yaningyang / methylartist Goto Github PK

Tools for parsing and plotting methylation patterns from nanopore data

Recommended:

pip install methylartist

Likely outdated:

conda install -c bioconda methylartist

Alternatively:

git clone https://github.com/adamewing/methylartist.git or download a .zip file from GitHub.

install methylartist and dependencies via:

python setup.py install

Tests/examples:

git clone https://github.com/adamewing/methylartist-tests.git
cd methylartist-tests
source run_tests.sh

Alignments stored in .bam format should be sorted and indexed and should use the same read names as the associated methylation data.

The easiest way to provide modified basecall data is through .bam files with the Mm and Ml tags for modified base calling. Note that the mod_mappings.bam file output by megalodon will work for modified base calling, but is unsuitable for downstream applications involving sequence variation, including phasing.

If .bam files with modified base calls are not available, methylartist has functions for loading methylation data can from nanopolish, megalodon, or basecalled guppy fast5s, using the appropriate function below.

Once coverted, the sqlite .db file can be input to methylartist functions (e.g. segplot, locus, etc).

Load nanopolish methylation into sqlite db.

Example:

Loading results from nanopolish call-methylation to a database:

methylartist db-nanopolish -m MCF7_ATCC_REP1.nanopolish.tsv.gz -d MCF7_ATCC.nanopolish.db

Appending additional results to the above database:

methylartist db-nanopolish -m MCF7_ATCC_REP2.nanopolish.tsv.gz -d MCF7_ATCC.nanopolish.db -a

Loading results with the current recommended cutoffs for nanopolish (abs(llr) > 2.0, scale grouped CpGs):

methylartist db-nanopolish -m MCF7_ATCC_REP1.nanopolish.tsv.gz,MCF7_ATCC_REP2.nanopolish.tsv.gz -d MCF7_ATCC.nanopolish.db -t 2.0 -s

Inputs can be uncompressed or .gzipped.

Load megalodon methylation into sqlite db.

The input file is the output of megalodon_extras per_read_text modified_bases /path/to/megalodon_output, which needs to be run prior to this script.

The default filename (/path/to/megalodon_output/per_read_modified_base_calls.txt) is the same for all megalodon runs, so the --db option is recommended to make the output database more identifiable for downstream analysis.

Example:

methylartist db-megalodon -m MCF7_ATCC_REP1/per_read_modified_base_calls.txt --db MCF7_ATCC.megalodon.db

Appending (-a) additional results to the above database:

methylartist db-megalodon -m MCF7_ATCC_REP2/per_read_modified_base_calls.txt --db MCF7_ATCC.megalodon.db -a

Input files can be uncompressed or .gzipped.

This enables free-form parsing of modified basecall tables into methylartist .db files for tools where modified base .bam files are not available and certain requirements are met. The table must contain, at a minimum, the read names, genomic position (chromosome and position), strand, and probability of the target base being modified. If not specified by a column (--modbasecol), the modification is specified by --modbase. The probability is assumed to be a raw probability between 0 and 1 of a given base being modified i.e. 1-p(canonical), other schemes may be used but --canprob and --mincanprob must be specified to set a column for canoncial base scores and a cutoff for a base being canonical.

For example, modified basecalls from deepsignal-plant can be loaded as follows:

/home/taewing/methylartist/methylartist db-custom -m deepsignal_example.C.call_mods.tsv --readname 4 --chrom 0 --pos 1 --strand 2 --modprob 7 --modbase m -d deepsignal_example.db

Outputs aggregate methylation / demethylation call counts over intervals. Required before generating strip / violin plots with segplot

Requires whitespace-delimited list of segments in a BED3+2 format: chromosome, start, end, label, strand.

One or more .bam files may be supplied via the -b/--bams parameter. Multiple .bams may be comma-delimited.

Optional sample input file -d/--data has the following whitespace-delimited fields (one sample per line): BAM file, Methylation DB (generated with e.g. db-nanopolish)

Highly recommend parallelising with -p/--proc option if possible.

Can be used to generate genome-wide methylation stats aggregated over windows via bedtools makewindows.

Example:

Aggregate whole-genome CpG methylation in 10kbp bins, promoters (Eukaryotic Promoter Database, EPD), L1HS and SVA retrotransposons:

methylartist segmeth -d MCF7_data_megalodon.txt -i MCF7_megalodon_annotations.bed -p 32

Contents of MCF7_data_megalodon.txt:

MCF7_ATCC.haplotag.bam MCF7_ATCC.megalodon.db
MCF7_ECACC.haplotag.bam MCF7_ECACC.megalodon.db

Contents of MCF7_megalodon_annotations.bed (first 10 lines):

chr1    0       10000   WG_10kbp
chr1    10000   20000   WG_10kbp
chr1    20000   30000   WG_10kbp
chr1    30000   40000   WG_10kbp
chr1    40000   50000   WG_10kbp
chr1    50000   60000   WG_10kbp
chr1    60000   70000   WG_10kbp
chr1    70000   80000   WG_10kbp
chr1    80000   90000   WG_10kbp
chr1    90000   100000  WG_10kbp

Output in MCF7_megalodon_annotations.segmeth.tsv (first 10 lines):

seg_id  seg_chrom       seg_start       seg_end seg_name        seg_strand      MCF7_ATCC.haplotag_m_meth_calls MCF7_ATCC.haplotag_m_unmeth_calls       MCF7_ATCC.haplotag_m_no_calls   MCF7_ATCC.haplotag_m_methfrac    MCF7_ECACC.haplotag_m_meth_calls        MCF7_ECACC.haplotag_m_unmeth_calls      MCF7_ECACC.haplotag_m_no_calls  MCF7_ECACC.haplotag_m_methfrac
chr1:0-10000    chr1    0       10000   WG_10kbp        .       0       0       0       NaN     0       0       0       NaN
chr1:10000-20000        chr1    10000   20000   WG_10kbp        .       4836    1205    893     0.8005297136235723      5994    1629    1254    0.7863046044864227
chr1:20000-30000        chr1    20000   30000   WG_10kbp        .       1923    2641    802     0.42134092900964065     2093    3216    1032    0.39423620267470333
chr1:30000-40000        chr1    30000   40000   WG_10kbp        .       974     790     273     0.5521541950113379      1331    821     416     0.6184944237918215
chr1:40000-50000        chr1    40000   50000   WG_10kbp        .       361     398     149     0.4756258234519104      579     664     255     0.4658085277554304
chr1:50000-60000        chr1    50000   60000   WG_10kbp        .       631     300     133     0.677765843179377       1086    472     242     0.6970474967907574
chr1:60000-70000        chr1    60000   70000   WG_10kbp        .       315     494     130     0.38936959208899874     571     671     255     0.45974235104669886
chr1:70000-80000        chr1    70000   80000   WG_10kbp        .       196     150     31      0.5664739884393064      288     214     79      0.5737051792828686
chr1:80000-90000        chr1    80000   90000   WG_10kbp        .       297     122     29      0.7088305489260143      127     57      16      0.6902173913043478

Generates strip plots or violin plots (-v/--violin) from segmeth output.

Examples:

Strip plot of whole-genome CpG methylation in 10kbp bins, promoters (Eukaryotic Promoter Database, EPD), L1HS and SVA retrotransposons:

methylartist segplot -s MCF7_megalodon_annotations.segmeth.tsv

As above, but use violin plots:

methylartist segplot -s MCF7_megalogon_annotations.segmeth.tsv -v

Note that default output is in .png format. For .svg vector output suitable for editing in inkscape or illustrator add the --svg option. Note that for strip plots, this is often inadvisable due to the large number of points.

New in 1.0.7, ridge plots (-g/--ridge):

methylartist segplot -s L1_FL.MCF7_data_megalodon.segmeth.tsv -c L1HS,L1PA2,L1PA3,L1PA4,L1PA5,L1PA6,L1PA7,L1PA8 -g --palette magma

Ridge plots can also be grouped by annotation (--ridge_group_by_annotation) rather than by sample as in the above example.

Generates smoothed methylation profiles across specific loci with many configurable parameters for one or more samples.

One or more .bam files (with Mm/Ml tags) may be supplied via the -b/--bams parameter. Multiple .bams may be comma-delimited.

Optional sample input file -d/--data has the following whitespace-delimited fields (one sample per line): BAM file, Methylation DB (generated with e.g. db-nanopolish)

Example:

Plot of the GPER1 locus in hg38, highlighting the GeneHancer promoter/enhancer annotation (GH07J001085).

methylartist locus -d MCF7_data_megalodon.txt -i chr7:1072064-1101499 -g Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz --genes GPER1 -l 1085667-1089471 -p 1,6,1,3,4

From top to bottom, the plot shows the genome coordinates, gene models (optional if --gtf is supplied), read mappings with modified bases as closed (modified) or open (unmodified) circles, translation from genome to CpG-only coordinate space, raw log-likelihood ratios, and smoothed methylated fraction plot. Note that --genes results in plotting only the genes specified after --genes (comma-delimited), leaving this out plots all genes across the window. Gene name labels can be added via --labelgenes:

methylartist locus -d MCF7_data_megalodon.txt -i chr7:1072064-1101499 -g Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz -l 1085667-1089471 -p 1,6,1,3,4 --labelgenes

Examples:

Plot of the ESR2 locus using the --samplepalette option:

methylartist locus -d MCF7_data_megalodon.txt -i chr14:64213064-64351433 --gtf Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz --genes ESR2,SYNE2,TEX21P --samplepalette viridis

Plot of the ERBB2 (HER2) locus in hg38, using a data input file (-d) with custom colour specification:

methylartist locus -d MCF7_data_megalodon.colours.txt -i chr17:39677914-39738361 -g Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz --genes ERBB2 --highlight_bed erbb2.highlights.txt --highlightpalette viridis --labelgenes

Contents of MCF7_data_megalodon.colours.txt:

MCF7_ATCC.haplotag.bam MCF7_ATCC.megalodon.db #FC766A
MCF7_ECACC.haplotag.bam MCF7_ECACC.megalodon.db #184A45

Contents of erbb2.highlights.txt:

chr17 39686731 39689728
chr17 39698981 39707766

Use the --phased flag to produce haplotype-aware methylation profiles. Requires haplotypes to be tagged with whatshap.

Examples:

Plot of the PEG3 imprinted region on chromosome 19, hg38.

methylartist locus -d MCF7_data_megalodon.txt -i chr19:56810076-56870725 -l 56835376-56840476 -g Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz --genes PEG3 --slidingwindowsize 20 -s 36 --samplepalette viridis --phased

Note the apparent differential imprinting between the ATCC and ECACC MCF7 lines.

More tractable than "locus" for larger regions using binned methylation.

Example:

Plot of the PGR (Progesterone Receptor) region on chr11 in MCF7 cells. Highlighed region corresponds to the GeneHancer annotation GH11J101126. Note the parameter setting -n CG to normalised bins for content of CpG dinucleotides. Options --samplepalette and --highlightpalette are used to set colours. The number of windows and smoothing parameters are set automatically but can be modified from automatically set values via -w/--windows and -s/--smoothwindowsize.

methylartist region -i chr11:100956822-101228191 -d MCF7_data_megalodon.txt -g Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz -p 32 -n CG -r Homo_sapiens_assembly38.fasta --samplepalette magma -l 101126888-101129371 --highlightpalette viridis

Expanded to a larger ~2Mbp region:

methylartist region -i chr11:98956822-103228191 -d MCF7_data_megalodon.txt -g Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz -p 32 -n CG -r Homo_sapiens_assembly38.fasta --samplepalette magma -l 101126888-101129371 --highlightpalette viridis

For an even larger region, it helps to drop the read alignment panel:

methylartist region -i chr11:95956822-106228191 -d MCF7_data_megalodon.txt -g Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz -p 32 -n CG -r Homo_sapiens_assembly38.fasta --samplepalette magma -l 101126888-101129371 --highlightpalette viridis --skip_align_plot --panelratio 1,0,1,4 --height 4.5

It's also possible to use region plots to explore methylation profiles across entire chromosomes. This can be accomplished through turning off the read alignment plot with --skip_align_plot and adjusting --panelratios to set the read alignment plot panel to 0. Setting the image height via --height 4.5 maintains similar panel heights to the default.

Example:

methylartist region -i chr22:1-50818468 -d MCF7_data_megalodon.txt -p 32 -g Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz -n CG -r Homo_sapiens_assembly38.fasta --skip_align_plot --panelratios 1,0,1,4 --height 4.5 --genepalette viridis --samplepalette viridis

methylartist region -i chr17:1-83257441 -d MCF7_data_megalodon.txt -p 32 -g Homo_sapiens.GRCh38.97.chr.sorted.gtf.gz -n CG -r Homo_sapiens_assembly38.fasta --skip_align_plot --panelratios 1,0,1,4 --height 4.5 --genepalette viridis --samplepalette viridis

Generates "composite" methylation plots where multiple per-element profiles are aligned to and plotted against a reference sequence.

Example:

The contents of L1HS.bed are formatted as follows (first 10 lines), derived from hg38 repeatmasker .out files:

chr1    34566056        34572105        L1HS    -
chr1    67078892        67084915        L1HS    -
chr1    71513699        71519742        L1HS    +
chr1    80939204        80945257        L1HS    -
chr1    84052390        84058406        L1HS    -
chr1    85748520        85754548        L1HS    +
chr1    85927068        85933100        L1HS    +
chr1    86679081        86685111        L1HS    -
chr1    104770248       104776278       L1HS    -
chr1    104843834       104849864       L1HS    -

The consensus sequence for establishing a common coordinate system is in L1.3.fa (fasta formatted with one sequence), available here: L19088.1

The contents of L1.3.highlights.bed used for optional highlighting of regions (ORF1 and ORF2 in this case):

910 1926 ORF1 #bcbddc
1990 5817 ORF2 #756bb1

Command to methylartist composite (note the parsing of individual profiles can be parallelised via -p):

methylartist composite -d MCF7_data_megalodon.txt -s L1HS.bed -r Homo_sapiens_assembly38.fasta -t L1.3.fa -p 32 --blocks L1.3.highlights.bed

(adjust number of processes used for computing individual profiles via -p/--procs to be appropriate for your system)

Outputs genome-wide statistics on CpGs covered by at least one call. By default, wgmeth yields output in bedMethyl format (0-based coordinates):

chr1    10467   10468   MCF7_ECACC.nanopolish    3       .       10467   10468   0,0,0   3       1.0
chr1    10469   10470   MCF7_ECACC.nanopolish    3       .       10469   10470   0,0,0   3       1.0
chr1    10482   10483   MCF7_ECACC.nanopolish    7       .       10482   10483   0,0,0   7       0.7142857142857143
chr1    10487   10488   MCF7_ECACC.nanopolish    7       .       10487   10488   0,0,0   7       0.7142857142857143
chr1    10491   10492   MCF7_ECACC.nanopolish    7       .       10491   10492   0,0,0   7       0.7142857142857143
chr1    10495   10496   MCF7_ECACC.nanopolish    7       .       10495   10496   0,0,0   7       0.7142857142857143
chr1    10523   10524   MCF7_ECACC.nanopolish    8       .       10523   10524   0,0,0   8       0.875
chr1    10540   10541   MCF7_ECACC.nanopolish    11      .       10540   10541   0,0,0   11      0.9090909090909091
chr1    10561   10562   MCF7_ECACC.nanopolish    10      .       10561   10562   0,0,0   10      1.0
chr1    10569   10570   MCF7_ECACC.nanopolish    10      .       10569   10570   0,0,0   10      1.0

The --dss option yields genome-wide files suitable for input to DSS for statistical assessment of differential methylation (1-based coordinates):

chr     pos     N       X
chr1    10468   3       3
chr1    10470   3       3
chr1    10483   6       5
chr1    10488   6       5
chr1    10492   6       5
chr1    10496   6       5
chr1    10524   6       6
chr1    10541   8       7
chr1    10562   8       8

Outputs distribution of modified base statistics with current cutoffs for one or more methylartist databases

methylartist scoredist -d MCF7_ATCC.megalodon.db,MCF7_ECACC.megalodon.db -m m

Seth W Cheetham, Michaela Kindlova, Adam D Ewing, Methylartist: tools for visualizing modified bases from nanopore sequence data, Bioinformatics, Volume 38, Issue 11, 1 June 2022, Pages 3109–3112, https://doi.org/10.1093/bioinformatics/btac292