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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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Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
...Yesterday, 11:10 AM -
Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.
There is no single reason why many patients don’t respond to treatment as expected. Cancer is...07-08-2026, 05:17 AM -
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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