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.
-
-
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
-
CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).
Despite this, “CRISPR helped turn genome editing from a specialized technique into...Yesterday, 11:01 AM -
Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.
The systematic characterization of the human proteome has...07-20-2026, 11:48 AM -
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.
...07-09-2026, 11:10 AM
Comment