RecAssDNA

RecAssDNA is a computational framework designed to reconstruct comprehensive DNA sequences from fragmented or degraded genetic material through recursive assembly. It combines iterative de novo assembly with reference-guided scaffolding, error correction, and gap-filling to improve contiguity and accuracy in challenging samples such as ancient genomes, forensic specimens, and environmental DNA.

History and development

The concept of recursive or recursive-like assembly approaches gained attention in computational genomics in the 2010s and early 2020s. RecAssDNA, as a formalized framework, emerged in discussions and pilot software efforts aiming to address systematic gaps that arise when sequences are short, damaged, or unevenly covered. Proponents emphasize its potential to leverage repeatedly refined contigs as seeds for subsequent assembly rounds, integrating multiple data types to enhance genome recovery from compromised materials.

Methodology

RecAssDNA operates on fragmented sequencing data, often with uneven or low coverage. The workflow typically includes quality control, damage-aware error modeling, and an initial assembly to produce contigs. These contigs seed subsequent recursive rounds that recruit additional reads, refine overlaps, and extend sequences. The process may incorporate reference-guided scaffolding to orient contigs, long-read or mate-pair information for connectivity, and targeted gap-filling to close assemblies. Validation uses both simulated datasets and, when available, related reference genomes or conserved marker sets to assess completeness and accuracy.

Applications

The method is proposed for reconstructing genomes from ancient or degraded samples, improving microbiome or environmental DNA assemblies, and aiding forensic genomics where DNA material is limited or damaged. It can also support metagenomic projects by enhancing recovery of low-abundance genomes.

Limitations

Challenges include computational intensity, potential reference bias, risk of misassembly in repetitive regions, and sensitivity to contamination or uneven coverage. Careful validation and complementary data are important for reliable results.

See also

Genome assembly, De novo assembly, Reference-guided assembly, Ancient DNA, Environmental DNA.