assembly
Definition
Assembly in bioinformatics refers to the computational process of reconstructing longer DNA, RNA, or protein sequences from shorter sequencing reads or fragments. This fundamental technique addresses the limitation that sequencing technologies cannot read entire genomes in one piece. Assembly algorithms identify overlapping regions between reads to merge them into contiguous sequences (contigs) and scaffolds. Two main approaches exist: de novo assembly, which builds sequences without a reference genome, and reference-guided assembly, which aligns reads to an existing template. Quality assembly is critical for genome annotation, variant calling, comparative genomics, and understanding genetic architecture. Assembly accuracy depends on read length, coverage depth, error rates, and the complexity of repetitive regions in the target sequence.
Visualize assembly in Nodes Bio
Researchers can visualize assembly graphs in Nodes Bio to understand the topology of sequence reconstruction. Nodes represent contigs or reads, while edges show overlaps and connections. This network view reveals assembly ambiguities, repetitive regions causing branching structures, and gaps requiring additional sequencing. Users can overlay metadata like coverage depth, GC content, or taxonomic classification to identify misassemblies or contamination patterns across the assembly network.
Visualization Ideas:
- Assembly graph networks showing contig connections and overlaps
- Coverage depth heatmaps overlaid on assembly topology networks
- Comparative assembly networks across multiple samples or strains
Example Use Case
A microbiology team sequencing a novel bacterial pathogen uses metagenomic assembly to reconstruct its genome from environmental samples. The assembly produces multiple contigs with uncertain connections due to repetitive mobile genetic elements. By visualizing the assembly graph in Nodes Bio, they identify which contigs likely belong to the main chromosome versus plasmids based on coverage patterns and connectivity. They discover a potential antibiotic resistance gene cluster on a highly connected subgraph, suggesting horizontal gene transfer events that explain the pathogen's multi-drug resistance phenotype.