--- title: "DAMEfinder Workflow" author: - name: Stephany Orjuela affiliation: - &IMLS Institute for Molecular Life Sciences, University of Zurich - &IMCR Institute for Molecular Cancer Research, University of Zurich - &SIB SIB Swiss Institute of Bioinformatics, Switzerland email: sorjuelal@gmail.com - name: Dania Machlab affiliation: - &FMI Friedrich Miescher Institute for Biomedical Research, Basel - *SIB - name: Mark Robinson affiliation: - *IMLS - *SIB date: "`r Sys.Date()`" package: DAMEfinder output: BiocStyle::html_document vignette: > %\VignetteIndexEntry{DAMEfinder Workflow} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} bibliography: papers.bib --- ```{r setup, include=FALSE} knitr::opts_chunk$set(cache = 0, fig.width = 6, fig.height = 7) ``` ```{r style, echo = FALSE, results = 'asis'} BiocStyle::markdown() ``` # Introduction ## What is allele-specific methylation? The phenomenon occurs when there is an asymmetry in methylation between one specific allele and the alternative allele [@hu2013]. The best studied example of allele-specific methylation (ASM) is genomic imprinting. When a gene is imprinted, one of the parental alleles is hyper-methylated compared to the other allele, which leads to parent-allele-specific expression. This asymmetry is conferred in the gametes or very early in embryogenesis, and will remain for the lifetime of the individual [@kelsey2013]. ASM not related to imprinting, exhibits parental-specific methylation, but is not inherited from the germline [@hanna2017]. Another example of ASM is X chromosome inactivation in females. DAMEfinder detects ASM for several bisulfite-sequenced (BS-seq) samples in a cohort, and performs differential detection for regions that exhibit loss or gain of ASM. # Overview We focus on any case of ASM in which there is an imbalance in the methylation level between two alleles, regardless of the allele of origin. DAMEfinder runs in two modes: **SNP-based** (exhaustive-mode) and **tuple-based** (fast-mode), which converge when differential ASM is detected. ## Why **SNP-based**? This is the exhaustive mode because it extracts an ASM score for every CpG site in the reads containing the SNPs in a VCF file. Based on this score, DAMEs are detected. From a biological point of view, you might want to run this mode if you are interested in loss or gain of allele-specificity linked to somatic or germline heterozygous SNPs (sequence-dependent ASM). More specifically, you could detect genes that exhibit loss of imprinting (e.g. as in colorectal cancer [@cui2002]). ## Why **tuple-based**? To run the **tuple-based** mode you have to run [methtuple](https://github.com/PeteHaitch/methtuple)[@hickey2015] first. The methtuple output is the only thing needed for this mode. I call this the fast-mode because you don't need SNP information. The assumption is that intermediate levels of methylation represent ASM along the genome. For example, we have shown (paper in prep) that the ASM score can distinguish females from males in the X chromosome. Using SNP information this wouldn't be possible. ## Installation ```{r eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("DAMEfinder") ``` # Get bam files In order to run any of the two modes, you must obtain aligned bam files using [`bismark`](http://www.bioinformatics.babraham.ac.uk/projects/bismark/). Here we demonstrate how to generate these starting from paired-end fastq files of bisulfite-treated reads: ```{bash eval=FALSE} #Check quality of reads fastqc -t 2 sample1_R1.fastq.gz sample1_R2.fastq.gz #Trim reads to remove bad quality regions and adapter sequence trim_galore --paired sample1_R1.fastq.gz sample2_R2.fastq.gz ``` To trim the reads we use [`Trim Galore`](https://github.com/FelixKrueger/TrimGalore) and specify the use of paired reads. By default it will remove any adapter sequence it recognizes. Please refer to the user guide for further specifications. ```{bash eval=FALSE} #Build bisulfite reference bismark_genome_preparation #run Bismark bismark -B sample1 --genome -1 sample1_R1_val_1.fq.gz -2 sample1_R2_val_2.fq.gz #deduplicate (optional) deduplicate_bismark -p --bam sample1_pe.bam #sort and index files samtools sort -m 20G -O bam -T _tmp -o sample1_pe.dedupl_s.bam sample1_pe.deduplicated.bam samtools index file1_pe.dedupl_s.bam ``` Before the alignment, you must download a reference fasta file from [Ensembl](https://www.ensembl.org/info/data/ftp/index.html) or [Gencode](https://www.gencodegenes.org/), and generate a bisulfite converted reference. For this we use `bismark_genome_preparation` from the `bismark` suite, and specify the folder that contains the fasta file with its index file. Depending on the library type and kit used to obtain the reads, you may want to deduplicate your bam files (e.g. TruSeq). Please refer to the [user guide](http://www.bioinformatics.babraham.ac.uk/projects/bismark/) for further explanation and specifications. # SNP-based (aka slow-mode) To run the SNP-based mode, you need to additionally have a VCF file including the heterozygous SNPs per sample. If you do not have this, we recommend using the tuple-based mode, or running [`Bis-SNP`](http://people.csail.mit.edu/dnaase/bissnp2011/) to obtain variant calls from bisulfite-converted reads. ## Example Workflow In this example we use samples from two patients with colorectal cancer from a published dataset [@parker2018]. For each patient two samples were taken: `NORM#` corresponds to normal mucosa tissue and `CRC#` corresponds to the paired adenoma lesion. Each of these samples was sequenced using targeted BS-seq followed by variant calling using `Bis-SNP`. ### Obtain allele-based methylation calls Similar to the `bismark_methylation_extractor`, we obtain methylation calls. However since we are interested in allele-specific methylation, we only extract methylation for CpG sites that fall within reads including a SNP. For every SNP in the VCF file an independent methylation call is performed by using `extract_bams`, which "extracts" reads from the bam file according to the alleles, and generates a `list` of `GRangesList`s: ```{r} suppressPackageStartupMessages({ library(DAMEfinder) library(SummarizedExperiment) library(GenomicRanges) library(BSgenome.Hsapiens.UCSC.hg19) }) bam_files <- c(system.file("extdata", "NORM1_chr19_trim.bam", package = "DAMEfinder"), system.file("extdata", "CRC1_chr19_trim.bam", package = "DAMEfinder")) vcf_files <- c(system.file("extdata", "NORM1.chr19.trim.vcf", package = "DAMEfinder"), system.file("extdata", "CRC1.chr19.trim.vcf", package = "DAMEfinder")) sample_names <- c("NORM1", "CRC1") #Use another reference file for demonstration, and fix the seqnames genome <- BSgenome.Hsapiens.UCSC.hg19 seqnames(genome) <- gsub("chr","",seqnames(genome)) reference_file <- DNAStringSet(genome[[19]], use.names = TRUE) names(reference_file) <- 19 #Extract reads and extract methylation according to allele snp.list <- extract_bams(bam_files, vcf_files, sample_names, reference_file, coverage = 2) #CpG sites for first SNP in VCF file from sample NORM1 snp.list$NORM1[[1]] #CpG sites for first SNP in VCF file from sample CRC1 snp.list$CRC1[[1]] ``` For demonstration, we include bam files from chromosome 19, and shortened VCF files. Typically we would run the function on an entire bam and VCF file, which would generate a large output. The function also takes as input the reference file used to generate the alignments. For demonstration we use chromosome 19 of the `GRCh37.91` reference fasta file. ### Summarize methylation calls across samples We use `calc_derivedasm()` to generate a `RangedSummarizedExperiment` from the large list we generated above: ```{r} derASM <- calc_derivedasm(snp.list) derASM assays(derASM) ``` Every row in the object is a single CpG site, and each column a sample. It contains 6 matrices in `assays`: * `der.ASM`: A derived SNP-based ASM defined as $abs(\frac{X^{r}_M}{X^{r}} - \frac{X^{a}_M}{X^{a}})$, where $X$ is the coverage in the reference $r$ or alternative allele $a$, and $X_M$ the number of methylated reads in $r$ or $a$. Basically, CpG sites with values of 1 or close to 1 have more allele-specificity. ASM of 1 represents the perfect scenario in which none of the reads belonging to one allele are methylated, and the reads of the other allele are completely methylated. * `snp.table`: Location of the SNP associated to the CpG site. * `ref.cov`: Coverage of the "reference" allele. * `alt.cov`: Covearage of the "alternative" allele. * `ref.meth`: Methylated reads from the "reference" allele. * `alt.meth`: Methylated reads from the "alternative" allele. You can access these assays as: ```{r} x <- assay(derASM, "der.ASM") head(x) ``` ### Find DAMEs Now we detect regions that show differential ASM. The function `find_dames()` performs several steps: 1. Obtains a moderated t-statistic per CpG site using `lmFit()` and `eBayes()` from the `limma` package. The statistic reflects a measure of difference between the conditions being compared, in this case normal Vs cancer. The t-statistic is optionally smoothed (`smooth` parameter). After this, two methods can be chosen (`pvalAssign` parameter): * Simes method: 2. (Default) Clusters of CpG sites are determined by closeness (`maxGap`), and a p-value for each cluster is calculated using the simes method, similar to the package `csaw` from @lun2014. With this approach, the p-value represents evidence against the null hypothesis that no sites are differential in the cluster. * Bumphunting method: 2. CpG sites with a t-statistic above and below a certain cutoff (set with `Q`), are grouped into segments (after being clustered). This is done with the `regionFinder()` function from `bumphunter` [@jaffe2012]. 3. For each of these segments, a p-value is calculated empirically by permuting the groups (covariate) of interest. Depending on the number of samples, this can take longer than the Simes method. However the number of permutations can be controlled with `maxPerms`. Here we show an example with a pre-processed set of samples: 4 colorectal cancer samples, and their paired normal mucosa: ```{r} data(extractbams_output) #The data loaded is an output from `split_bams()`, therefore we run #`calc_derivedasm` to get the SummarizedExperiment derASM <- calc_derivedasm(extractbams_output, cores = 1, verbose = FALSE) #We remove all CpG sites with any NA values, but not 0s filt <- rowSums(!is.na(assay(derASM, "der.ASM"))) == 8 derASM <- derASM[filt,] #set the design matrix grp <- factor(c(rep("CRC",4),rep("NORM",4)), levels = c("NORM", "CRC")) mod <- model.matrix(~grp) mod #Run default dames <- find_dames(derASM, mod) head(dames) #Run empirical method dames <- find_dames(derASM, mod, pvalAssign = "empirical") head(dames) ``` A significant p-value represent regions where samples belonging to one group (in this case the cancer samples), gain or lose allele-specificity compared to the other group (here the normal group). # tuple-based (aka fast-mode) Before running the tuple-based mode, you must obtain files from the `methtuple` tool to input them in the `read_tuples` function. ## Run Methtuple on bam files Methtuple requires the input `BAM` files of paired-end reads to be sorted by query name. For more information on the options in `methtuple`, refer to the user [guide](https://github.com/PeteHaitch/methtuple). For example the `--sc` option combines strand information. ```{bash, eval=FALSE} # Sort bam file by query name samtools sort -n -@ 10 -m 20G -O bam -T _tmp -o sample1_pe_sorted.bam sample1_pe.deduplicated.bam # Run methtuple methtuple --sc --gzip -m 2 sample1_pe_sorted.bam ``` ## Example Workflow ### Read methtuple files We use the same samples as above to run `methtuple` and obtain `.tsv.gz` files. We read in these files using `read_tuples` and obtain a list of `tibble`s, each one for every sample: ```{r} tuple_files <- c(system.file("extdata", "NORM1_chr19.qs.CG.2.tsv.gz", package = "DAMEfinder"), system.file("extdata", "CRC1_chr19.qs.CG.2.tsv.gz", package = "DAMEfinder")) sample_names <- c("NORM1", "CRC1") tuple_list <- read_tuples(tuple_files, sample_names) head(tuple_list$NORM1) ``` Each row in the `tibble` displays a tuple. The chromosome name and strand are shown followed by `pos1` and `pos2`, which refer to the genomic positions of the first and second CpG in the tuple. The `MM`, `MU`, `UM`, and `UU` counts of the tuple are displayed where `M` stands for methylated and `U` for unmethylated. For example, `UM` shows the read counts for the instances where `pos1` is unmethylated and `pos2` is methylated. The coverage and distance between the two genomic positions in the tuple are shown under `cov` and `inter_dist` respectively. ### Calculate ASM Score The `calc_asm` function takes the output from `read_tuples()`, and as in the SNP-based mode, generates a `RangedSummarizedExperiment` where each row is a tuple and each column is a sample. The object contains 6 assays including the `MM`, `MU`, `UM`, and `UU` counts, as well as the total coverage and the tuple-based ASM score. This score is a measure of ASM calculated directly from the reads without the need of SNP information. Because of this, it is a lot quicker than the SNP-based ASM, and is useful for more explorative purposes. Equations \@ref(eq:asmGeneral), \@ref(eq:asmWeight) and \@ref(eq:asmTheta) show how the score is calculated. The log odds ratio in equation \@ref(eq:asmGeneral) provides a higher score the more `MM` and `UU` counts the tuple has, whereas a higher `UM` and `MU` would indicate "random" methylation. The weight further adds allele-specificity where a rather balanced MM:UU increases the score. \begin{equation} ASM^{(i)} = log{ \Big\{ \frac{X_{MM}^{(i)} \cdot X_{UU}^{(i)}}{X_{MU}^{(i)} \cdot X_{UM}^{(i)}} \Big\} \cdot w_i } (\#eq:asmGeneral) \end{equation} \begin{equation} w_i = P(0.5-\epsilon < \theta < 0.5+\epsilon~|~ X_{MM}^{(i)}, X_{UU}^{(i)}, \beta_1, \beta_2) (\#eq:asmWeight) \end{equation} \begin{equation} \theta^{(i)} | X_{MM}^{(i)}, X_{UU}^{(i)},\beta_1, \beta_2 \sim Beta(\beta_1+X_{MM}^{(i)}, \beta_2+X_{UU}^{(i)}) (\#eq:asmTheta) \end{equation} where $\theta^{(i)}$ represents the moderated proportion of MM to MM+UU alleles. The weight, $w_i$ is set such that the observed split between MM and UU alleles can depart somewhat from 50/50, while fully methylated or unmethylated tuples, which represents evidence for absence of allele-specificity, are attenuated to 0. The degree of allowed departure can be set according to $\epsilon$, the deviation from 50/50 allowed and the level of moderation, $\beta_1$ and $\beta_2$. ```{r} ASM_mat <- calc_asm(tuple_list) ASM_mat ``` ### Find DAMEs As above, the `RangedSummarizedExperiment` is used to detect differential ASM. Here we show an example with a pre-processed set of samples: 3 colorectal cancer samples, an 2 normal mucosa samples ```{r} #load package data data(readtuples_output) #run calc_asm and filter object ASMscore <- calc_asm(readtuples_output) filt <- rowSums(!is.na(assay(ASMscore, "asm"))) == 5 #filt to avoid warnings ASMscore <- ASMscore[filt,] #make design matrix (or specify a contrast) grp <- factor(c(rep("CRC",3),rep("NORM",2)), levels = c("NORM", "CRC")) mod <- model.matrix(~grp) #run default and increase maxGap to get longer, more sparse regions dames <- find_dames(ASMscore, mod, maxGap = 300) head(dames) #run alternative mode dames <- find_dames(ASMscore, mod, maxGap = 300, pvalAssign = "empirical") head(dames) ``` # Visualization ## DAME tracks After detecting a set of DAMEs you want to look at them individually. We do this with the function `dame_track`. Depending on which object I used to obtain my DAMEs (tuple or SNP mode), I choose which SummarizedExperiment to input in the field `ASM` (for tuple), or `derASM` (for SNP). Either way, the SummarizedExperiment must have the columns `group` and `samples` in the `colData` field: ```{r, dametrack} #Here I will use the tuple-ASM SummExp colData(ASMscore)$group <- grp colData(ASMscore)$samples <- colnames(ASMscore) #Set a DAME as a GRanges. I choose a one from the tables we obtained above dame <- GRanges(19,IRanges(323736,324622)) dame_track(dame = dame, ASM = ASMscore) ``` Because we used the tuple-ASM object, we get by default two tracks: the ASM score, and the marginal methylation (aka beta-value). The shaded square delimits the DAME we defined to plot. We can look at the flanking regions of the DAME by changing `window` or `positions`. With `window` we specify the number of CpG positions we want to add to the plot up and down-stream. With `positions` we specify the number of base pairs. ```{r, dt2} dame_track(dame = dame, ASM = ASMscore, window = 2) ``` If we use the SNP-ASM as input we get different tracks: ```{r, dt3} dame <- GRanges(19,IRanges(387966,387983)) grp <- factor(c(rep("CRC",4),rep("NORM",4)), levels = c("NORM", "CRC")) colData(derASM)$group <- grp dame_track(dame = dame, derASM = derASM) ``` Here we get three tracks: the SNP-ASM score, and the methylation levels for each allele. Since the ASM score here depends on SNPs, we can see what SNPs are involved in the ASM calculation at each CpG position: ```{r, dt4} dame_track(dame = dame, derASM = derASM, plotSNP = TRUE) ``` We see that the SNP located at `chr19:388,065` was the one used to split the allele methylation. If you put both SummarizedExperiments with a single DAME, you would get all the tracks: ```{r, dt5} dame_track(dame = dame, derASM = derASM, ASM = ASMscore) ``` Notice that the first two tracks depend on the tuple-ASM, hence each point represents the midpoint between a pair of CpG sites. If you think plotting all the samples separately is difficult to see, you can use the function `dame_track_mean` to summarize: ```{r, dt6} dame_track_mean(dame = dame, derASM = derASM, ASM = ASMscore) ``` As you can see, this region is not a very good DAME. ## Methyl-circle plot A typical way of visualizing ASM is to look at the reads overlapping a particular SNP, and the methylation state of the CpG sites in those reads (black circles for methylated and white for unmethylated, see @shoemaker2010 for examples). Here we offer this option with the function `methyl_circle_plot()`. As input it takes a `GRanges` with the SNP of interest, and the bam, VCF and reference files as in the `extract_bams()` function. ```{r, fig1} #put SNP in GRanges (you can find the SNP with the dame_track function) snp <- GRanges(19, IRanges(267039, width = 1)) #always set the width if your #GRanges has 1 site snp bam.file <- system.file("extdata", "CRC1_chr19_trim.bam", package = "DAMEfinder") vcf.file <- system.file("extdata", "CRC1.chr19.trim.vcf", package = "DAMEfinder") methyl_circle_plot(snp = snp, vcfFile = vcf.file, bamFile = bam.file, refFile = reference_file) ``` You can reduce the number of reads included with the option `sampleReads`, which performs a random sampling of the number of reads to be shows per allele. The number of reads can be specified with `numReads`. If you are interested in a specific CpG site within this plot, you can include an extra `GRanges` with its location, and the triangle at the bottom will point to it: ```{r, fig2} cpgsite <- GRanges(19, IRanges(266998, width = 1)) methyl_circle_plot(snp = snp, vcfFile = vcf.file, bamFile = bam.file, refFile = reference_file, cpgsite = cpgsite) ``` If you are instead interested in reads overlapping a CpG site, you can use `methyl_circle_plotCpG()`, which is useful if you run the tuple-mode: ```{r, fig3} cpgsite <- GRanges(19, IRanges(266998, width = 1)) methyl_circle_plotCpG(cpgsite = cpgsite, bamFile = bam.file, refFile = reference_file) ``` You can also limit both the SNP plot and the CpG plot to a specific window of interest (to zoom in or out), or if you want to look at the specific DAME region: ```{r, fig4} #a random region dame <- GRanges(19, IRanges(266998,267100)) methyl_circle_plot(snp = snp, vcfFile = vcf.file, bamFile = bam.file, refFile = reference_file, dame = dame) ``` ## MDS plot To plot a multidimensional scaling plot (MDS), we provide a wrapper to `plotMDS()` from `limma`, which adjusts the ASM score to calculate the euclidean distances. The input is a SummarizedExperiment, and the vector of covariates to color the points by: ```{r, fig5} grp <- factor(c(rep("CRC",3),rep("NORM",2)), levels = c("NORM", "CRC")) methyl_MDS_plot(ASMscore, group = grp) ``` # Session Info ```{r} utils::sessionInfo() ``` # References