Volume 15 Supplement 12
Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2013): Bioinformatics
BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data
- Zijing Mao†^{1},
- Chifeng Ma†^{1},
- Tim H-M Huang^{2, 5},
- Yidong Chen^{3, 4, 5}Email author and
- Yufei Huang^{1, 3}Email author
https://doi.org/10.1186/1471-2105-15-S12-S6
© Mao et al.; licensee BioMed Central Ltd. 2014
Published: 6 November 2014
Abstract
DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer.
Keywords
DNA methylation differential methylation MBDCap-seq Hidden Markov Model (HMM)Introduction
DNA methylation refers to the chemical modification of DNA nucleotides. One of the most common DNA methylation is the modification of cytosine, which typically occurs in CpG sites. When CpG sites in the promoter region that transcription factors bind are methylated, permanent silencing of gene expression is observed in the cell. DNA methylation is highly prevalent in cancer, involved in almost all types of cancer development by altering the normal regulation of gene expression and silencing the tumor suppressor genes [1]. There are three sequencing-based technologies for whole-genome DNA methylation profiling: bisulfite treatment[2] based or bisulfite sequencing, methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq)[3], and Methyl-CpG binding domain-base capture followed by sequencing (MBDCap-seq)[4]. Among the three technologies, MBDCap-seq has higher dynamic range and better sensitivities and it detects more enrichment in CpG-dense methylated DNA regions [5, 6]. We choose to focus on MDBCap-seq data analysis in this study.
Two computational problems concern these genome-wide methylation data including methylation site detection and differential methylation region (DMR) detection. The problem of methylation site detection is similar to the peak detection for ChIP-seq. However, since methylation signals give rise to wider sequence read distribution than that from ChIP-seq peak identification algorithms such as SPP[7] and MACS[8] that are designed primarily for ChIP-seq data analysis would produce poor identification of methylation sites. Specific changes and new algorithms have been proposed to account for the nature of wider read distribution in methylation sequencing data. For instance, Hidden Markov Model [9, 10] have been proposed to model the correlation between adjacent bins of a methylation site. The main aim of DMR detection is to identify aberrant DNA methylation regions that are specifically associated with disease phenotype. It is also fundamental to understanding the cause of altered gene expression in cancer. Most of the popular DMR detection pipelines includes two parts: the first part concerns detection of methylation sites in normal and disease samples individually and the second part includes identification of differential methylated regions in disease sample versus normal samples [11]. Many algorithms for differential methylation detection have been proposed including for example ChIPnorm [11] and ChIPDiff [12]. ChIPnorm performs a quantile normalization on normal and disease samples and applies differential analysis to detect DMRs, whereas ChIPDiff detects enriched methylation regions in normal and disease samples with a Binomial model, and then performs differential analysis based on a HMM. These existing algorithms are very powerful tools in differential methylation analysis but they have clear disadvantages especially when applied to MBDCap-seq. First, the targeted resolutions of the data are relatively low, for instance, the bin size in ChIPDiff is 1000 base pairs (bps). However for a typical MBDCap-seq data, the resolution is normally 100bp bins. Second, these existing pipelines mentioned above are two-step procedures, which are prone to error propagation. If there is an error in methylation site detection, this error will be passed on to the following DMR detection step and impact negatively the performance of differential methylation. Last but not the least, with an exception in [13], most existing algorithms were developed to handle single sample. When replicates are available, they perform prediction on individual samples separately and then fuse the detection results from individual together. Such fusion based algorithms is easily influenced by the erroneous predictions made at individual level. The algorithm in [13] applies LOESS to normalize the difference between replicate samples. However, it assumes that only a small portions of methylation regions are DMRs [11], which might not be applicable to all cases.
In this paper, we proposed a novel algorithm for differential methylation regions (DMRs) detection based on Hidden Markov Model (HMM) and we call the algorithm BIMMER. BIMMER models the methylation status and detect DMRs in normal and cancer samples simultaneously. By doing this, BIMMER avoids error propagation in the existing two-step pipelines and therefore can improve the performance of DMRs detection. BIMMER was tested first on a simulated datasets and applied to a real breast cancer MBDCAP-seq data. The results from breast cancer data revealed that there are 8.83% of 30,804,183 bines detected with differentially methylated status, most of which are hypo-methylated in breast cancer samples.
Methods
Notation
Each MBDCap-seq data sample is pre-processed to be in a BED file, which records the sequence reads counts in consecutive 100 base pair (bp) bins over the entire genome. Let's denote the sample size of the normal sample MBDCap-Seq datasets as ${N}_{1}$, that of the cancer dataset as ${N}_{2}$, and the total number of the bins is denoted as $\phantom{\rule{0.1em}{0ex}}M$. We further denote the reads count of the ${i}_{th}$ bin in ${N}_{1}$ normal samples by a vector${X}_{{n}_{i}}={\left[{X}_{{n}_{i}.1},{X}_{{n}_{i}.2},\dots ,{X}_{{n}_{i}.{N}_{1}}\right]}^{\top}$, where ${X}_{{n}_{i},j}$ is the reads count of the ${j}_{th}$ sample, and similarly the reads count of the ${i}_{th}$ bin in ${N}_{2}$ cancer samples by ${X}_{{c}_{i}}={\left[{X}_{{c}_{i}.1},{X}_{{c}_{i}.2},\dots ,{X}_{{c}_{i}.{N}_{2}}\right]}^{\top}$, where ${X}_{c\text{\_}i,k}$ represents the reads count in the ${k}_{th}$ sample. The aim of this work is to predict the differential methylation status of the cancer samples over the normal samples for every bin in the genome.
Two layer HMM model for differential methylation
The EM solution
and
$\begin{array}{c}P\left({X}_{n,i+1:M},{X}_{c,i+1:m}\text{|}d{m}_{i},{m}_{ci},{m}_{ni},{\Psi}^{k-1}\right)=\hfill \\ {\displaystyle \sum _{d{m}_{i-1}}^{}}{\displaystyle \sum _{{m}_{c\left(i-1\right)}}^{}}{\displaystyle \sum _{{m}_{n\left(i-1\right)}}^{}}P\left(d{m}_{i+1},{m}_{n\left(i+1\right)},{m}_{c\left(i+1\right)},{X}_{n,i+1:M},{X}_{c,i+1:M}|d{m}_{i},{m}_{ci},{m}_{ni},{\Psi}^{k-1}\right)\hfill \end{array}$.
$=P\left({X}_{ni}|{m}_{ni},{\Psi}^{k-1}\right)\times P\left({X}_{n,i+2:M},{X}_{c,i+2:m}\text{|}d{m}_{i+1},{m}_{c\left(i+1\right)},{m}_{n\left(i+1\right)},{\Psi}^{k-1}\right)$.
The maximization yields
where the last equation is calculated by the Newton-Raphson algorithm. Maximizing this $\phantom{\rule{0.1em}{0ex}}Q$ function guarantees that the likelihood $L\left({\Psi}^{k}\right)$ is always greater than $L\left({\Psi}^{k-1}\right)$, hence ensures global convergence of the solution.
Model initialization and prediction of DMRs
To implement the EM algorithm, the initial parameter set Φ^{(0)} and the parameters for the first layer needs to be carefully defined because specific choice of these initial parameter values could lead to difference local optimal solutions and affect the prediction performance. After the convergence of the EM solution, the differential methylation statuses, dm, are predicted using the Viterbi algorithm[14] as the chain of the states with the largest probability given the estimated parameter set. Additionally, the methylation statuses ${m}_{c}\phantom{\rule{0.3em}{0ex}}\text{and}\phantom{\rule{0.3em}{0ex}}{m}_{n}$ can also be predicted using the Viterbi algorithm provided the parameters of the first layer HMM are set to the estimated ones.
Results
BIMMER was validated on both simulated data and applied to a real breast cancer dataset. It was first tested on the simulated systems, where the data models were assumed known. Then, BIMMER was applied to a real breast cancer dataset to explore the state of differential methylation.
Test on simulated data
Parameter set used for simulation.
Table 1-1 | ||||||
---|---|---|---|---|---|---|
${\tau}_{c}$ | ${A}_{c}$ | ${m}_{c}$ Likelihood Of | ||||
0.99999 | 0.7296 | 0.2704 | -1.619118E7 | |||
0.00001 | 0.0225 | 0.9775 | ||||
Table 1-2 | ||||||
${\tau}_{n}$ | ${A}_{n}$ | ${m}_{n}$ Likelihood Of | ||||
0.99999 | 0.7614 | 0.2386 | -2.928487E7 | |||
0.00001 | 0.0563 | 0.9437 | ||||
Table 1-3 | ||||||
Symbols | 0 | 1 | 2 | 3 | 4 | 5 |
${m}_{c}$ = 0 | 0.9 | 0.04 | 0.03 | 0.01 | 0.01 | 0.01 |
${m}_{c}$ = 1 | 0.26 | 0.24 | 0.2 | 0.18 | 0.08 | 0.04 |
Table 1-4 | ||||||
Symbols | 0 | 1 | 2 | 3 | 4 | 5 |
${m}_{n}$ = 0 | 0.8 | 0.08 | 0.07 | 0.03 | 0.01 | 0.01 |
${m}_{n}$ = 1 | 0.22 | 0.26 | 0.20 | 0.16 | 0.1 | 0.06 |
Table 1-5 | ||||||
${\tau}_{dm}$ | ${A}_{dm}$ | Weight $\phantom{\rule{0.1em}{0ex}}\alpha $ | Likelihood Of dm | |||
0.99999 | 0.9705 | 0.0295 | 0.3519 | -4.538562E8 | ||
0.00001 | 0.2862 | 0.7138 |
Estimated parameters after training using different initial weight: 0.01 and 0.3
Weight of simulator | 0.3 | 0.2 | 0.1 | ||||
---|---|---|---|---|---|---|---|
Initial weight for training | 0.01 | 0.3 | 0.01 | 0.3 | 0.01 | 0.3 | |
Transition of simulator | Weight | 0.2820 | 0.2850 | 0.1993 | 0.2002 | 0.0974 | 0.0980 |
0.97 | Differential Transition | 0.9705 | 0.9710 | 0.9698 | 0.9699 | 0.9689 | 0.9690 |
0.71 | 0.7146 | 0.7173 | 0.7186 | 0.7192 | 0.7048 | 0.7044 | |
0.76 | Patient Transition | 0.7584 | 0.7584 | 0.7545 | 0.7545 | 0.7594 | 0.7594 |
0.97 | 0.9698 | 0.9698 | 0.9698 | 0.9698 | 0.9699 | 0.9699 | |
0.66 | Normal Transition | 0.6300 | 0.6300 | 0.6562 | 0.6562 | 0.6867 | 0.6867 |
0.92 | 0.9173 | 0.9173 | 0.9217 | 0.9217 | 0.9289 | 0.9289 |
Test on real data
To demonstrate the utility and further validate the performance of BIMMER, we applied BIMMER to a real dataset published in [4], which includes MBDCap-seq reads of whole genome methylation profiles from 10 normal and 75 breast cancer tissues from the 1000 methylome project (http://cbbiweb.uthscsa.edu/KMethylomes). The raw reads (FASTQ) file of MBDCap-seq data was first aligned to UCSC hg18 genome by BWA aligner [17]. The aligned SAM file was then converted to BED format later for further analysis.
Initial values and the prior probabilities of BIMMER.
${m}_{n}$ | 1 | 0 | ${m}_{c}$ | 1 | 0 | dm | 1 | 0 | State | ${m}_{c}$ | ${m}_{n}$ | dm | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0.9 | 0.1 | 1 | 0.9 | 0.1 | 1 | 0.9 | 0.1 | 0 | 0.1 | 0.1 | 0.9 | ||||||
0 | 0.1 | 0.9 | 0 | 0.1 | 0.9 | 0 | 0.1 | 0.9 | 1 | 0.9 | 0.9 | 0.1 | ||||||
Table. 3-1 | Table. 3-2 | Table. 3-3 | Table. 3-4 |
The estimated parameters of the second hidden layer
${\tau}_{dm}$ | ${A}_{dm}$ | Weight$\phantom{\rule{0.1em}{0ex}}\alpha $ | |
---|---|---|---|
0.99999 | 0.9705 | 0.0295 | 0.3519 |
0.00001 | 0.2862 | 0.7138 |
Differential rate of 4 regions on 24 chromosomes
Chromosome | Promoter | Exon | Enhancer | Gene Body |
---|---|---|---|---|
Chr1 | 0.005729 | 0.015478 | 2.331E-4 | 3.890E-4 |
Chr2 | 0.003440 | 0.009209 | 1.528E-4 | 2.357E-4 |
Chr3 | 0.001957 | 0.006006 | 9.138E-4 | 1.402E-4 |
Chr4 | 0.002017 | 0.005697 | 8.264E-5 | 1.336E-4 |
Chr5 | 0.001348 | 0.004764 | 9.434E-5 | 9.545E-5 |
Chr6 | 0.001857 | 0.005404 | 1.131E-4 | 1.300E-4 |
Chr7 | 0.002172 | 0.005126 | 1.022E-4 | 1.287E-4 |
Chr8 | 0.002223 | 0.004775 | 8.555E-5 | 1.182E-4 |
Chr9 | 0.001470 | 0.003957 | 6.925E-5 | 1.079E-4 |
Chr10 | 0.001583 | 0.004071 | 6.994E-5 | 1.044E-4 |
Chr11 | 0.001412 | 0.003576 | 6.113E-5 | 9.748E-5 |
Chr12 | 0.001212 | 0.003218 | 5.683E-5 | 8.236E-5 |
Chr13 | 0.001256 | 0.002956 | 5.161E-5 | 7.356E-5 |
Chr14 | 0.001070 | 0.002745 | 4.777E-5 | 6.970E-5 |
Chr15 | 0.001162 | 0.002940 | 4.717E-5 | 7.995E-5 |
Chr16 | 0.001172 | 0.002930 | 5.095E-5 | 8.412E-5 |
Chr17 | 0.001196 | 0.002969 | 5.242E-5 | 8.350E-5 |
Chr18 | 0.001090 | 0.002745 | 4.886E-5 | 7.272E-5 |
Chr19 | 8.860E-4 | 0.002388 | 4.385E-5 | 6.917E-5 |
Chr20 | 0.001014 | 0.002686 | 5.052E-5 | 7.976E-5 |
Chr21 | 9.763E-4 | 0.002416 | 4.783E-5 | 6.888E-5 |
Chr22 | 9.368E-4 | 0.002513 | 4.462E-5 | 7.624E-5 |
ChrX | 6.024E-4 | 0.001642 | 2.793E-5 | 4.002E-5 |
ChrY | 4.400E-4 | 0.001178 | 2.086E-5 | 2.979E-5 |
Total | 0.001225 | 0.003201 | 5.644E-5 | 8.452E-5 |
Top 20 differential methylated gene
GENE SYMBOL | DIFFMETHY RATE | METHYLATION STATUS |
---|---|---|
CDC5L | 0.380952381 | 0 |
BCL3 | 0.333333333 | 0 |
C6ORF123 | 0.333333333 | 0 |
C6ORF124 | 0.333333333 | 0 |
COX6B1 | 0.333333333 | 0 |
CRYAB | 0.333333333 | 0 |
GRIP2 | 0.333333333 | 0 |
HSD17B1 | 0.333333333 | 0 |
NAGLU | 0.333333333 | 0 |
OR5M11 | 0.333333333 | 0 |
PHTF2 | 0.333333333 | 0 |
PIH1D2 | 0.333333333 | 0 |
PTPN12 | 0.333333333 | 0 |
RSBN1L | 0.333333333 | 0 |
SFTPD | 0.333333333 | 0 |
TAAR6 | 0.333333333 | 0 |
TAAR8 | 0.333333333 | 0 |
C6ORF192 | 0.317460317 | 1 |
AKR1C4 | 0.285714286 | 0 |
APOC2 | 0.285714286 | 0 |
Differential rate of normal and patient samples for 22 breast cancer related genes
Gene Name | Relation with Breast Cancer | Differential Methylation Status | Differential Rate |
---|---|---|---|
RECK | Related to Survival | Yes | 0.2195 |
SFRP2 | Related to Survival | No | |
ITR | Related to Survival | Not maped | |
UGT3A1 | Related to Survival | No | |
ACADL | Related to Survival | Yes | 0.3659 |
UAP1L1 | Related to Survival | Yes | 0.2195 |
HSD17B12 | Related to Tumor Size | No | |
IMPACT | Related to Tumor Size | Yes | 0.2683 |
IL6 | Related to Tumor Size | Yes | 0.3171 |
PLAT | Related to Tumor Size | No | |
NCL | Related to Tumor Size | No | |
FES | Related to Tumor Size | No | |
PLAUR | Related to Tumor Size | No | |
ALK | Related to Tumor Size | No | |
IRF7 | Related to ER+ | No | |
RARA | Related to ER+ | Yes | 0.2195 |
ACG2 | Related to ER+ | No | |
AXL | Related to ER+ | No | |
ZNF264 | Related to ER+ | No | |
DAB2IP | Related to ER+ | Yes | 0.1951 |
FZD9 | Related to ER+ | No | |
SRC | Related to ER+ | No |
Discussion and future work
In this work, BIMMER, an HMM based algorithm for DMRs detection for MBDCap-seq data is proposed. BIMMER models the methylation status and differential methylation status simultaneously, which does not suffer from the error propagation of existing two-step DMRs detection algorithms. In addition, BIMMER can handle replicate samples at the same time, producing more coherent detections. BIMMER relies on an EM algorithm to estimate the model parameters jointly. BIMMER was validated using simulated data and applied to real breast cancer datasets.
In the future work, four possible aspects could contribute to the performance improvement of BIMMER. First, adding more states of differential methylation into the HMM model and including hyper- and hypo- methylation type status will clearly provide better interpretation of the result. Second, more accurate models can be developed to model the differential methylation status and methylations in different phenotype of samples. Third, more accurate solution could be introduced to replace the weighted average approach. For example, product of experts (PoE) [32] has been shown to be a power tool in recent studies. Finally, more epigenetic information such as CpG island or histone modification can be included into BIMMER to produce biologically more relevant results.
Notes
Declarations
Declaration
Publication of this article has been funded by NSF Grant (CCF-1246073) to YH and a Qatar National Research Fund(09-897-3-235)to YH and YC to support this project. This work also has been supported by the help of UTSA Computational System Biology Core,funded by the National Institute on Minority Health and Health Disparities(G12MD007591) from the National Institutes of Health.
This article has been published as part of BMC Bioinformatics Volume 15 Supplement 12, 2014: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2013): Bioinformatics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S12.
Authors’ Affiliations
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