagenomic sequencing reads.
• Assembly: Includes a machine learning-optimized assembly step that adjusts peak RAM usage to match actual requirements, reducing the need for high-memory hardware.
• Binning: Performs metagenome binning as part of the standard analysis workflow.
• Annotation: Performs annotation of metagenomic data as a standard feature.
• Plasmid Identification: Provides unique capability for plasmid identification from metagenomic data.
• Recovery of Unassembled Microbial Community Members: Enables recovery of microbial community members that remain unassembled after standard assembly processes.
• Microbial Interdependency Discovery: Discovers microbial interdependencies through dereplication, co-occurrence analysis, and genome-scale metabolic modeling.
• Machine Learning-Enabled Resource Allocation: Uses machine learning to optimize assembly step resource allocation based on actual RAM requirements.
• Cloud-Based Cluster Execution: Includes optimizations for efficient execution on cloud-based cluster infrastructure.

Scientific Applications:

• Complex Environmental Metagenome Analysis: Analyzes complex environments with thousands of datasets, such as those in the Sequence Read Archive.
• Sewage Microbiome Investigation: Enables investigation of possible core microbiomes in sewage metagenomes, as demonstrated on 757 sewage metagenome datasets.
• Microbial Community Structure Elucidation: Supports recovery and characterization of complete and unassembled microbial community members from environmental samples.
• Microbial Interaction Network Inference: Facilitates discovery of microbial interdependencies and metabolic interactions through co-occurrence and metabolic modeling approaches.

Methodology:

The toolkit processes short reads from Illumina and long reads from Oxford Nanopore Technology devices through quality control, assembly, binning, and annotation steps, with machine learning optimization of assembly RAM usage and support for cloud-based cluster execution.