Next Generation Sequencing Improved by Genome Mapping

Last month we ran a series of blog posts about next generation sequencing. Among the topics discussed were the limitations of next generation sequencing. It’s an incredible technology that has opened the doors on what’s possible: whole genomes are being sequenced, compiled into large databases, and compared. Sequencing has become cheaper and faster than ever before. We can plan and execute large projects such as cataloguing the genetic diversity of the human microbiome. These huge projects result in a flood of information; more than we can easily process. To justify the time and money invested in making sense of so much data, it is critical that the data be good.

One problem with next generation sequencing is that the results are often just a “rough draft.” This is because DNA sequencers generally have short read lengths, and each next generation sequencing platform struggles to make sense of the genome in its own way; for example, some aren’t great at homopolymer stretches, some have a GC bias. The genomes that are being deposited in GenBank are prone to error: one study found that > 50% of “finished” genomes in GenBank have errors including sequence assembly errors, large stretches of sequence (~1.3 Mb) missing, large inversions, and missed repeats.¹ It’s possible to upgrade that rough draft to a more polished version through traditional Sanger sequencing methods, but that is expensive and not necessarily practical for these large-scale projects.

Enter genome mapping, a new technology that quickly analyzes large fragments of DNA to create a more accurate picture of the whole genome. DNA is cut into long fragments and loaded into microfluidic channels or nanochannels that separate and stretch the DNA out into single strands for analysis. Because such long strands are analyzed, the result is a much larger view of the genome. This includes long-range structural information and information on chromosome number that is typically missing from next-generation sequencing results.

Genome mapping can be used for genome assembly and validation of next-generation sequencing results, structural variation analysis, and comparative genomics. Variations in chromosomal structure have been clinically associated with conditions including autism, Crohn’s disease, and cancer. Comparative genomics can quickly provide information about, for example, antibiotic resistance in an ongoing outbreak. No matter the size of the genome being studied, genome mapping can provide valuable information in conjunction with next-generation sequencing results. More information about genome mapping can be obtained from the vendors advancing the technology, including OpGen and BioNano Genomics.