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I want to do sliding window analysis for differential methylation calculations for WGBS and target bisulfite sequencing libraries. Obviously I am not comparing the 2 library types against each other. I have found the software to make the sliding windows with specific step sizes but my question is how do you know how big your windows should be and then what step to use. I've read papers that use 3Kb windows all the way down to 500bp windows. Is choosing the window and step just arbitrary? I would like to hear others suggestions on what they have done to determine how big their windows and steps are. I am thinking for low coverage WGBS the larger window is probably best to get enough coverage but I am not sure what "step" to use. For my targeted libraries, I have very high coverage so I should probably use a smaller window. I would just like to hear what others would do. Thanks.
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If you have a look at the Differential Methylation Leture document on our methylation training course material and have a look at slides 11 and 12 you can see some information about this.
The short answer is that the best window size is a balance between statistical power and biological effect size. You need the window to be large enough that you get enough data in it to be able to achieve significance, but you need it to be small enough that it doesn't end up larger than the region whose methylation is changing so you don't get averaging against the surrounding regions.
We also don't generally recommend using fixed size windows (eg 500bp), since the uneven distribution of CpGs within the genome means that different windows will have very different numbers of CpGs within them, and you therefore get a statistical power bias which can mean getting biases in the regions of the genome where you find hits. We tend to prefer defining windows based on the number of CpGs then contain, which then means you get a variation in resolution, but a more even statistical power. -
If I use fixed windows versus number of CpG windows as you are suggesting, is the concern that I will get no significance or that I will be flooded with regions of significance that are really false positives?Comment
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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.Today, 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... -
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.
...06-02-2026, 10:05 AM
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