viaDBG

viaDBG reconstructs viral quasispecies de novo from high-throughput sequencing reads using de Bruijn graph methods to characterize intra-host viral diversity.

Key Features:

• De Bruijn graph-based assembly: Employs a de Bruijn graph approach for de novo assembly of viral quasispecies, optimizing speed relative to overlap graph methods while enhancing accuracy.
• Error correction and large k-mers: Performs iterative sequencing error correction to enable the use of large k-mers in the de Bruijn graph, reducing noise from erroneous reads.
• Paired-end information integration: Adapts the paired de Bruijn graph to incorporate paired-end information and leverage long-range linkage during contig construction.
• Performance and accuracy: Demonstrates at least ninefold speed improvement over SAVAGE while maintaining comparable or superior accuracy, matches PEHaplo in speed and better recovers low-abundance quasispecies.
• Implementation: Implemented in C++.

Scientific Applications:

• Viral evolution and epidemiology: Enables characterization of viral diversity, mutation dynamics, and intra-host population structure from sequencing data without requiring reference genomes.

Methodology:

Constructs de Bruijn graphs from sequencing reads, performs iterative error correction to permit large k-mers, adapts paired de Bruijn graph structures to integrate paired-end information, and builds contigs from the resulting graph.