nanomethphase 0.2

Phase long reads from Oxford Nanopore Technologies based on their methylated profile.

NanoMethPhase

Phase long reads and CpG methylations from Oxford Nanopore Technologies.

Installation

Using pypi repository

pip install nanomethphase

NOTE: NanoMethPhase needs python >= 3.7

Using Docker image

It ships with complementary softwares SNVoter, Nanopolish, Clair, WhatsHap & Tabix. The container does not natively support interactive usage, please refer to the workaround below.

docker pull jmgarant/nanomethphase

# usage example:
docker run -t jmgarant/nanomethphase nanomethphase
docker run -t jmgarant/nanomethphase snvoter
docker run -t jmgarant/nanomethphase nanopolish
docker run -t jmgarant/nanomethphase clair
docker run -t jmgarant/nanomethphase whatshap
docker run -t jmgarant/nanomethphase tabix

# workaround for interactive use
docker run -it jmgarant/nanomethphase bash -il

From source

git clone https://github.com/vahidAK/NanoMethPhase.git
cd NanoMethPhase
./nanomethphase.py

Creating a dedicated conda environment

Environment file available in the git repository

git clone https://github.com/vahidAK/NanoMethPhase.git
conda env create -f NanoMethPhase/envs/environment.yaml

Quickstart

If you have your methylation call data and phased vcf file you can get the haplotype methylome via:

1- Processing and indexing methylation call file

nanomethphase methyl_call_processor -mc MethylationCall.tsv -t 20 | sort -k1,1 -k2,2n -k3,3n | bgzip > MethylationCall.bed.gz && tabix -p bed MethylationCall.bed.gz

2- Getting haplotype methylome:

nanomethphase  phase -mc MethylationCall.bed.gz -o Test_methylome -of bam,methylcall,bam2bis -b sorted.bam -r hg38.fa -v Phased.vcf -t 64

You can select 3 output options:

bam: output phased bam files

methylcall: this will output phased methylation call (MethylCall.tsv, read level data) and methylation frequency files (MethylFrequency.tsv, Aggregated methylations for each region. These files can be used to detect differentially methylated regions between haplotype using dma module.). The headers for methylation call files are as follow:

Shorten	Description
chromosome	Chromosome name.
start	Zero-Based start position of CpG.
end	Zero-Based end position of CpG.
strand	Strand.
read_name	Read ID.
log_lik_ratio	llr from nanopolish given to each CpG as being methylated or not.

The headers for methylation frequency files are as follow:

Shorten	Description
chromosome	Chromosome name.
start	Zero-Based start position of CpG.
end	Zero-Based end position of CpG.
strand	Strand.
NumOfAllCalls	Number of all called CpGs.
NumOfModCalls	Number of all CpGs that called as methylated.
MethylFreq	Methylation frequency (NumOfModCalls/NumOfAllCalls).

bam2bis: output mock whole-genome bisulfite converted bam files which can be visualized in IGV.

Full Tutorial

In order to get the phased methylome you also need the following third-party software:

Nanopolish : To call CpG methylation.

Clair or other variant callers: To call variants for your sample. Alternatively, you might already have variant calling data for example from Illumina sequencing.

WhatsHap: To phase single nucleotide variants.

1- Methylation Calling

1-1 indexing fastq file and fast5 files:

NOTE: Fastqs must be merged to a single file

nanopolish index -d /path/to/fast5s_directory/.fastq

2-1 Methylation calling for CpG from each read:

nanopolish call-methylation -t <number_of_threads> -q cpg -r /path/to/fastq_fromstep-1/fastq.fastq -b /path/to/sorted_and_indexed/bam.bam -g /path/to/reference.fa > /path/to/MethylationCall.tsv

For the full tutorial please refer to Nanopolish page on GitHub.

2- Variant Calling

We have used Clair to call variants. However, you may call variants with other tools or your variant data may come from Illumina or other methods.

You can call variants for each chromosome using the following command and the concatenate all files:

for i in chr{1..22} chrX chrY; do callVarBam --chkpnt_fn <path_to_model_file> --ref_fn <reference_genome.fa> --bam_fn <sorted_indexed.bam> --ctgName $i --sampleName <your_sample_name> --call_fn $i".vcf" --threshold 0.2 --samtools <path_to_executable_samtools_software> --pypy <path_to_executable_pypy > --threads <number_of_threads>

For the full tutorial please refer to Clair page on GitHub.

After variant calling, you can select only SNVs which will be used for phasing:

awk '$4 != "." && $5 != "." && length($4) == 1 && length($5) == 1' && $6 > <the_variant_calling_quality_threshold> variants.vcf > HighQualitySNVs.vcf

If you are calling variants from low coverage nanopore data (<30x) using Clair, you can also use our other tool SNVoter to improve SNV detection.

3- Phasing of detected SNVs

If you have your SNVs data available you need to phase them using WhatsHap.

whatshap phase --ignore-read-groups --reference reference.fa -o HighQualitySNVs_whatshap_phased.vcf HighQualitySNVs.vcf sorted_indexed.bam

For the full tutorial please refer to WhatsHap page on GitHub.

If you have Trio data (Father, Mother, Child) you can use the script Trio_To_PhaseVCF_4FemaleChild.sh or Trio_To_PhaseVCF_4MaleChild.sh script to make a mock phased vcf file and use it as input for NanoMethPhase.

4- Detecting Haplotype Methylome

1-4 First you need to phase process methylation call file from Nanopolish.

nanomethphase methyl_call_processor -mc MethylationCall.tsv -t 20 | sort -k1,1 -k2,2n -k3,3n | bgzip > MethylationCall.bed.gz && tabix -p bed MethylationCall.bed.gz

2-4 Getting haplotype methylome:

nanomethphase  phase -mc MethylationCall.bed.gz -o Test_methylome -of bam,methylcall,bam2bis -b sorted.bam -r hg38.fa -v Phased.vcf -t 64

If your are not using called SNVs from nanopore data, and they come from, for example, short-read sequencing, we recommend using -mbq 0 in the above code.

You can select 3 output options:

bam: output phased bam files

methylcall: this will output phased methylation call (MethylCall.tsv, read level data) and methylation frequency files (MethylFrequency.tsv, Aggregated methylations for each region. These files can be used to detect differentially methylated regions between haplotype using dma module.). The headers for methylation call files are as follow:

Shorten	Description
chromosome	Chromosome name.
start	Zero-Based start position of CpG.
end	Zero-Based end position of CpG.
strand	Strand.
read_name	Read ID.
log_lik_ratio	llr from nanopolish given to each CpG as being methylated or not.

The headers for methylation frequency files are as follow:

Shorten	Description
chromosome	Chromosome name.
start	Zero-Based start position of CpG.
end	Zero-Based end position of CpG.
strand	Strand.
NumOfAllCalls	Number of all called CpGs.
NumOfModCalls	Number of all CpGs that called as methylated.
MethylFreq	Methylation frequency (NumOfModCalls/NumOfAllCalls).

bam2bis: output mock whole-genome bisulfite converted bam files which can be visualized in IGV.

3-4 Differential Methylation Analysis:

nanomethphase dma -c 1,2,4,5,7 -ca <path to methylation frequency for haplotype1> -co <path to methylation frequency for haplotype2> -rs <path to executable Rscript> -o <output directory> -op <output Prefix>

We use DSS R/Bioconductor package to call DMRs between haplotypes. callDMR.txt is the main output you need that stores differentially methylated regions, callDML.txt is the output that stores differentialy methylated loci and DMLtest.txt is the output that stores statistical test results for all loci. For more documentation of output data refere to DSS page.

To findout about different modules run:

nanomethphase -h

For a full list of options and help for each module run:

nanomethphase <module> -h

We have included an example data in the Example_Data folder which you can use for a quick detection of haplotype methylome on 1Mb of chr21.