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Hello, I am wondering why the error pattern of PacBio raw data is dominated by InDel. Could someone help to explain it ?
Thanks in advance.
Thanks in advance.
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I think it's due to both the 'single molecule' and 'real time' nature of the system. The RS is essentially taking a video of the polymerase adding nucleotides in real time. As this happens very quickly, the imaging system might miss an addition (or maybe an unlabeled nucleotide slipped into the system), making it look like a small deletion. I'm not sure what would lead to a small insert (but I'm also not sure that error model really happens with PacBio).Originally posted by juckdnarocks View Post -
The insertion errors result from a nucleotide entering the detection zone for a significant amount of time, but without being incorporated.Comment
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Indeed. The measured signal is based on the residence time of the tagged nucleotide rather than from the tagged end of a terminal incorporation. You can also have problems with pulses merging together when you hit a long homopolymerComment
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It means that SMRT has the problem with a long homopolymer like 454? I thought it is a random error model.Originally posted by ELoomis View PostComment
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"Problem" is relative. You'll have a harder time calling the exact number of nt's in the homopolymer run, but polymerase keeps running through it, so you can still get accuracy improvement with CCS (if you really want to know how many nt's are there) or flanking sequence on either side (if you just want to map your read). If the exact number of nt's in a homopolymer run is your thing, you could also delve into the basecalling parameters to improve/optimize since this isn't a major priority for the default user...Originally posted by juckdnarocks View Post
In my experience, the SMRT error profile is remarkably stable through very extreme sequence compositions (100% GC, trinucleotide repeats) and all the way to the end of the raw read.Comment
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Homopolymers
PacBio's sequencing errors in homopolymers are still stochastic (random), just at a higher rate. With enough coverage, the consensus across homopolymers approaches 100%, just like in non-homopolymer regions. This is the case for de novo assembly, resequencing, and also single molecules (circular consensus).
By contrast, systematic errors don't go away with coverage, and limit the ultimate consensus accuracy. That's why many sequencing experiments plateau at Q40 or so. By contrast, because of the randomness of the errors, PacBio has demonstrated results greater than Q50 for a range of bacterial genomes and BACs.
See this blog from PacBio for a better explanation:
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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