MetaPathways

MetaPathways annotates and infers metabolic pathways from metagenomic and metatranscriptomic next-generation sequencing data to characterize ecological and biogeochemical functions of microbial communities.

Key Features:

• Operational Stages: Processes data through four stages—Quality Control, Feature Prediction, Functional Annotation, and Pathway Inference—to transform raw sequences into functional outputs.
• Quality Control and Assessment: Performs quality assessment and control on both assembled and unassembled nucleotide sequences from next-generation sequencing datasets.
• Feature Prediction and Annotation: Predicts and annotates noncoding genes and open reading frames (ORFs) to support downstream functional assignment.
• Pathway Inference with PathoLogic: Maps functional annotations onto the MetaCyc database using the PathoLogic algorithm to construct environmental Pathway/Genome Databases (ePGDBs), as an alternative to KEGG or SEED-based approaches, and produces ePGDBs compatible with Pathway Tools.
• Phylogenetic Analysis: Builds phylogenetic trees using MLTreeMap based on selected taxonomic anchor and functional gene markers.
• Data Conversion and Output Formats: Converts General Feature Format (GFF) files into concatenated GenBank files for ePGDB construction, generates Sequin files for GenBank submission, and produces comprehensive gene feature tables.
• Quantitative Comparison and Pathway Prediction: Integrates a weighted taxonomic distance metric and a normalized read-mapping measure to improve pathway prediction and enable quantitative comparison of assembled annotations.
• Improved Homology Searches: Uses LAST with BLAST-equivalent E-values to enhance homology search precision and compatibility with prevailing software.
• Annotation Mapping to Functional Hierarchies: Supports mapping of annotations onto user-defined functional gene hierarchies, including the Carbohydrate-Active Enzyme (CAZy) database.

Scientific Applications:

• Metabolic Network Reconstruction: Reconstructs metabolic interaction networks from environmental sequence data to investigate microbial ecological functions.
• Community Structure and Function Analysis: Generates ePGDBs and MLTreeMap-based phylogenetic trees to analyze microbial community structure, functional potential, and evolutionary relationships across diverse ecosystems.
• Cross-omic Integration: Applies to metagenomic, metatranscriptomic, genomic, and transcriptomic datasets from various sequencing platforms to explore metabolic pathways and biogeochemical interactions.

Methodology:

Performs quality assessment on assembled and unassembled nucleotide sequences; predicts noncoding genes and ORFs; conducts homology searches with LAST (BLAST-equivalent E-values); maps annotations to MetaCyc via PathoLogic to build ePGDBs; converts GFF to concatenated GenBank and generates Sequin files and gene feature tables; constructs phylogenetic trees with MLTreeMap; and applies weighted taxonomic distance and normalized read-mapping measures for pathway prediction and quantitative comparison.