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What is the best option for methylation analysis at this point? We would like to analyze methylation in mouse liver samples and are considering all options: bisulfite, MeDIP, MBD sequencing, etc. We'll be using a GAII, and It's been a while since we've done this, so we're wondering what the easiest way is to get good data at this point?
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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. -
Harris RA, et al. 2010. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol. 2010 Oct;28(10):1097-105.
Source
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
Abstract
Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression.
Comment in
Nat Biotechnol. 2010 Oct;28(10):1026-8.
PMID: 20852635 [PubMed - indexed for MEDLINE] PMCID: PMC2955169Last edited by epistatic; 08-11-2011, 11:31 AM.Comment
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When you say you can't get enough coverage, which stage of your experiment is failing? Are you able to generate a BS-Seq library? (or RRBS?) Is your library diverse (ie not full of duplicated sequences)? Is your sequencing quality OK? Is your mapping efficiency OK? How are you judging that you're not getting enough coverage?Originally posted by jamal View Post
There's quite a few places these experiments can go wrong so it's useful to know where to focus.Comment
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Hi
thank you for your reply
Actually I used Lister et al protocol and made library according to paired-end sequensing sample preparation guide(illumina) and used QIAGEN EpiTect 96 Bisulfite Kit twice for bisulfite conversion. our sequencing had good quality and I used several software for mapping. but mapping efficency was around 60% and then when I calculate the coverage using "genomeBedcoverage" it showed that we don't have any read for around 45% of genome.
I think my problem is making library and bisulfite treatment. because I checked sequencing and mapping with other data and they were ok.
so I wanted to compair my procedure of making library and bisulfite treatment with yours.
I'm thankful if you guide me.
jamal
Originally posted by simonandrews View PostComment
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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.07-01-2026, 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...
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