MeDIP-seq or BS-seq (WGBS)
It depends on what you want to know. We've switched between MeDIP and BS-Seq a few times and we're now coming to the conclusion that they're both useful and complementary techniques.
MeDIP is great because you can effectively get coverage of a whole genome from a single lane of a GAIIx. Any region you're interested in will be covered and it's not too expensive to run. The downsides are that MeDIP doesn't do well at telling you if there is an overall change in methylation level between samples (ie, the same basic pattern of methylation but with everything reduced). It also can't separate out the influences of methlyation in different contexts (CpG, CHG, CHH), and if these are being regulated independently then you can't extract out the separate changes.
Bisulphite requires much more data than MeDIP to achieve the same level of coverage as MeDIP, and the large volumes of data are more difficult to handle, but once you have the data they're really easy to interpret. All methylation calls are specific down to single base resolution, and there's no problem in separating the methylation levels in different contexts. Methylation calls are absolute and results are clear. Apart from the cost, the only real downside is that it's more difficult to get coverage over repetitive regions in BS-seq (MeDIP does better because you don't directly sequence the repeat, but a sequence slightly offset from it).
If you're only really interested in CpG islands then RRBS-Seq is definitely the way to do. A single illumina lane gives you good coverage of most CpG islands to high depth.
In the study we're currently working on we did both MeDIP and BS-Seq on the samples. Our BS-seq data was low coverage (only 1 GA2x lane), which isn't enough to look at individual regions, but is enough to give us good absolute measures over the whole genome or a functional subset (exons, promoters etc), and is also enough to be able to validate sets of changing regions pulled out by MeDIP analysis. Having both of these kinds of data available and being able to compare between the two has actually proved to be a really nice way of looking at this kind of data.
MeDIP is great because you can effectively get coverage of a whole genome from a single lane of a GAIIx. Any region you're interested in will be covered and it's not too expensive to run. The downsides are that MeDIP doesn't do well at telling you if there is an overall change in methylation level between samples (ie, the same basic pattern of methylation but with everything reduced). It also can't separate out the influences of methlyation in different contexts (CpG, CHG, CHH), and if these are being regulated independently then you can't extract out the separate changes.
Bisulphite requires much more data than MeDIP to achieve the same level of coverage as MeDIP, and the large volumes of data are more difficult to handle, but once you have the data they're really easy to interpret. All methylation calls are specific down to single base resolution, and there's no problem in separating the methylation levels in different contexts. Methylation calls are absolute and results are clear. Apart from the cost, the only real downside is that it's more difficult to get coverage over repetitive regions in BS-seq (MeDIP does better because you don't directly sequence the repeat, but a sequence slightly offset from it).
If you're only really interested in CpG islands then RRBS-Seq is definitely the way to do. A single illumina lane gives you good coverage of most CpG islands to high depth.
In the study we're currently working on we did both MeDIP and BS-Seq on the samples. Our BS-seq data was low coverage (only 1 GA2x lane), which isn't enough to look at individual regions, but is enough to give us good absolute measures over the whole genome or a functional subset (exons, promoters etc), and is also enough to be able to validate sets of changing regions pulled out by MeDIP analysis. Having both of these kinds of data available and being able to compare between the two has actually proved to be a really nice way of looking at this kind of data.
http://seqanswers.com/forums/archive/index.php/t-12346.html
We were just contemplating this issue. Can you give some details on the absolute correlation between the two both genome wide and only at CGIs?
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