Originally posted by pogaora
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If RNA-seq was strand-specific it may explain why you did not get any peak calls (if your peak caller requires peaks on both strands). You could perhaps calculate RPKMs and compare to the other samples, or compare SNP calls in exons to figure out where the reads come from...Originally posted by pogaora View PostWe haven't done any exome capture experiments, but there were RNAseq samples on the same flowcell. The picture shown is not typical of most genes though - even some which we know are highly expressed in these cells.
Bias may be different in ChIP and input since you start with less material in the ChIP library, but GC-bias will not result in such enrichments over exons.
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Hi folks,
Is your target protein expected to bind to a single location (like a transcription factor)? or over a more distributed region?
If the former is true, I would suggest visualizing your read pile-up using the Broad's IGV which lets you look nicely at the strand-bias (Fig 1 here for an explanation of what I mean!). I've found this to be a great way visually inspecting the data for true binding sites vs. noise-peaks.
Cheers,
Lizzy
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Hello again all. Thanks to everyone for their interest in this question.Originally posted by JohnK View PostI talked to a colleague who sees these exon-peak events in some of his data depending on what protein or transcription factor he's after. They could be perfectly valid.
Does the above mean that your colleague validated in the wet lab?
The peaks we're talking about are from targetting a transcription factor and we see reads on both strands in all of the peaks that were in the pic posted earlier.
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Hi Pogaora,
We observed the same exon enriched peak patterns in our ChIP-seq datasets. We also tried another replicate but it didn't happen again. Can you reproduce such peak patterns? I'm interested in comparing your data with ours (we can provide our data too if you are interested). I've sent you a message about the details.
Best regards,
yxi
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how should that work technically, mRNA getting fixed into the library? you have any experimental evidence?Originally posted by hon View PostIt may also due to RNAase treatment is not enough, so mRNA was carried over to library construction. Make sure RNAase treatment is done long enough, trace of RNA will be magnified in library.
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I didn't have exact experimental evidence but that phenomenon also occurred to me once that many reads pile up on exons of some genes. My reason is that only mRNA mapped to exons, it was either due to mRNA contamination in the lab or ChIP sample was not completely RNAase treated. Since then, I paid careful attention to ChIP-seq library contamination, it didn't happen again.
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by GATTACATLove 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.
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07-01-2026, 11:43 AM -
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by SEQadmin2
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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