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What are the current limiting factors of genome sequencing accuracy? By accuracy I mean a closeness relation between the sequenced genome and the finally assembled (I am not sure whether there is a proper name for this metric). I hope this way of measuring accuracy is useful since it also captures errors introduced during read-alignment (if short-read sequencing technology is used) and assembly.

As I understand it, there are two sources of errors limiting accuracy: errors in determining the correct bases and errors made in data analysis (read alignment, assembly, etc.). Which of these two sources is responsible for most errors in long- and short-read sequencing techniques? Are a significant amount of errors stemming from the data analysis?

As @David guessed correctly, I am a student (engineering) and wondering whether accuracy may be significantly improved by better algorithms.

As I currently understand it, short-read sequencing techniques are accurate but the repetitive regions are hard/impossible to align, while long-read sequencing techniques are more error-prone, and long-read & accurate sequencing (HiFi) is very expensive. Hence, my overly simplified perspective suggests that algorithmic improvements may continue to improve cheap long-read accuracy through hybrid approaches or improve the alignment and assembly of short reads. Is that correct?

The resources I used were:

https://www.pacb.com/blog/understanding-accuracy-in-dna-sequencing/ https://spectrum.ieee.org/tech-talk/biomedical/diagnostics/99-9-percent-accurate-genome-sequencing and the paper recommended by @Maximilian Press

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  • $\begingroup$ Welcome to Biology.SE. The Biology.SE community has agreed that questions that show little or no prior research effort are off-topic on this site. Please edit your question and tell us where you've looked for answers, what you do know about the topic, and where exactly you still have questions. In addition, there is more than one method for doing genome sequencing and they have different limitations — this makes your question as currently written too broad. Under researched questions may be subject to down-voting and closure. $\endgroup$
    – tyersome
    Jul 6 at 18:16
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    $\begingroup$ I notice that you have not completed the Tour. Doing so will help you understand how this SE works. The help on asking questions is a good follow up. More specifically, it is helpful to provide context and support for any assumptions in San question. What makes you think accuracy is a major problem? And the idea that it has to do with algorithms suggests that you have not researched the topic, but are data scientist or programmer looking for a problem. Are you? $\endgroup$
    – David
    Jul 6 at 18:28
  • $\begingroup$ On reflection, I wonder whether you mean "accuracy" or something else. "Accuracy" of genome sequencing relates to correct identification of a bases at a particular position: how frequently will a sequence contain a G (say) where there is really an A. However that is not a major problem in sequencing eukaryotic genomes, especially those of higher organisms. The main problems are closing gaps representing regions that contain long stretches of numerous, repeated gene copies, and mapping stretches of near-identical DNA called segmental duplications. This is not accuracy, but may be what you meant $\endgroup$
    – David
    Jul 7 at 14:15
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One that isn't on your list: cost.

  • In the form of Oxford Nanopore data, we have extremely long reads of low accuracy. These are not too expensive but on their own they are probably not viable for many applications.
  • In the form of Illumina data, we have extremely abundant, cheap, high-accuracy short reads, suitable for "counting" approaches. This is great for some variant calling approaches, and also for some kinds of orthogonal measures. On their own, they are not viable for a lot of applications, though.
  • In the form of PacBio (specifically HiFi) data, we have long(ish) reads of high accuracy. These are the total package, and you can in principle use them pretty much on their own for any given application that I can imagine. The issue is that they remain quite expensive.

Here is a recent review as a reference that goes over these (I haven't looked at it in detail, but it seems to capture the tradeoffs accurately).

We have gotten extremely good algorithmically at using all of these various kinds of data, and at hacking them in various ways to complement each other, but we can't get around the fundamental constraints of contiguity (length) and accuracy.

The HiFiAsm assembler for example is at peak performance for the moment, and it combines PacBio HiFi data with e.g. Hi-C data (a hack of Illumina) to deliver pretty much whole diploid human genomes, which is really an astonishing technical achievement. It was also used to assemble the redwood genome, which is 10X the size of the human genome.

The issue is taking these technical possibilities and getting them to a scale and a technological refinement where we can generate this extremely high-quality data at-will and for all of the applications where we would like to use it.

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    $\begingroup$ Do you really accept the poster’s assumption that accuracy is a major problem? Massive over-sequencing seems to deal with it. $\endgroup$
    – David
    Jul 6 at 18:38
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    $\begingroup$ @David I do in certain narrow contexts. For genome assembly, an application that the OP brought up, inaccuracy (and relatedly, incompleteness) is a major issue in the final product. One could argue both sides about whether accuracy at the read-level base call is particularly an issue. It is one that is now being addressed by technology in the fashions that you and I mention, but "massive oversequencing" is prohibitively expensive with the technologies that actually address the relevant problems. $\endgroup$ Jul 6 at 19:12
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    $\begingroup$ @David Massive over-sequencing also only helps with sequencing applications where you have sufficiently large and pure amounts of source material. There are many applications where this is not the case. $\endgroup$
    – jakebeal
    Jul 6 at 23:24

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