Software breakthrough radically boosts the speed of nanopore DNA sequencers

to me nanopore seq is not that promising. it has been here for a long time now and the benefits didn't convince a lot of customers to adopt it.

there are 2 main use cases now in precision medicine: rare disease and cancer. for both you need high precision reads, which nanopore doesn't provide.

Link to biorxiv version: https://www.biorxiv.org/content/10.1101/2020.02.03.931923v1

Summary because the title overstates significance a bit (of a cool paper): Most sequencing today is done using Illumina machines, which basically break DNA into small parts (on the order of ~hundred letters/bases), then use fluorescence/imaging to find sequence.

This paper applies to a new technology, nanopore-based sequencing, which pulls much longer pieces of DNA (record length: 2.3 million base pairs, but most end up shorter, on the order of thousands of bases) through a microscopic molecular channel, providing real-time outputs of voltage that can be somewhat noisily mapped to nucleotide sequences (since different DNA sequences have different voltage outputs when run through a channel).

This technology is very cool, and the fact that it's real-time opens up an interesting idea: you can potentially give real-time feedback to the sequencer as the run is operating about whether a given piece of DNA it's reading is interesting to you. If it's not, the channel can spit out the piece of DNA and start reading in a new one.

So for example, let's say you want to sequence viral sequences present in a human tissue sample. Well, naively, if you just try to collect all the DNA in the sample, most of the DNA is going to be from the human genome (human genome is ~100,000x bigger than viral genomes and likely not all cells are infected). This approach aims to map the DNA as it's being read to some reference (in this case, the human genome) and avoid re-sequencing pieces of DNA mapping to the reference (by getting the channel to spit out the piece of DNA it's reading and wait for a new sequence to come in). Your sequencing results are therefore enriched in the actual sequences of interest.

In terms of the contributions of this paper, nanopore sequencing is still a growing area. This real-time aspect has been a key motivation for some time: this paper improves the feasibility of the approach by algorithmic improvements to mapping between real-time voltage readings and reference sequences. This has to be fast to be effective, since DNA is read pretty quickly and in parallel across many channels, and previous methods apparently weren't fast enough to provide real advantages.

The caveats are that this only applies to cases where you don't want to read the majority of DNA present (an important use case but not universal), nanopore sequencing still has issues with high error rates which makes it a bit less attractive than Illumina sequencing, and the amount of DNA you can read through nanopore is still less than what you can do with Illumina. So it's a cool step on the way to a future where we can do some really exciting "interactive" real-time sequencing work but it's still a part of a developing technology suite.

This appears to be a potential alternative to selective amplification. https://www.wikiwand.com/en/Polymerase_chain_reaction#/Selec...

Instead of pre-processing the DNA by preferentially amplifying the DNA segments of interest (so that 99%+ of what you sequence is stuff you want), this software/hardware combo sequences the first N bases of each molecule, and then moves on to the next molecule if it doesn't match any expected sequences. If you're sequencing molecules over >200bp, being able to eject the molecule in the first ~10bp could be a very big speedup.

Our lab already has sub-1 day processing times for this type of analysis, but I believe the traditional sequencer machine we use (an Illumina MiSeq) is likely more expensive and more cumbersome than the portable solution described here.

theyre not really similar problem, genome assembly is a problem that is most effectively solved in the lab and with traditional graph algorithms. protein folding fits very nicely into structured prediction and has a very quantitative way of measuring performance. Genome assembly is more qualitative, to say the least.

given a bunch of reads of short(50-300bp) or long (1000-100,000 bp) lengths (we use different algorithms for each) you need to resolve a 1D sequence that holds all of those continguous subsequences for each chromosome. Genome assembly is often described as finding a hamiltonian cycle among the data. For short reads, we use debrujin graphs to avoid the hardness of a complete hamiltonian. for long reads, MinHash has become a popular heuristic.

But this can be tricky, for instance, in species that isn't haploid -- you actually have two possible genomes that you need to correctly assemble. Sometimes the difference is a base or two, but sometimes its much longer stretches that can appear as large 'bubbles' in the assembly graph.

The assembly problem can be harder or easier depending on the organism, for instance, Wheat is notoriously hard to assemble due to the fact that it has 3 mostly (but not completely) distinct genomes. The Norway Spruce is composed of ~80B basepairs, which will put strain on even the biggest machines. Oh and its mostly repeats.

Repeats are _everywhere_, and they can be really long (sometimes on the order of millions of nucleotides). Also -- depending on the species youre assembling, the repeat content and the kind of repeats can change. You also can get different errors from the library preparation, again related to the species of origin.

Using lab techniques is particularly helpful, as we can leverage molecular and genetic information. BACs (baceterial artificial choromosomes) can be used to break the genome into smaller continguous chunks and to act as 'anchors' for the larger assembly problem. Recombination rates of different SNPs can be used to infer spatially local segments of DNA, and optical tags can be added and imaged to provide physical anchors for certain sequences. Some organisms can be bred into pure lines, with limited heterozygosity. Expressed RNA transcripts can be used in a similar manner, as they are the concatenation of ordered exons. Combining these methods is typically the best way to get a good genome assembly.

Some of the bubble resolution heuristics could probably be improved with deep learning, but getting the right data for that is probably more effective with traditional graph algorithms. Also -- you really only need a good genome assembly once, and sometimes it doesnt even need to be all that good.

Genome assembly is a _really_ fascinating area of research, and honestly, the right direction is probably to represent genomes as something other than 1D sequence that more accurately reflects biology. Deep learning is still looking to make its mark on genomics, and unfortunately its not well suited for much of the discovery efforts such as genome assembly.

No practical assemblers take assembly as a Hamiltonian problem.