Genome assembly is the process of reconstructing an organism’s complete DNA sequence from many short or long fragments generated by sequencing technologies. It aims to produce a contiguous, accurate representation of the genome that can be used to study genes, regulatory elements, structural variation and evolution.

Modern sequencing platforms generate huge numbers of reads, but each read is only a small piece of the genome and often contains errors. Assembly algorithms therefore face two central challenges: dealing with sequencing errors and resolving repeats, where identical or highly similar sequences occur in multiple genomic locations. Short reads are cheaper and highly accurate, but struggle with large repetitive regions. Long reads can span repeats and complex rearrangements, but are usually more error prone. Current strategies often combine both, using long reads to establish the overall structure and short reads to polish and correct errors.

Assembly methods fall into overlapping categories. De novo assembly builds a genome from scratch using overlaps and graph structures without a reference. Reference guided assembly aligns reads to an existing closely related genome to assist reconstruction and gap filling. Hybrid approaches leverage multiple data types, such as long reads, short reads, optical maps and chromatin conformation data, to reach chromosome level assemblies.

Quality assessment relies on continuity metrics, error rates, and completeness measures such as recovery of conserved single copy genes. High quality assemblies enable identification of structural variants, population level comparisons, and detailed functional annotation. Ongoing research focuses on improving algorithms, reducing computational costs, and extending accurate, phased assembly to complex, repetitive and polyploid genomes.