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Workflow

Mapping Genomes

In 4 steps, as little as 24 hours

The Argus® Whole Genome Mapping System and MapSolver™ analysis software provide a straightforward, automated workflow for evaluating whole genomes and chromosomes in as little as 24 hours for smaller genomes.

1.     Extract

Using OpGen’s optimized DNA extraction kit, high molecular weight DNA needed to produce Whole Genome Maps can be extracted in as little as two hours.

2.     Immobilize & Digest

Single DNA molecules are flowed through microfluidic channels and immobilized on a charged glass surface. The immobilized DNA is digested, maintaining the fragment order.

Genomic DNA, captured as single DNA molecules from random breakage of intact chromosomes, is loaded into microchannels, immobilized electrostatically, then digested with a restriction endonuclease.

Digestion reveals cleavage sites. The restriction fragment order is maintained for each molecule.

 

3.     Measure & Assemble

The DNA fragments are stained with fluorescent dye; fragment length is proportional to fluorescence intensity. By overlapping fragment patterns, the single-molecule maps are assembled to produce a Whole Genome Map that provides a minimum 30 X coverage.

Automated measurements capture fluorescence intensity to determine fragment sizes.

Overlapping Single-molecule Maps are assembled to produce a highly accurate, Whole Genome Map. The red circle indicates 30x coverage, ensuring accuracy of the consensus Whole Genome Map.

 

4.     Analyze

The MapSolver™ Analysis tool provides powerful features to evaluate and compare Whole Genome Maps. Discover genetic variation, perform high-resolution epidemiology, or accelerate whole-genome sequencing.

  • View and compare genomes
  • Identify important genetic variations
  • Upload sequence contig data to align contigs against an ordered whole-genome scaffold
  • Use the Whole Genome Map as an independent source to guide sequence placement, confirm assemblies, identify misassemblies and gaps, and finish de novo sequences

 

Directly compare maps to discover insertions, deletions, and other genetic elements that other technologies miss. Differences and similarities are highlighted for at-a-glance discovery.
Map-based similarity clustering

Map-based similarity clustering accurately distinguishes strains and describes relatedness between isolates.

Align sequence contigs with Whole Genome Maps to quickly orient contigs, locate gaps and correct misassemblies.