METATRYP

METATRYP identifies tryptic peptides from predicted protein-coding sequences and infers their taxonomic origin to support peptide-based taxonomic attribution in metaproteomics.

Key Features:

• Sequence assembly ingestion: Supports metagenomic and metatranscriptomic assemblies, single-cell amplified genomes (SAGs), and metagenome-assembled genomes (MAGs) as input sequence sources.
• Tryptic peptide identification: Extracts tryptic peptides from predicted protein-coding sequences derived from genomes and metagenomes.
• Least Common Ancestor (LCA) prediction: Predicts the LCA for peptides shared across multiple organisms to provide taxonomic attribution.
• Performance and scalability: Backend architecture updates improve performance for large-scale analyses.
• Marine database expansion: Incorporates an expanded marine peptide database that revealed a low occurrence of shared tryptic peptides among disparate marine microorganisms.
• Coronavirus-specific analysis: Performs coronavirus proteome analyses that identified candidate SARS-CoV-2 peptide biomarkers and minimal shared tryptic peptides between SARS-CoV-2 and other taxa.

Scientific Applications:

• Ocean metaproteomics: Enables peptide-based taxonomic inference for marine microbial community studies.
• Phyloproteomics: Allows evaluation of shared tryptic peptide frequencies across taxonomic groups for comparative proteomic analyses.
• Metaproteomic biomarker discovery: Assists in identifying and assessing candidate peptide biomarkers for taxa of interest.
• Coronavirus proteomics: Supports identification of SARS-CoV-2–specific peptides and differentiation from other taxa.
• Microbial community composition and evolution: Facilitates taxonomic attribution to inform studies of microbial ecology and evolutionary relationships.

Methodology:

Analyzes predicted protein-coding sequences from genomes and metagenomes to extract tryptic peptides; predicts least common ancestors for shared peptides to assign taxonomic attribution; ingests metagenomic and metatranscriptomic assemblies, SAGs, and MAGs; employs backend optimizations to improve performance.