methylpy

Welcome to the home page of methylpy, a pyhton-based analysis pipeline for

• (single-cell) (whole-genome) bisulfite sequencing data
• (single-cell) NOMe-seq data
• differential methylation analysis

methylpy is available at github and PyPI.

Note

• Version 1.3 has major changes on options related to mapping. A new aligner, minimap2, is supported starting in this version. To accommodate this new features, --bowtie2 option is replaced with --aligner, which specifies the aligner to use. The parameters of --build-reference function are modified as well.
• methylpy only considers cytosines that are in uppercase in the genome fasta file (i.e. not masked)
• methylpy was initiated by and built on the work of Mattew D. Schultz
• beta version of tutorial is released!

What can methylpy do?

Processing bisulfite sequencing data and NOMe-seq data

• fast and flexible pipeline for both single-end and paired-end data
• all the way from raw reads (fastq) to methylation state and/or open chromatin readouts
• also support getting readouts from alignment (BAM file)
• including options for read trimming, quality filter and PCR duplicate removal
• accept compressed input and generate compressed output
• support post-bisulfite adaptor tagging (PBAT) data

Calling differentially methylated regions (DMRs)

• DMR calling at single cytosine level
• support comparison across 2 or more samples/groups
• conservative and accurate
• useful feature for dealing with low-coverage data by combining data of adjacent cytosines

What you want to do

• Use methylpy without installation
• Install methylpy
• Test methylpy
• Process data
• Call DMRs
• Additional functions for data processing
• Cite methylpy

run methylpy -h to get a list of functions.

Use methylpy without installation

Methylpy can be used within docker container with all dependencies resolved. The docker image for methylpy can be built from the Dockerfile under methylpy/ directory using the below command. It will take ~3g space.

git clone https://github.com/yupenghe/methylpy.git
cd methylpy/
docker build -t methylpy:latest ./

Then, you can start a docker container by running

docker run -it methylpy:latest

methylpy can be run with full functionality within the container. You can mount your working directory to the container by adding -v option to the docker command and store methylpy output there.

docker run -it -v /YOUR/WORKING/PATH/:/output methylpy:latest

See here for details.

Install methylpy

Step 1 - Download methylpy

The easiest way of installing methylpy will be through PyPI by running pip install methylpy. The command pip install --upgrade methylpy updates methylpy to latest version.

Methylpy can also be installed through anaconda or [miniconda] (https://docs.conda.io/en/latest/miniconda.html).

conda env create --name methylpy_env
conda activate methylpy_env
conda install -y -c bioconda -c conda-forge methylpy              

Alternatively, methylpy can be installed through github: enter the directory where you would like to install methylpy and run

git clone https://github.com/yupenghe/methylpy.git
cd methylpy/
python setup.py install

If you would like to install methylpy in path of your choice, run python setup.py install --prefix=/USER/PATH/. Then, try methylpy and if no error pops out, the setup is likely successful. See Test methylpy for more rigorious test. Last, processing large dataset will require large spare space for temporary files. Usually, the default directory for temporary files will not meet the need. You may want to set the TMPDIR environmental variable to the (absolute) path of a directory on hard drive with sufficient space (e.g. /YOUR/TMP/DIR/). This can be done by adding the below command to ~/.bashrc file: export TMPDIR=/YOUR/TMP/DIR/ and run source ~/.bashrc.

Step 2 - Install dependencies

python is required for running methylpy. Both python2 (>=2.7.9) and python3 (>=3.6.2) will work. methylpy also depends on two python modules, numpy and scipy. The easiest way to get these dependencies is to install anaconda.

In addition, some features of methylpy depend on several publicly available tools (not all of them are required if you only use a subset of methylpy functions).

• cutadapt (>=1.9) for raw read trimming
• bowtie and/or bowtie2 for alignment
• samtools (>=1.3) for alignment result manipulation. Samtools can also be installed using conda conda install -c bioconda samtools
• Picard (>=2.10.8) for PCR duplicate removal
• java for running Picard (its path needs to be included in PATH environment variable) .
• wigToBigWig for converting methylpy output to bigwig format

Lastly, if paths to cutadapt, bowtie/bowtie2, samtools and wigToBigWig are included in PATH variable, methylpy can run these tools directly. Otherwise, the paths have to be passed to methylpy as augments. Path to Picard needs to be passed to methylpy as a parameter to run PCR duplicate removal.

Optional step - Compile rms.cpp

DMR finding requires an executable methylpy/methylpy/run_rms_tests.out, which was compiled from C++ code methylpy/methylpy/rms.cpp. In most cases, the precompiled file can be used directly. To test this, simply run execute methylpy/methylpy/run_rms_tests.out. If help page shows, recompiling is not required. If error turns up, the executable needs to be regenerated by compiling rms.cpp and this step requires GSL installed correctly. In most linux operating system, the below commands will do the job

cd methylpy/methylpy/
g++ -O3 -l gsl -l gslcblas -o run_rms_tests.out rms.cpp

In Ubuntu (>=16.04), please try the below commands first.

cd methylpy/methylpy/
g++ -o run_rms_tests.out rms.cpp `gsl-config --cflags --libs`

Lastly, the compiled file run_rms_tests.out needs to be copied to the directory where methylpy is installed. You can get the directory by running the blow commands in python console (python to open a python console):

import methylpy
print(methylpy.__file__[:methylpy.__file__.rfind("/")]+"/")

Test methylpy

To test whether methylpy and the dependencies are installed and set up correctly, run

wget http://neomorph.salk.edu/yupeng/share/methylpy_test.tar.gz
tar -xf methylpy_test.tar.gz
cd methylpy_test/
python run_test.py

The test should take around 3 minutes, and progress will be printed on screen. After the test is started, two files test_output_msg.txt and test_error_msg.txt will be generated. The former contains more details about each test and the later stores error message (if any) as well as additional information.

If test fails, please check test_error_msg.txt for the error message. If you decide to submit an issue regarding test failure to methylpy github page, please include the error message in this file.

Process data

Please see tutorial. for more details.

Step 1 - Build converted genome reference

Build bowtie/bowtie2 index for converted genome. Run methylpy build-reference -h to get more information. An example of building mm10 mouse reference index:

methylpy build-reference \
	--input-files mm10_bt2/mm10.fa \
	--output-prefix mm10_bt2/mm10 \
	--bowtie2 True

Step 2 - Process bisulfite sequencing and NOMe-seq data

Function single-end-pipeline is For processing single-end data. Run methylpy single-end-pipeline -h to get help information. Below code is an example of using methylpy to process single-end bisulfite sequencing data. For processing NOMe-seq data, please use num_upstr_bases=1 to include one base upstream cytosine as part of cytosine sequence context, which can be used to tease out GC sites.

methylpy single-end-pipeline \
	--read-files raw/mESC_R1.fastq.gz \
	--sample mESC \
	--forward-ref mm10_bt2/mm10_f \
	--reverse-ref mm10_bt2/mm10_r \
	--ref-fasta mm10_bt2/mm10.fa \
	--num-procs 8 \
	--remove-clonal True \
	--path-to-picard="picard/"

An command example for processing paired-end data. Run methylpy paired-end-pipeline -h to get more information.

methylpy paired-end-pipeline \
	--read1-files raw/mESC_R1.fastq.gz \
	--read2-files raw/mESC_R2.fastq.gz \
	--sample mESC \
	--forward-ref mm10_bt2/mm10_f \
	--reverse-ref mm10_bt2/mm10_r \
	--ref-fasta mm10_bt2/mm10.fa \
	--num-procs 8 \
	--remove-clonal True \
	--path-to-picard="picard/"

If you would like methylpy to perform binomial test for teasing out sites that show methylation above noise level (which is mainly due to sodium bisulfite non-conversion), please check options --binom-test and --unmethylated-control.

Output format

Output file(s) are (compressed) tab-separated text file(s) in allc format. "allc" stands for all cytosine (C). Each row in an allc file corresponds to one cytosine in the genome. An allc file contain 7 mandatory columns and no header. Two additional columns may be added with --add-snp-info option when using single-end-pipeline, paired-end-pipeline or call-methylation-state methods.

index	column name	example	note
1	chromosome	12	with no "chr"
2	position	18283342	1-based
3	strand	+	either + or -
4	sequence context	CGT	can be more than 3 bases
5	mc	18	count of reads supporting methylation
6	cov	21	read coverage
7	methylated	1	indicator of significant methylation (1 if no test is performed)
8	(optional) num_matches	3,2,3	number of match basecalls at context nucleotides
9	(optional) num_mismatches	0,1,0	number of mismatches at context nucleotides

Call DMRs

This function will take a list of compressed/uncompressed allc files (output files from methylpy pipeline) as input and look for DMRs. Help information of this function is available via running methylpy DMRfind -h.

Below is the code of an example of calling DMRs for CG methylation between two samples, AD_HT and AD_IT on chromosome 1 through 5 using 8 processors.

methylpy DMRfind \
	--allc-files allc/allc_AD_HT.tsv.gz allc/allc_AD_IT.tsv.gz \
	--samples AD_HT AD_IT \
	--mc-type "CGN" \
	--chroms 1 2 3 4 5 \
	--num-procs 8 \
	--output-prefix DMR_HT_IT

Please see tutorial for details.

Additional functions for data processing

Extract cytosine methylation state from BAM file

The call-methylation-state function allows users to get cytosine methylation state (allc file) from alignment file (BAM file). It is part of the data processing pipeline which is especially useful for getting the allc file from alignment file from other methylation data pipelines like bismark. Run methylpy call-methylation-state -h to get help information. Below is an example of running this function. Please make sure to remove --paired-end True or use --paired-end False for BAM file from single-end data.

methylpy call-methylation-state \
	--input-file mESC_processed_reads_no_clonal.bam \
	--paired-end True \
	--sample mESC \
	--ref-fasta mm10_bt2/mm10.fa \
	--num-procs 8

Get methylation level for genomic regions

Calculating methylation level of certain genomic regions can give an estimate of the methylation abundance of these loci. This can be achieved using the add-methylation-level function. See methylpy add-methylation-level -h for more details about the input format and available options.

methylpy add-methylation-level \
	--input-tsv-file DMR_AD_IT.tsv \
	--output-file DMR_AD_IT_with_level.tsv \
	--allc-files allc/allc_AD_HT_1.tsv.gz allc/allc_AD_HT_2.tsv.gz \
		allc/allc_AD_IT_1.tsv.gz allc/allc_AD_IT_2.tsv.gz \
	--samples AD_HT_1 AD_HT_2 AD_IT_1 AD_IT_2 \
	--mc-type CGN \
	--num-procs 4

Merge allc files

The merge-allc function can merge multiple allc files into a single allc file. It is useful when separate allc files are generated for replicates of a tissue or cell type, and one wants to get a single allc file for that tissue/cell type. See methylpy merge-allc -h for more information.

methylpy merge-allc \
	--allc-files allc/allc_AD_HT_1.tsv.gz allc/allc_AD_HT_2.tsv.gz \
	--output-file allc/allc_AD_HT.tsv.gz \
	--num-procs 1 \
	--compress-output True

Filter allc files

The filter-allc function is for filtering sites by cytosine context, coverage etc. See methylpy filter-allc -h for more information.

methylpy filter-allc \
	--allc-file allc/allc_AD_HT_1.tsv.gz \
	--output-file allc/allCG_AD_HT_1.tsv.gz \
	--mc-type CGN \
	--min-cov 2 \
	--compress-output True

Index allc files

The index-allc function allows creating index file for each allc file. The index file can be used for speeding up allc file reading similar to the .fai file for .fasta file. See methylpy index-allc -h for more information.

methylpy index-allc \
	--allc-files allc/allc_AD_HT_1.tsv.gz allc/allc_AD_HT_2.tsv.gz \
	--num-procs 2 \
	--no-reindex False

Convert allc file to bigwig format

The allc-to-bigwig function generates bigwig file from allc file. Methylation level will be calculated in equally divided non-overlapping genomic bins and the output will be stored in a bigwig file. See methylpy allc-to-bigwig -h for more information.

methylpy allc-to-bigwig \
	--allc-file results/allc_mESC.tsv.gz \
	--output-file results/allc_mESC.bw \
	--ref-fasta mm10_bt2/mm10.fa \
	--mc-type CGN \
	--bin-size 100 	

Quality filter for bisulfite sequencing reads

Sometimes, we want to filter out reads that cannot be mapped confidently or are likely from under-converted DNA fragments. This can be done using the bam-quality-filter function. See methylpy bam-quality-filter -h for parameter inforamtion.

For example, below command can be used to filter out reads with less than 30 MAPQ score (poor alignment) and with mCH level greater than 0.7 (under-conversion) if the reads contain enough (at least 3) CH sites.

methylpy bam-quality-filter \
	--input-file mESC_processed_reads_no_clonal.bam \
	--output-file mESC_processed_reads_no_clonal.filtered.bam \
	--ref-fasta mm10_bt2/mm10.fa \
	--min-mapq 30 \
	--min-num-ch 3 \
	--max-mch-level 0.7 \
	--buffer-line-number 100

Reidentify DMRs from existing result

methylpy is able to reidentify-DMR based on the result of previous DMRfind run. This function is especially useful in picking out DMRs across a subset of categories and/or with different filters. See methylpy reidentify-DMR -h for details about the options.

methylpy reidentify-DMR \
	--input-rms-file results/DMR_P0_FBvsHT_rms_results.tsv.gz \
	--output-file results/DMR_P0_FBvsHT_rms_results_recollapsed.tsv \
	--collapse-samples P0_FB_1 P0_FB_2 P0_HT_1 P0_HT_2 \
	--sample-category P0_FB P0_FB P0_HT P0_HT \
	--min-cluster 2

Cite methylpy

If you use methylpy, please cite

Matthew D. Schultz, Yupeng He, John W.Whitaker, Manoj Hariharan, Eran A. Mukamel, Danny Leung, Nisha Rajagopal, Joseph R. Nery, Mark A. Urich, Huaming Chen, Shin Lin, Yiing Lin, Bing Ren, Terrence J. Sejnowski, Wei Wang, Joseph R. Ecker. Human Body Epigenome Maps Reveal Noncanonical DNA Methylation Variation. Nature. 523(7559):212-216, 2015 Jul.