The April 4th issue of Science had an article by Helicos BioSciences in which they described the single-molecule DNA sequencing of a viral genome. I knew about Helicos because they came and gave a talk to our Genetics department describing their planned strategy to develop a method for single-molecule sequencing. As I recall, the talk was entirely theoretical as they didn’t have much experimental data to show. Clearly things have gone well for Helicos, since their article convincingly demonstrates the potential of single-molecule sequencing for high-throughput, low-cost sequencing.
Introduction: The Problems with PCR
Why bother with single molecule sequencing? The introduction briefly discussed three problems associated with PCR-based sequencing.
- Bias in template representation. Due to thermodynamics and other factors I don’t well understand, PCR efficiency is directly affected by characteristics of the template. Shorter products, for example, are more efficient to amplify than longer products.
- Library preparation complications. PCR-based sequencing methods require a lot of templates, and preparation of the libraries can be “onerous and expensive in terms of DNA manipulation,” according to the article. I don’t do library prep myself, but this sounds reasonable.
- Error incorporation. Here is something that I do know about. Any time you use PCR, there’s a chance that mis-incorporation at an early cycle will introduce (and then amplify) errors in the sequence. We’ve seen some problems with 454 and Solexa sequencing that may be attributed to this. The idea of taking PCR-induced errors out of sequence reads appeals to me very much.
Results: Sequencing-by-synthesis of the M13 Viral Genome
The authors report sequencing the ~7 kbp M13 genome with 100% coverage and at an average depth of 150X. The read lengths averaged 23-27 bp, depending on the run and some post-processing; the authors claim to have performed runs with average read lengths of over 30 bp. According to alignment statistics in Table 1, there were 32,473 forward-orientation reads (relative to the reference) for an average coverage of 96X, and 34,109 reverse-orientation readds for an average coverage of 105X. Coverage in both orientations becomes important during their mutation-detection simulations.
Simulations of Mutation Detection
Because they sequenced the canonical strain of M13, there should be no sequence polymorphisms. So, to test the ability of this sequencing method to pick up mutations, the authors created “synthetic mutations” in the reference sequence and re-performed alignments. The synthetically-introduced mutations are picked up with an average sensitivity of ~98%. To me, this was the weaker part of the paper – mutations created in silico won’t accurately represent real variation, but at least it let the authors discuss analysis and refinement steps that led to improved mutation detection.
Discussion: Caveats and Future Directions
I don’t think Helicos is yet a threat to established next-generation platforms like Roche/454 and Illumina/Solexa. At 25 bp, the reads are too short to be useful in eukaryotes. Like 454, the Helicos platform has some difficulties with homopolymers , especially runs of cytosine residues. The authors readily admit that “large genomes, heterogeneous samples, and genomic structural variations will likely require longer reads, reduced homopolymer run through, and enhanced alignment tools.”
Yet this publication is an important proof-of-principle for the Helicos method. As far as single-molecule DNA sequencing goes, it looks like Helicos Biosciences is the one to beat.
David Dooling says
While PCR may have its drawbacks, it also has its advantages.