Skip to content
A pipeline for the integration of DNA methylation and hydroxymethylation data
R Shell Other
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs
install
scripts
.gitignore
README.md
VERSIONS.md
init.R
narrowPeak.as
template_config.mk
template_makefile

README.md

mint: Analysis, integration, classification, and annotation of DNA methylation and hydroxymethylation data

v0.4.0

Contents

Overview

The mint pipeline analyzes single-end reads coming from sequencing assays measuring DNA methylation and hydroxymethylation. The pipeline analyzes reads from both bisulfite-converted assays such as WGBS and RRBS, and from pulldown assays such as MeDIP-seq, hMeDIP-seq, and hMeSeal. Moreover, with data measuring both 5-methylcytosine (5mc) and 5-hydroxymethylcytosine (5hmc), the mint pipeline integrates the two data types to classify genomic regions of 5mc, 5hmc, a mixture, or neither.

The mint pipeline is executed with make and includes configurable steps for:

  • Quality control (FastQC and MultiQC)
  • Adapter and quality trimming (trim_galore)
  • Alignment (bismark and bowtie2)
  • Sample-wise quantification (bismark and macs2)
  • Differential methylation detection with multi-factor models with categorical or continuous covariates (DSS and csaw)
  • Classification
    • of samples into regions of no, low, medium, or high methylation
    • of 5mc + 5hmc sample-wise integration into regions of 5mc, 5hmc, a mixture, or neither
    • of group comparisons into regions of hyper/hypo DMR or hyper/hypo DhMR
    • of 5mc + 5hmc group-wise integration into regions of hyper/hypo DMR, hyper/hypo DhMR, a mixture, or neither
  • Genomic annotation and visualization of methylation quantifications and classifications (annotatr)
  • Visualization in the UCSC Genome Browser

The mint pipeline is also implemented for Galaxy, and the repository for setting up the Galaxy version of mint is https://github.com/sartorlab/mint_galaxy.

Top

Installation

mint and Dependencies

The following steps will install mint and its dependencies on a Linux or macOS system. NOTE: Any references to /path/to need to be modified throughout the code below.

  1. cd into the directory you'd like mint installed.
  2. Clone the mint repository with:
git clone https://github.com/sartorlab/mint.git
  1. cd into the mint directory and install the dependencies to mint/apps with:
bash install/install_deps.sh

Some NOTES on the dependencies:

  • A complete list of dependencies with links to corresponding versions can be found in VERSIONS.md.
  • All dependencies are installed in mint/apps and should not interfere with any preexisting installs of the dependencies on the system.
  • Users may already have some dependencies installed. In which case, the install/install_deps.sh script can be modified to suit those situations.
  • The install script checks for a pip installation and installs it with the --user flag if it is not present.
  • Python packages are installed in a virtualenv in mint/apps/mint_env, ensuring existing Python packages remain untouched.
  • The virtualenv activation binary is symlinked in /mint/apps/bin as mint_venv for convenience.
  • The install script expects a version of R >= 3.3.0 to be installed on the system.
  1. Add /path/to/mint/apps/bin to your $PATH variable in ~/.bashrc or ~/.bash_profile (whichever exists), by adding the corrected version of this line:
export PATH=/path/to/mint/apps/bin:~/.local/bin:$PATH

NOTE: Adding the /path/to/mint/apps/bin before $PATH ensures that the versions installed in mint/apps are used rather than previous installations. 5. Source the ~/.bashrc or ~/.bash_profile to update your path in your current terminal session.

source ~/.bashrc
  1. In /path/to/mint do:
mkdir projects

Top

Checking Dependency Installation

At this point using which with one of the dependencies should return a path in mint/apps:

which bowtie2
# Should return /path/to/mint/apps/bin/bowtie2

It is also worthwhile to try loading the virtualenv and testing which on a Python package:

# Activate the virtualenv
# NOTE: Running install/install_deps.sh creates a symlink for mint/apps/mint_env/bin/activate
# as mint/apps/mint_venv
source mint_venv

which multiqc
# Should return /path/to/mint/apps/mint_env/bin/multiqc

Top

Reference Genomes

As with any high-throughput sequencing analysis, the correct reference genome is required to align reads. The most convenient resource for reference genomes is the Illumina iGenomes page.

Top

Symlinks

The following symlinks at the root of the reference genome folder are needed for the config.mk file mint creates for each project.

# Go to the reference genome root
cd /path/to/reference/genome/

ln -s ./Sequence/WholeGenomeFasta/Bisulfite_Genome Bisulfite_Genome
ln -s ./Annotation/Genes/genes.gtf genes.gtf
ln -s ./Sequence/Bowtie2Index/genome.1.bt2 genome.1.bt2
ln -s ./Sequence/Bowtie2Index/genome.2.bt2 genome.2.bt2
ln -s ./Sequence/Bowtie2Index/genome.3.bt2 genome.3.bt2
ln -s ./Sequence/Bowtie2Index/genome.4.bt2 genome.4.bt2
ln -s ./Sequence/Bowtie2Index/genome.fa genome.fa
ln -s ./Sequence/Bowtie2Index/genome.fa.fai genome.fa.fai
ln -s ./Sequence/Bowtie2Index/genome.rev.1.bt2 genome.rev.1.bt2
ln -s ./Sequence/Bowtie2Index/genome.rev.2.bt2 genome.rev.2.bt2

# NOTE: mint requires a chromosome length file for many operations this particular
# line of code is specific to the hg19 reference genome from iGenomes
ln -s ./Annotation/Archives/archive-2013-03-06-11-23-03/Genes/ChromInfo.txt chromInfo_hg19.txt

Top

Bisulfite-converted Reference Genome

If you download a reference genome from iGenomes, it is possible that a bisulfite-converted reference genome is already included. If not, in order to use the bismark aligner on WGBS or RRBS data you must create your own. For more information see the documentation. In short the following will build it:

bismark_genome_preparation [options] <path_to_genome_folder>

Top

Testing mint

After following the installation instructions, we recommend testing the pipeline on a small dataset to ensure everything is working properly. We have created a repository with test data at https://github.com/sartorlab/mint_test based on data from GSE52945. Follow these steps to test the entire pipeline:

  1. Clone the test data repository in a folder outside the mint installation:
git clone https://github.com/sartorlab/mint_test.git
  1. Copy the metadata files for the test data into /path/to/mint/projects:
cp /path/to/mint_test/hybrid/*txt /path/to/mint/projects/
  1. cd to mint:
cd /path/to/mint
  1. Initialize the test_hybrid_small project:
Rscript init.R --project test_hybrid_small --genome hg19 --genomepath /path/to/hg19 --chrompath /path/to/hg19/chromInfo_hg19.txt --datapath /path/to/mint_test/hybrid/
  1. Run the modules. In the simplest form, the following would run in series with all output going to stdout:
# Assumes an interactive server session, and NOT a cluster with queueing

# cd into test_hybrid_small
cd /path/to/mint/projects/test_hybrid_small

# Activate the virtualenv
source mint_venv

make pulldown_align
make bisulfite_align
make pulldown_sample
make bisulfite_sample
make pulldown_compare
make bisulfite_compare
make compare_classification
make sample_classification

To help the tests run faster, you can use the -j flag of make to run operations in parallel, and use & to run operations in the background:

# Assumes an interactive server session where the server has ample resources
# For example, a server with 20 cores and 128GB RAM will be fine

# cd into test_hybrid_small
cd /path/to/mint/projects/test_hybrid_small

# Activate the virtualenv
source mint_venv

# Here we use nohup to write the stdout to a file
# These can be run simultaneously
nohup make -j 4 pulldown_align > nohup_pulldown_align.out &
nohup make -j 4 bisulfite_align > nohup_bisulfite_align.out &

# After the above two are finished these can be run simultaneously
nohup make -j 4 pulldown_sample > nohup_pulldown_sample.out &
nohup make -j 4 bisulfite_sample > nohup_bisulfite_sample.out &

# After the above two are finished these can be run simultaneously
nohup make -j 3 pulldown_compare > nohup_pulldown_compare.out &
nohup make -j 4 bisulfite_compare > nohup_bisulfite_compare.out &

# After the above two are finished, these should be run serially
nohup make -j 3 compare_classification > nohup_compare_classification.out &
nohup make -j 4 sample_classification > nohup_sample_classification.out &

As documented in https://github.com/sartorlab/mint_test, the test data contains reads drawn from the following regions, and have the following resulting features:

  • chr1:2209176-2214175 (hyper_mc_hyper_hmc)
  • chr2:71942840-72005839 (hypo_mc_hyper_hmc)
  • chr7:36074781-36102780 (hyper_hmc)
  • chr8:35090385-35095384 (hyper_mc)
  • chr11:67384075-67389174 (hypo_mc_hypo_hmc)
  • chr16:3046564-3096563 (hyper_mc_hyper_hmc and hyper_mc_hyper_hmc)
  • chr19:39702429-39752428 (hyper_mc_hypo_hmc)

Top

Upgrading mint

To upgrade mint, all you need to do is cd /path/to/mint and do git pull. Existing analyses in the mint/projects/ folder will be unaffected (since this folder isn't tracked by git). To use the upgraded version of mint on existing projects, you will need to reinitialize a project with Rscript init.R ....

If there are changes to any directories that are tracked by git, you might need to git stash your changes, and then git pull.

Details

Supported Experiments and Designs

The mint pipeline supports either:

  • a hybrid setup, with data from bisulfite-conversion experiments representing 5mc + 5hmc methylation (RRBS, WGBS, etc.) and from pulldown experiments representing 5hmc methylation (hMeDIP-seq, hMeSeal, etc.), or
  • a pulldown setup, with data representing 5mc methylation (MeDIP-seq, etc.) and 5hmc methylation (hMeDIP-seq, hMeSeal, etc.).

And the mint pipeline can analyze:

  • sample-wise, where no groups are present for comparison, and 5mc / 5hmc integration is done per-sample, and/or
  • comparison-wise, where groups are tested for differential methylation using a multi-factor design with categorical and/or continuous covariates, and 5mc / 5hmc differential methylation integration is done per-comparison.

Top

Setting up a project

Taking the example in Testing mint as a guide, setting up a project requires the following:

  1. A samples file describing the samples and any covariates associated with them. Put this in /path/to/mint/projects.
  2. A comparisons file describing the comparisons to be made and models and covariates to use. Put this in /path/to/mint/projects.
  3. The location of the raw sequencing reads corresponding to the samples, and the relevant reference genome.

Top

Samples file

The sample file is a tab-delimited text file named [projectID]_samples.txt in /path/to/mint/projects, and contains the following 9+ columns (with headers):

  1. projectID: The name of the project.
  2. sampleID: Often an alphanumeric ID (perhaps from SRA, GEO, a sequencing core, etc.). These will be the names of the .fastq.gz files containing the raw reads.
  3. humanID: An ID that makes the sampleIDs easier to understand. For example, instead of a sampleID from the SRA like 'SRA876549', use 'IDH2mut_1' to indicate what kind of mutant and what replicate the sample is. NOTE: The humanID may not be unique if a particular condition has a corresponding input sample (e.g., pulldown) or a corresponding sample on a different platform (e.g., WGBS). See examples below.
  4. pulldown: A binary indicating whether the sample is the result of a pulldown experiment (1) or not (0).
  5. bisulfite: A binary indicating whether the sample is the result of a bisulfite-conversion experiment (1) or not (0).
  6. mc: A binary indicating whether the sample represents 5mc methylation (1) or not (0). If a sample was run on WGBS, this column and the hmc column would both be 1.
  7. hmc: A binary indicating whether the sample represents 5hmc methylation (1) or not (0). If a sample was run on WGBS, this column and the mc column would both be 1.
  8. input: A binary indicating whether the sample represents an input (1) or not (0).
  9. group: A comma-separated string denoting the group numbers the sample belongs to for comparisons.
  10. Any additional columns are categorical or continuous covariates, and their column headers should match what is used in the models in [projectID]_comparisons.txt.

In particular, for the test data the annotation file looks like:

projectID	sampleID	humanID	pulldown	bisulfite	mc	hmc	input	group	subject	age
test_hybrid_small	IDH2mut_1_hmeseal	IDH2mut_1	1	0	0	1	0	1	1	3
test_hybrid_small	IDH2mut_2_hmeseal	IDH2mut_2	1	0	0	1	0	1	2	6
test_hybrid_small	IDH2mut_1_hmeseal_input	IDH2mut_1	1	0	0	1	1	1	1	3
test_hybrid_small	IDH2mut_2_hmeseal_input	IDH2mut_2	1	0	0	1	1	1	2	6
test_hybrid_small	IDH2mut_1_errbs	IDH2mut_1	0	1	1	1	0	1	1	3
test_hybrid_small	IDH2mut_2_errbs	IDH2mut_2	0	1	1	1	0	1	2	6
test_hybrid_small	NBM_1_hmeseal	NBM_1	1	0	0	1	0	0	1	10
test_hybrid_small	NBM_2_hmeseal	NBM_2	1	0	0	1	0	0	2	15
test_hybrid_small	NBM_1_hmeseal_input	NBM_1	1	0	0	1	1	0	1	10
test_hybrid_small	NBM_2_hmeseal_input	NBM_2	1	0	0	1	1	0	2	15
test_hybrid_small	NBM_1_errbs	NBM_1	0	1	1	1	0	0	1	10
test_hybrid_small	NBM_2_errbs	NBM_2	0	1	1	1	0	0	2	15

Top

Comparisons file

The comparisons file is a tab-delimeted file named [projectID]_comparisons.txt in /path/to/mint/projects, and contains 13 columns:

  1. projectID: The name of the project.
  2. comparison: The prefix for the output files.
  3. pulldown: A binary indicating whether the comparison is for a pulldown experiment (1) or not (0).
  4. bisulfite: A binary indicating whether the comparison is for a bisulfite-conversion experiment (1) or not (0).
  5. mc: A binary indicating whether the comparison is for 5mc methylation (1) or not (0). If for WGBS, this column and the hmc column would both be 1.
  6. hmc: A binary indicating whether the comparison is for 5hmc methylation (1) or not (0). If for WGBS, this column and the mc column would both be 1.
  7. input: A logical indicating whether to use the input data in the test for differential methylation. This only applies to csaw.
  8. model: A string as one would pass to formula(). Note, group should be in the model, and any covariates should have matching column headings in [projectID]_samples.txt.
  9. contrast: A comma-separated binary string denoting which coefficient in the model to test.
  10. covariates: A comma-separated string indicating the names of the covariates to use.
  11. covIsNumeric: A comma-separated binary string denoting which covariates are numeric (1) and categorical (0).
  12. groups: A comma-separated string denoting which groups are present in this comparison. This is used to get the correct samples from [projectID_samples.txt].
  13. interpretation: A comma-separated string with descriptions for what it means for a region to have logFC or methdiff less than 0 (first entry) and greater than 0 (second entry).

In particular, for the test data the comparisons file looks like:

projectID	comparison	pulldown	bisulfite	mc	hmc	input	model	contrast	covariates	covIsNumeric	groups	interpretation
test_hybrid_small	IDH2mut_v_NBM	1	0	0	1	TRUE	~1+group	"0,1"	NA	0	"0,1"	"NBM,IDH2mut"
test_hybrid_small	IDH2mut_v_NBM	0	1	1	1	FALSE	~1+group	"0,1"	NA	0	"0,1"	"NBM,IDH2mut"
test_hybrid_small	IDH2mut_v_NBM_paired	1	0	0	1	TRUE	~1+group+subject	"0,1,0"	subject	0	"0,1"	"NBM,IDH2mut"
test_hybrid_small	IDH2mut_v_NBM_paired	0	1	1	1	FALSE	~1+group+subject	"0,1,0"	subject	0	"0,1"	"NBM,IDH2mut"

Top

Instantiating a project

After creating an appropriate annotation file for your project, within the mint/ folder do the following:

  1. mkdir projects
  2. Put the test_hybrid_small_annotation.txt file in mint/projects/.
  3. In mint/ do:
Rscript init.R --project projectID --genome genome_build --genomepath /path/to/genome --chrompath /path/to/genome/chromInfo_file.txt --datapath /path/to/data/

The init.R script creates an appropriate directory structure in mint/projects/projectID/, creates symlinks to the .fastq.gz files in /path/to/data, and creates the makefile and config.mk files that control the analysis of your project.

NOTE: The genome should use the UCSC notation of hg19, hg38, mm9, or mm10 in order to take advantage of genomic annotations. The --genome parameter should be the same genome as the --genomepath in spirit, if not in name. e.g. --genome hg38 and --genomepath /path/to/GRCh38.

Top

Configuring a project

The mint/projects/projectID/config.mk file contains options for analysis implied in the project annotation file. As appropriate, links to documentation for each software tool is provided at the corresponding section fo the config.mk file.

NOTE: Default parameters may not be correct for your project, so check them carefully!

Top

Running a project

Once a project is instantiated and configured, the analysis steps can begin. The mint pipeline is controlled by make and there are up to 8 simple commands to run the modules, depending on the analyses implied by the project annotation file. In general, the bisulfite and pulldown commands are independent of each other until classification, and the *_sample and *_compare commands rely on the corresponding *_align steps.

For example, the following commands will analyze the test hybrid data:

# Go to the project root
cd /path/to/mint/projects/test_hybrid_small

# Activate the python virtualenv
source mint_venv

# Run modules
make pulldown_align
make bisulfite_align
make pulldown_sample
make bisulfite_sample
make bisulfite_compare
make pulldown_compare
make sample_classification
make compare_classification

To see what will be run by the pipeline without actually running anything, you can make -n pulldown_align, etc. for each of the commands. NOTE: This is also a way to check that a module completed without problem. If you run make -n pulldown_align after the module finishes, make will report that nothing is to be done for that rule.

Depending on the computing hardware used, projects can be run with the make -j n command where n is a positive integer. The -j flag specifies how many commands make is allowed to run simultaneously. When it is not present, the default is to run commands in serial.

NOTE: Some software in the mint pipeline have options for the number of processors to use, so some care should be taken not to exceed the computing limitations of the hardware. Tools that have parameters for the user of multiple processors are: bismark_methylation_extractor. By default, the number of processors to use is set to 1.

Top

Outputs

In the case of the test_hybrid_small project the following directory structure is created by Rscript init.R and the outputs of the make commands above are organized within:

test_hybrid_small
test_hybrid_small/tmp
test_hybrid_small/data
test_hybrid_small/data/raw_fastqs
test_hybrid_small/data/test_hybrid_small_annotation.txt
test_hybrid_small/summary
test_hybrid_small/summary/figures
test_hybrid_small/summary/tables
test_hybrid_small/summary/reports
test_hybrid_small/test_hybrid_small_hub
test_hybrid_small/test_hybrid_small_hub/hg19
test_hybrid_small/test_hybrid_small_hub/genomes.txt
test_hybrid_small/test_hybrid_small_hub/hub.txt
test_hybrid_small/test_hybrid_small_hub/test_hybrid_small_hub.html
test_hybrid_small/classifications
test_hybrid_small/classifications/simple
test_hybrid_small/classifications/sample
test_hybrid_small/classifications/comparison
test_hybrid_small/RData
test_hybrid_small/bisulfite
test_hybrid_small/bisulfite/raw_fastqs
test_hybrid_small/bisulfite/raw_fastqcs
test_hybrid_small/bisulfite/trim_fastqs
test_hybrid_small/bisulfite/trim_fastqcs
test_hybrid_small/bisulfite/bismark
test_hybrid_small/bisulfite/dss
test_hybrid_small/pulldown
test_hybrid_small/pulldown/raw_fastqs
test_hybrid_small/pulldown/raw_fastqcs
test_hybrid_small/pulldown/trim_fastqs
test_hybrid_small/pulldown/trim_fastqcs
test_hybrid_small/pulldown/bowtie2_bams
test_hybrid_small/pulldown/pulldown_coverages
test_hybrid_small/pulldown/macs2_peaks
test_hybrid_small/pulldown/csaw
test_hybrid_small/makefile
test_hybrid_small/config.mk
test_hybrid_small/narrowPeak.as

Top

FastQC

The FastQC output is by default not extracted, so the .zip files and .html files for the raw reads are located in test_hybrid_small/bisulfite/raw_fastqcs and test_hybrid_small/pulldown/raw_fastqcs. The output FastQC done after trimming are located in test_hybrid_small/bisulfite/trim_fastqcs and test_hybrid_small/pulldown/trim_fastqcs.

Trim Galore

The adapter and quality trimmed raw reads, as well as trimming reports, are output in test_hybrid_small/bisulfite/trim_fastqs and test_hybrid_small/pulldown/trim_fastqs. The trimming reports are the normal output of trim_galore.

Bisulfite

bismark

The results of the bismark alignment and methylation quantification from bismark_methylation_extractor go in test_hybrid_small/bisulfite/bismark. These include sorted and indexed alignment .bams, methylation rate bedGraphs, coverage files, CpG reports, M-bias plots, and the splitting reports.

DSS

The results of the tests for differential methylation with DSS go in test_hybrid_small/bisulfite/dss.

Pulldown

bowtie2

The results of bowtie2 alignments go in test_hybrid_small/pulldown/bowtie2_bams. All alignments are sorted and indexed after they are aligned, and the mapping efficiencies are output to a text file in the same folder.

Genome coverage

The read pileups using bedtools genomecov go in test_hybrid_small/pulldown/pulldown_coverages. This includes both pulldowns with an antibody and input pulldowns. The corresponding coverage .bedGraph files are compressed into .bigWigs and placed in test_hybrid_small/test_hybrid_small_hub/hg19. For downstream use, a 'merged' coverage .bed file is created that fills coverage gaps of up to 20bp. These files are used to determine where no signal is present for classifications.

macs2

The .narrowPeak files resulting from macs2 peak calling go in test_hybrid_small/pulldown/macs2_peaks. Additionally, the .pdf images of the model used for peak calling are in the same folder.

csaw

The *_csaw_significant.txt file resulting from csaw's test for differentially methylated regions goes in test_hybrid_small/pulldown/csaw.

Top

Classifications

Simple classification

Simple classifications are done on the results of bismark_methylation_extractor and macs2 in order to classify sites/regions in each sample as having no, low, medium, or high methylation. In the case of bismark_methylation_extractor we have absolute quantification of methylation levels and the breakdown classification is:

  • None: [0%, 5%)
  • Low: [5%, 33%)
  • Medium: [33%, 66%)
  • High : [66%, 100%]

Since macs2 peaks determines qualitative methylation, we determine if a peak represents low, medium, or high methylation by dividing the fold-changes reported by macs2 into tertiles based on the range of the fold-changes: The minimum observed fold-change and the minimum observed fold-change among the top 1%-tile of fold changes. Regions with no peaks are considered to have no methylation.

The resulting simple classifications go in test_hybrid_small/classifications/simple as .bed files which include colors for visualization in the UCSC Genome Browser. The .bed files are then compressed into bigBed and are located in test_hybrid_small/test_hybrid_small_hub/hg19.

chr2	39812689	39812804	hmc_low	1000	.	39812689	39812804	102,102,255
chr21	15197751	15198018	hmc_low	1000	.	15197751	15198018	102,102,255
chr21	15198928	15199092	hmc_low	1000	.	15198928	15199092	102,102,255
chr21	15200122	15200346	hmc_low	1000	.	15200122	15200346	102,102,255
chr21	15353077	15353463	hmc_med	1000	.	15353077	15353463	0,0,255

Top

Sample classification

Sample classifications are done when data is available measuring 5mc + 5hmc (WGBS, RRBS, etc.) and 5hmc (hMeDIP-seq, hMeSeal, etc.), or 5mc (MeDIP-seq, etc.) and 5hmc (hMeDIP-seq, etc.). The inputs into the classifier are sites/regions of 5mc + 5hmc and regions of 5hmc or regions of 5mc and regions of 5hmc. The tables below determine how the data is integrated into classifications.

Hybrid sample classification:

hmc peak No hmc peak No signal
High mc+hmc hmc mc hmc or mc
Low mc+hmc hmc mc (low) hmc or mc (low)
No mc+hmc hmc No methylation No methylation
No signal hmc No methylation Unclassifiable

Pulldown sample classification:

hmc peak No hmc peak No signal
mc peak hmc and mc mc mc
No mc peak hmc No methylation No methylation
No signal hmc No methylation Unclassifiable

The sample classifications go in test_hybrid_small/classifications/sample as .bed files which include colors for visualization in the UCSC Genome Browser. The .bed files are then compressed into bigBed and are located in test_hybrid_small/test_hybrid_small_hub/hg19.

chr21   9826885 9826886 mc      1000    .       9826885 9826886 255,0,0
chr21   9826886 9826887 mc_low  1000    .       9826886 9826887 255,165,0
chr21   9826887 9826888 mc      1000    .       9826887 9826888 255,0,0
chr21   9826891 9826892 no_meth 1000    .       9826891 9826892 0,0,0
chr21   9826892 9826893 mc_low  1000    .       9826892 9826893 255,165,0
chr21   9826895 9826896 no_meth 1000    .       9826895 9826896 0,0,0
chr21   9826896 9826897 mc_low  1000    .       9826896 9826897 255,165,0
chr21   9826899 9826901 mc_low  1000    .       9826899 9826901 255,165,0
chr21   9826903 9826905 mc_low  1000    .       9826903 9826905 255,165,0

Top

Comparison classification

Comparison classifications are done when data is available measuring 5mc + 5hmc (WGBS, RRBS, etc.) and 5hmc (hMeDIP-seq, hMeSeal, etc.), or 5mc (MeDIP-seq, etc.) and 5hmc (hMeDIP-seq, etc.), and there are groups of samples compared against each other for differential methylation (DM). The inputs into the classifier are sites/regions of 5mc + 5hmc DM and regions of 5hmc DM or regions of 5mc DM and 5hmc DM. The tables below determine how the data is integrated into classifications.

Hybrid comparison classification:

Hyper hmc Hypo hmc No DM No signal
Hyper mc + hmc Hyper mc & Hyper hmc Hyper mc & Hypo hmc Hyper mc Hyper mc
Hypo mc + hmc Hypo mc & Hyper hmc Hypo mc & Hypo hmc Hypo mc Hypo mc
No DM Hyper hmc Hypo hmc No DM No DM
No signal Hyper hmc Hypo hmc No DM Unclassifiable

Pulldown comparison classification:

Hyper hmc Hypo hmc No DM No signal
Hyper mc Hyper mc & Hyper hmc Hyper mc & Hypo hmc Hyper mc Hyper mc
Hypo mc Hypo mc & Hyper hmc Hypo mc & Hypo hmc Hypo mc Hypo mc
No DM Hyper hmc Hypo hmc No DM No DM
No signal Hyper hmc Hypo hmc No DM Unclassifiable

The comparison classifications go in test_hybrid_small/classifications/comparison as .bed files which include colors for visualization in the UCSC Genome Browser. The .bed files are then compressed into bigBed and are located in test_hybrid_small/test_hybrid_small_hub/hg19.

chr21   16423450        16423650        hypo_hmc        1000    .       16423450        16423650        102,102,255
chr21   16423650        16424400        no_DM   1000    .       16423650        16424400        0,0,0
chr21   16424400        16424650        hyper_hmc       1000    .       16424400        16424650        0,0,255
chr21   16424650        16427750        no_DM   1000    .       16424650        16427750        0,0,0
chr21   16427750        16427900        hypo_hmc        1000    .       16427750        16427900        102,102,255
chr21   16427900        16429700        no_DM   1000    .       16427900        16429700        0,0,0
chr21   16429700        16429850        hyper_hmc       1000    .       16429700        16429850        0,0,255

Top

Annotations and Visualizations

The genomic regions given by bismark_methylation_extractor, DSS, csaw, and the classifications are annotated to genomic annotations using annotatr, a fast and flexible R package designed for exactly this task. As with DSS, every annotatr session is saved as an .RData file in test_hybrid_small/RData to enable users to quickly go back to the annotations, investigate further, alter plots, or create new plots.

Only the hg19, hg38, mm9, or mm10 genomes are currently supported for annotation in the mint pipeline. We plan to implement the use of custom annotations within the pipeline in the future (annotatr already supports custom annotations). All files are annotated against CpG features (islands, shores, shelves, and inter CGI) and genic features based on UCSC knownGene transcripts (1-5kb upstream of promoter, promoter (<1kb upstream of TSS), 5'UTR, exons, introns, and 3'UTR). In the case of hg19, the FANTOM5 robust enhancers are also included. Annotations include UCSC transcript IDs, Entrez Gene IDs, and gene symbols.

All annotation sessions output a table of all genomic annotations intersecting the input regions in test_hybrid_small/summary/tables. Also output are summary tables indicating the number of regions annotated to a particular type, along with corresponding numbers for a set of random regions.

For details on the general features of annotatr, visit the GitHub repository.

Top

Table: Complete annotations

annot_chrom	annot_start	annot_end	annot_strand	annot_type	annot_id	entrez_id	symbol	data_chrom	data_start	data_end	data_strand	name
chr21	9909278	9966321	-	hg19_knownGenes_introns	uc002zka.2,uc021wgx.1	100132288	TEKT4P2	chr21	9909661	9909789	*	hmc_low
chr21	9909515	9909759	*	hg19_cpg_islands	island:17089	NA	NA	chr21	9909661	9909789	*	hmc_low
chr21	9909759	9912040	*	hg19_cpg_shores	shore:30779	NA	NA	chr21	9909661	9909789	*	hmc_low
chr21	9909278	9966321	-	hg19_knownGenes_introns	uc002zka.2,uc021wgx.1	100132288	TEKT4P2	chr21	9944159	9944343	*	hmc_low

Top

Table: Annotation summaries

data_type       annot_type      n
Data    hg19_cpg_inter  2977
Data    hg19_cpg_islands        134
Data    hg19_cpg_shelves        327
Data    hg19_cpg_shores 519
Data    hg19_enhancers_fantom   294
Data    hg19_knownGenes_1to5kb  399
Data    hg19_knownGenes_3UTRs   131
Data    hg19_knownGenes_5UTRs   1533
Data    hg19_knownGenes_exons   408
Data    hg19_knownGenes_intergenic      1528
Data    hg19_knownGenes_introns 3562
Data    hg19_knownGenes_promoters       158
Random Regions  hg19_cpg_inter  3420
Random Regions  hg19_cpg_islands        48
Random Regions  hg19_cpg_shelves        157
Random Regions  hg19_cpg_shores 177
Random Regions  hg19_enhancers_fantom   39
Random Regions  hg19_knownGenes_1to5kb  208
Random Regions  hg19_knownGenes_3UTRs   60
Random Regions  hg19_knownGenes_5UTRs   652
Random Regions  hg19_knownGenes_exons   169
Random Regions  hg19_knownGenes_intergenic      2394
Random Regions  hg19_knownGenes_introns 1938
Random Regions  hg19_knownGenes_promoters       66

Additionally, a variety of plots are output to help interpret the output of bismark_methylation_extractor, DSS, csaw, and the classifications.

Top

Plot: Number of regions per annotation

Regions per annotation type

Top

Plot: Distribution of % methylation in annotations

Distribution of perc. meth. in annotations

Top

Plot: Distribution of coverage in annotations

Distribution of coverage in annotations

Top

Plot: Distribution of peak widths

Distribution of peak widths

Top

Plot: Volcano plots of DM

Volcano plots from DSS results

Top

Plot: Distribution of DSS calls in annotations

Distribution of DSS calls in annots

Top

Plot: Distribution of chip1/chip2 peaks in annotations

Counts of DM type in annots

Prop. of DM type in annots

Top

Plot: Distribution of classifications in annotations

Simple classification pulldown

Simple classification bisulfite

Sample classification

Comparison classification

Top

UCSC Browser track hub

Each mint project has a UCSC Genome Browser track hub autogenerated in test_hybrid_small/test_hybrid_small_hub. In order to view it on the genome browser, it must be placed in a web-facing folder and the URL should be given under the My Hubs tab.

Included in each track hub, where applicable, are:

  • Pulldown coverage pileups from bedtools genomecov
  • Percent methylation tracks from bismark_methylation_extractor
  • Simple classification tracks for bismark_methylation_extractor, macs2, and csaw
  • Differential methylation from csaw with group labels
  • Differential methylation from DSS
  • Sample classifications
  • Comparison classifications

Top

You can’t perform that action at this time.