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Hi all, I have these ChIP-seq samples which may be enriched in high-repeated sequences... As many softwares for ChIP-seq rely on unique matches I wonder which could be a good strategy to analyze such samples....
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hi dOriginally posted by dawe View Post
if you are not explicitly interested in what binds the repeats, you just eliminate the non-unique sequences (and try to sequence sufficiently deep to get more than just repeptitive sequences).
if you are interested in targets that bind the repeats you will have problems to convincingly analyze the data (imho
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That's exactly what I fear!Originally posted by mudshark View Postif you are interested in targets that bind the repeats you will have problems to convincingly analyze the data (imho)
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why don't you treat your sequences as de-novo assembly and try to align them with very very strict alignment parameters. This will hopefully result in many unique contigs that rely on small changes even within repeat regions. Then you just have to find the corresponding unique contigs between treatment and control and calculate the statistical difference.Comment
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Mmm... that's an interesting approach... I'll try it for sure. Which assembler would you suggest for this task? I've tried once abyss but it didn't satisfy me. Velvet, maybe?Originally posted by What_Da_Seq View Post
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What type of repeats do you expect to be enriched? Do you see any alignments at all in these regions? If your repeats are relatively short and you are planning to do more sequencing one way would be to select larger fragments and do additional shearing before library construction, this would perhaps give more unique alignments around the repeats.Comment
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Sorry no experience with de-novo assembly only reference based assembly.Comment
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I do not follow. Don't you want to do LESS shearing to get larger fragments?Originally posted by Chipper View PostComment
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The person who gave us the sample claims that there may be enrichment in rDNA sequences (mouse). I've tried to align reads on rDNA sequence only and I get ~0.15 % reads aligned (which seems to me pretty high for a 45kbp sequence) in IP. I've just excluded the matching reads to align them on the rest of the genome (just to see what's happening).Originally posted by Chipper View Post
I've also noticed that rDNA sequence are not annotated on mouse genome (and they're expected to be ~400 copies scattered around).
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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...06-18-2026, 07:11 AM -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
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With the launch of new single-cell sequencing platforms in 2026, the field stands at an exciting inflection point. This article surveys the most impactful advances in the field and discusses how they’re reshaping research in cancer, immunology, and beyond.
Introduction
Single-cell sequencing technologies have undergone remarkable advances over the past decade, transitioning from low-throughput experimental approaches to highly scalable platforms capable of...05-22-2026, 06:42 AM
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