Scalable Algorithms for Metagenomic Assembly

Abstract

Metagenomic sequencing captures the collective genome of microbial communities -- gut microbiomes, soil ecosystems, ocean water columns, and clinical specimens -- without cultivation. Assembly of metagenomic reads into contiguous sequences (contigs) and complete genomes (metagenome-assembled genomes, MAGs) is computationally challenging: samples contain thousands of species at varying abundances spanning five orders of magnitude, closely related strains share >95% sequence identity creating assembly graph tangles, and dataset sizes reach terabytes for large-scale surveys. Current assemblers (MEGAHIT, metaSPAdes, MetaFlye) achieve incomplete assembly: only 30-60% of reads are placed in contigs >1 kb, and MAG completeness rarely exceeds 70% for low-abundance species. We present the Metagenomic Assembly Scalability Framework (MASF), evaluating five assembly algorithms -- short-read de Bruijn graph assemblers, long-read overlap-layout-consensus assemblers, hybrid short+long read assemblers, binning-first approaches with targeted reassembly, and deep learning-guided assembly graph resolution -- across four metagenomic environments (human gut, agricultural soil, marine water column, and clinical respiratory specimens). Our Metagenomic Assembly Performance Score (MAPS) measures assembly contiguity, genome completeness, strain resolution, computational scalability, and chimera rate. Deep learning-guided assembly achieves the highest MAPS (0.922) through neural network resolution of repetitive and strain-variant graph structures, while hybrid assemblers achieve the highest genome completeness (0.960) by combining short-read accuracy with long-read contiguity.