IMP

IMP performs integrated analysis of coupled metagenomic and metatranscriptomic datasets to reconstruct microbial genomes and characterize microbial community structure and functional gene expression.

Key Features:

• Robust Read Preprocessing: Performs rigorous preprocessing of raw sequencing reads to produce high-quality input data.
• Iterative Co-Assembly: Employs iterative co-assembly of metagenomic and metatranscriptomic data to optimize reconstruction of microbial genomes.
• Microbial Community Analysis: Provides detailed characterization of microbial community structure and function from integrated multi-omic data.
• Automated Binning: Includes automated binning to classify assembled sequences into genomic bins.
• Genomic Signature-Based Visualizations: Generates visualizations based on genomic signatures to aid interpretation of multi-omic results.
• Reference-Independent Analysis: Operates in a reference-independent manner to support analysis across diverse samples.
• Modular and Reproducible Pipeline: Implements a modular architecture to enable reproducible and customizable analyses.
• Implementation: Implemented using Python and Docker.

Scientific Applications:

• Microbial Ecology: Enables investigation of microbial community dynamics and ecological interactions.
• Environmental Microbiology: Supports studies of environmental impacts on microbial ecosystems.
• Systems Biology: Facilitates integrated multi-omic analysis of microbial system function.
• Functional Gene Expression Analysis: Enables characterization of functional gene expression using metatranscriptomic data.
• Genome-Resolved Studies: Supports reconstruction and downstream analysis of microbial genomes from complex samples.

Methodology:

Performs rigorous read preprocessing, iterative co-assembly of metagenomic and metatranscriptomic reads, automated binning, and genomic-signature-based visualization in a reference-independent, modular workflow implemented in Python and Docker.