methylome-miner 1.1.8

A tool for handling DNA methylation data in extended bedMethyl format obtained from the nanopore sequencing data by Dorado and Modkit.

MethylomeMiner

MethylomeMiner is a tool for handling DNA methylation data in extended bedMethyl format obtained from the nanopore sequencing data by Dorado and Modkit.

Content

• Features
• Installation
• Usage
  • MethylomeMiner
  • PanMethylomeMiner
• Contact
• License
• Citation

Features

• Filter bedMethyl tables based on valid coverage and the methylation rate.
• Sort base modifications into coding and non-coding types using genome annotation.
• Split base modifications according to reference sequences.
• Provide count statistics for each base modification, coding/non-coding types and reference sequence.
• Link coding base modifications with results from pangenome analysis conducted by Roary.

Installation

1. Create a Python 3.12 or newer virtual environment.
2. Install MethylomeMiner using:

python -m pip install methylome-miner

Usage

MethylomeMiner functionality is divided into two parts. The first part named MethylomeMiner deals with manipulation of bedMethyl files, the other, PanMethylomeMiner, integrates pangenome analysis with DNA methylation data processed by MethylomeMiner to create panmethylome.

MethylomeMiner

The primary use of MethylomeMiner is to filter out base modifications with low coverage and low rate of base modification and to sort methylations into coding and non-coding groups. So-called coding methylations are base modifications that are located in some coding region of the genome. On the other hand non-coding methylations are found between coding parts of the genome.

The basic command to run MethylomeMiner from command line is following:

MethylomeMiner --input_bed_file path\to\bed_file.bed --input_annot_file path\to\annot_file.gff --min_coverage 35

Required parameters

--input_bed_file

Path to a bedMethyl file with genome-wide single-base methylation data.

--input_annot_file

Path to a file with genome annotation in gff (v3) or gbk file format.

--min_coverage and --input_bed_dir

Parameters --min_coverage and --input_bed_dir are mutually exclusive as they are used to assess the minimum coverage of base modification to be retained.

The user can either set the threshold of minimum coverage by using parameter --min_coverage and an integer value of requested coverage, or provide a path to directory with bedMethyl files (--input_bed_dir parameter) and the threshold would be set to the median value of median values of coverage from individual bedMethyl files within the directory.

Optional parameters

--min_percent_modified

Sets minimum required percentage of reads supporting base modification. The value is float in the range <0, 100>. Default: 90.

--work_dir

Specifies the output directory where MethylomeMiner will save all result files. If not provided, a folder named MethylomeMiner_output will be automatically created in the current working directory.

--file_name

Specifies a custom name to use for the output files generated by MethylomeMiner. By default, MethylomeMiner use the name of the input bedMethyl file and add a specific suffix for each output (see Output files)

--write_filtered_bed

A flag. When set, the script writes the filtered bedMethyl data to a new output file in working directory. Use this option if you want to save the filtered results.

--filtered_bed_format

Specifies the format of the filtered bedMethyl output file. Available options: json, csv, tsv, bed. Default: csv. Use this option to control the format in which the filtered data is saved.

--split_by_reference

A flag. If set, all output files (except for filtered bedMethyl file) will be split and saved separately for each reference sequence.

Output files

The output of MethylomeMiner is a set of new files distinguished by file suffixes:

• _coding.csv - extended bedMethyl table with information about coding region, where methylations are located.

  reference_seq	start_index	end_index	modified_base_code	score	strand	...	percent_modified	...	gene_reference_seq	gene_id	gene_start	gene_end	gene_strand	gene_product
  chromosome_contig_1	1108	1109	6mA	36	+	...	94.44	...	chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_1	0	1335	+	chromosomal replication initiator protein DnaA
  chromosome_contig_1	1753	1754	6mA	36	+	...	97.25	...	chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_2	1339	2440	+	DNA polymerase III subunit beta
  chromosome_contig_1	1867	1868	5mC	37	+	...	100.0	...	chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_2	1339	2440	+	DNA polymerase III subunit beta
  chromosome_contig_1	2699	2700	6mA	36	+	...	97.25	...	chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_3	2647	3721	+	DNA replication/repair protein RecF
  chromosome_contig_1	2728	2729	5mC	36	+	...	100.0	...	chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_3	2647	3721	+	DNA replication/repair protein RecF
  ...

• _non_coding.csv - extended bedMethyl table with information about coding regions, that are located in front of and after methylation. Modifications in front of the first coding feature have empty annotation marked as prev (previous). Modifications after the last coding feature have empty annotation marked as next.

  reference_seq	start_index	end_index	modified_base_code	score	strand	...	percent_modified	...	prev_gene_reference_seq	prev_gene_id	prev_gene_start	prev_gene_end	prev_gene_strand	prev_gene_product	next_gene_reference_seq	next_gene_id	next_gene_start	next_gene_end	next_gene_strand	next_gene_product
  chromosome_contig_1	1482	1483	6mA	36	-	...	100.0	...							chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_14	15874	16207	-	YceK/YidQ family lipoprotein
  chromosome_contig_1	1601	1602	6mA	36	-	...	100.0	...							chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_14	15874	16207	-	YceK/YidQ family lipoprotein
  ...
  chromosome_contig_1	3553006	3553007	6mA	41	+	...	97.56	...	chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_3407	3544141	3545086	+	CDF family zinc transporter ZitB	chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_3435	3568768	3570052	+	citrate synthase
  ...
  plasmid_contig_3	153394	153395	5mC	45	+	...	100.0	...	plasmid_contig_3	0792ceb9f153a28951804de2a656f49c_5106	151555	152305	+	hypothetical protein
  plasmid_contig_3	153396	153397	5mC	36	-	...	100.0	...	plasmid_contig_3	0792ceb9f153a28951804de2a656f49c_5099	146025	146250	-	IS1-like element transposase

• _all_annot_with_coding_methylations.csv - extended genome annotation table, where each coding region has a list of found methylations' positions distinguished by methylation type.

  record_id	gene_id	start	end	strand	product	note	4mC	5mC	6mA
  chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_1	0	1335	+	chromosomal replication initiator protein DnaA	WP_015703901.1 chromosomal replication initiator protein DnaA (Klebsiella) [pid:94.8%, q_cov:100.0%, s_cov:95.1%, Eval:5.9e-240]	[]	[]	[1108]
  chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_2	1339	2440	+	DNA polymerase III subunit beta	WP_015369032.1 DNA polymerase III subunit beta (Klebsiella aerogenes) [pid:98.1%, q_cov:100.0%, s_cov:100.0%, Eval:5.2e-204]	[]	[1867]	[1753]
  chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_3	2647	3721	+	DNA replication/repair protein RecF	WP_015703900.1 DNA replication/repair protein RecF (Klebsiella aerogenes) [pid:97.5%, q_cov:100.0%, s_cov:100.0%, Eval:1.5e-200]	[]	[2728, 3115]	[2699, 2827, 3179, 3640]
  chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_4	3749	6164	+	DNA topoisomerase (ATP-hydrolyzing) subunit B	WP_015703899.1 DNA topoisomerase (ATP-hydrolyzing) subunit B (Klebsiella aerogenes) [pid:95.1%, q_cov:100.0%, s_cov:100.0%, Eval:0.0e+00]	[]	[3962, 4265, 4307, 4535, 4979, 5012, 5069, 5606, 5825, 5915]	[4454, 4944, 5309, 5406, 5417, 5697, 5991, 6015, 6069]
  chromosome_contig_1	0792ceb9f153a28951804de2a656f49c_5	6365	7178	+	sugar-phosphatase	WP_015369035.1 sugar-phosphatase (Klebsiella aerogenes) [pid:94.8%, q_cov:100.0%, s_cov:100.0%, Eval:1.3e-142]	[]	[6857]	[6696, 6801, 6809, 7023]
  ...

• _methylations_statistics.csv - table of filtered methylation counts showing the number of methylation sites detected in coding and non-coding regions, along with their totals and the overall number of methylations.

  genome	4mC_coding_count	4mC_non_coding_count	4mC_total_count	5mC_coding_count	5mC_non_coding_count	5mC_total_count	6mA_coding_count	6mA_non_coding_count	6mA_total_count	total_count
  KP825	38	36	74	9425	10809	20234	15797	16386	32183	52491

• _methylations_statistics_per_ref.csv - as above, but the counts are divided according to reference sequences in the annotation.

  genome	reference_seq	4mC_coding_count	4mC_non_coding_count	4mC_total_count	5mC_coding_count	5mC_non_coding_count	5mC_total_count	6mA_coding_count	6mA_non_coding_count	6mA_total_count	total_count
  KP825	chromosome_contig_1	33	33	66	8874	10033	18907	14967	15399	30366	49339
  KP825	plasmid_contig_2	2	1	3	252	333	585	418	459	877	1465
  KP825	plasmid_contig_3	3	2	5	299	443	742	412	528	940	1687

• _filtered.* - optional, filtered bedMethyl table in CSV, TSV, JSON or BED format.

If --split_by_reference is used flag:

• _coding.csv file is replaced by <reference sequence 1>_coding.csv, <reference sequence 2>_coding.csv etc. for each reference sequence present in the annotation file,
• _non_coding.csv file is replaced by <reference sequence 1>_non_coding.csv, <reference sequence 2>_non_coding.csv etc. for each reference sequence present in the annotation file.

Examples

• To run MethylomeMiner with minimum coverage not specified, but instead path to directory with bedMethyl files is provided and minimum coverage is calculated as a median coverage value from all bedMethyl files in the directory:

  MethylomeMiner --input_bed_file input_files\KP825_4mC_5mC_6mA_calls.bed --input_annot_file input_files\KP825_genome.gff --input_bed_dir input_files
  

• To set minimum coverage and minimum percentage of reads supporting base modification:

  MethylomeMiner --input_bed_file input_files\KP825_4mC_5mC_6mA_calls.bed --input_annot_file input_files\KP825_genome.gff --min_coverage 36 --min_percent_modified 95
  

• To set up working directory and custom name for output files:

  MethylomeMiner --input_bed_file input_files\KP825_4mC_5mC_6mA_calls.bed --input_annot_file input_files\KP825_genome.gff --min_coverage 36 --work_dir path\to\output_dir --file_name output_file_name_prefix
  

• To write filtered bedMethyl table to default CSV file:

  MethylomeMiner --input_bed_file input_files\KP825_4mC_5mC_6mA_calls.bed --input_annot_file input_files\KP825_genome.gff --write_filtered_bed
  

• To write filtered bedMethyl file and select its format:

  MethylomeMiner --input_bed_file input_files\KP825_4mC_5mC_6mA_calls.bed --input_annot_file input_files\KP825_genome.gff --write_filtered_bed --filtered_bed_format bed
  

• To split and save all output files (except for filtered bedMethyl file) by reference sequences:

  MethylomeMiner --input_bed_file input_files\KP825_4mC_5mC_6mA_calls.bed --input_annot_file input_files\KP825_genome.gff --split_by_reference
  

PanMethylomeMiner

PanMethylomeMiner is designed to integrate the results from MethylomeMiner with pangenome analysis performed by Roary. Its main purpose is to generate a comprehensive panmethylome table, in which each gene from the pangenome is annotated with the detected methylations across multiple genomes.

The basic command to run PanMethylomeMiner from command line is following:

PanMethylomeMiner --input_bed_dir path\to\bed_dir --input_annot_dir path\to\annot_dir --roary_file path\to\gene_presence_absence.csv

PanMethylomeMiner uses MethylomeMiner to process all input files (bedMethyl and annotation files). Therefore, some parameters in PanMethylomeMiner are similar to those in MethylomeMiner, allowing control over its internal settings.

In the basic use case, MethylomeMiner filters base modifications using a default threshold of 90 % for the minimum percentage of modified reads. The threshold for minimum coverage is automatically set to the median value calculated from all bedMethyl files provided via the --input_bed_dir parameter.

Required parameters

--input_bed_dir

Path to a directory with bedMethyl files, which should be integrated into pangenome analysis.

--input_annot_dir

Path to a directory with annotation files, that were used as input into pangenome analysis. Genome annotations could be in gff (v3) or gbk file format, but the locus tags for individual CDS have been renamed to correspond with the gene names used in the Roary output.

Important note:

PanMethylomeMiner pairs bedMethyl and annotation files according to the file names, more precisely by a prefix. In this case, a prefix is every character in front of the first underscore _ character. This prefix is later used to distinguish the individual genomes in the pangenome analysis.

Make sure the files are named accordingly, for example:

prefix	bedMethyl file	annotation file
KP825	KP825_4mC_5mC_6mA_calls.bed	KP825_genome.gff
KP1651	KP1651_4mC_5mC_6mA_calls.bed	KP1651_genome.gff
KP1824	KP1824_4mC_5mC_6mA_calls.bed	KP1824_genome.gff

--roary_file

Path to output file from Roary tool named gene_presence_absence.csv.

Optional parameters

--min_coverage

Sets minimum (as integer value) required read coverage for a base modification to be retained. This option is only used if MethylomeMiner was not previously run for the input bedMethyl files and no precomputed results are present in the work_dir folder. If not provided, the median coverage will be automatically calculated from files in the input_bed_dir folder.

--min_percent_modified

Sets minimum required percentage of reads supporting base modification. The value is float in the range <0, 100>. Default: 90. This option is only used if MethylomeMiner was not previously run for the input bedMethyl files and no precomputed results are present in the work_dir folder.

--matrix_values

Defines the format of values used in the output panmethylome matrix.

Options:

• presence: 0 for no detected base modification, 1 for detected base modification, no value for missing gene in the corresponding genome.
• positions: A list of genomic positions where base modifications were identified within each pangenome gene or no value for missing gene in the corresponding genome.

Default: presence.

If a particular gene from pangenome was not found in a genome, value in the matrix is left empty.

--work_dir

Specifies the output directory where PanMethylomeMiner will look for results from MethylomeMiner and save all result files. If not provided, a folder named MethylomeMiner_output will be automatically created in the current working directory.

--write_all_results

A flag. If set, writes all result tables produced by MethylomeMiner to output files. This includes:

• All detected methylation sites split into coding and non-coding regions.
• The extended genome annotation file with detected methylations located in coding regions.
• Filtered bedMethyl files after quality thresholds are applied.

Use this option if you want to save full intermediate results.

--heatmap_type

Specifies which type of heatmap visualization should be generated from the panmethylome presence matrix. Heatmaps are only created if the matrix values (via --matrix_values) are set to presence.

Options:

• compact: Generates a lower-resolution heatmap without gene names on the y-axis. Suitable for a quick overview across genomes.
• full: Generates a full-resolution heatmap with gene names. This version is more detailed but may result in a large image depending on the number of genes.
• both: Creates both compact and full heatmaps.

If no value is provided, no heatmap will be generated.

--heatmap_file_format

Sets the output format for generated heatmap(s). This applies to all generated heatmaps. Aviable options are png, svg and pdf.

Default: pdf.

--heatmap_min_percent_presence

Sets the minimum percentage of genomes in which a gene must be present in order to be included in the heatmap(s). This helps filter out genes that are absent from most genomes. For example --heatmap_min_percent_presence 90 will include only genes present in at least 90 % of genomes. Value 100 % will create heatmap for core genes.

The value is float in the range <0, 100> and default is 95.

Output files

PanMethylomeMiner results are provided as a set of CSV files, each named according to the type of base modification (e.g., 5mC, 6mA, 4mC) identified in the dataset. For each detected modification, a panmethylome matrix is generated in the format specified by the user:

presence

The presence/absence matrix indicates whether a given pangenome gene contains any detected base modifications of the specified type.

File name format: <modification>_panmethylome_presence.csv

Example: 5mC_panmethylome_presence.csv

Matrix values:

• 1 – base modification detected in the gene
• 0 – no modification detected
• no value – the gene is absent in the corresponding genome

Gene	KP1248	KP1267	KP1344	KP1622	KP1651	KP1658	KP1665	KP1666	KP1785	KP1814	KP1821	KP1822	KP1824	KP1829	KP1832	KP1833	KP1834	KP1842	KP1906	KP825
recD	1	1	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	0	0
paaF	0	1	1	1	1	1	0	1	0	1	1	1	0	1	1	1	1	0	0
tynA	0	1	0	1	1	1	0	1	0	1	1	0	1	0	1	1	1	0	0
group_1004	0	1	0	1	1	1	0	1	1	1	1	1	1	1	0	1	1	0	0
ompC	0	0	0	0	0	0	0	1	1	1	1	0	0	0	0	1	1	0	0
...

positions

Matrix stores precise genomic positions of modified bases located within each pangenome gene.

File name format: <modification>_panmethylome_positions.csv

Example: 5mC_panmethylome_positions.csv

Matrix values:

• a list of integers - genomic positions of detected base modifications in the gene
• empty list - no modifications were detected
• no value - the gene is absent in the corresponding genome

Gene	KP1248	KP1267	KP1344	KP1622	KP1651	KP1658	KP1665	KP1666	KP1785	KP1814	KP1821	KP1822	KP1824	KP1829	KP1832	KP1833	KP1834	KP1842	KP1906	KP825
recD	[923982, 924145, 924181]	[994950, 995152, 995220, 995315]	[]	[901881, 902044, 902535]	[902535, 902698]	[901537, 901881, 902044, 902440, 902535, 902698, 902734]	[]	[901539, 901883, 902046, 902442, 902537, 902700, 902736]	[888326, 888468, 888670, 888833]	[900277, 900419, 900621, 900784, 901180, 901275, 901438, 901474]	[909850, 910143, 910539, 910634, 910797]	[909638, 909852, 910145, 910541, 910636]	[909636, 909850, 910143, 910539, 910634, 910797]	[909639, 909853, 910146, 910637, 910800]	[909849, 910142, 910633, 910796]	[909791, 909988, 910084, 910480, 910575]	[909794, 910087, 910483, 910578, 910741]	[]	[]
paaF	[]	[2966497, 2966854]	[2931769]	[2868686, 2869043]	[2859549, 2859906]	[2859393, 2859750]	[]	[2859429]	[]	[2898167, 2898524]	[2922405, 2922762]	[2922416, 2922773]	[]	[2922408, 2922765]	[2922406]	[2922341, 2922698]	[2922351, 2922708]	[]	[]
tynA	[]	[2978635, 2978860, 2978918, 2979184, 2979446, 2979719, 2979927, 2979977]	[]	[2880824, 2881049, 2881107, 2881635, 2881908, 2881929, 2882116, 2882166, 2882472, 2882835]	[2871687, 2871912, 2871970, 2872236, 2872498, 2872771, 2872792, 2872979, 2873029, 2873335, 2873698]	[2871531, 2872615, 2872823, 2873179, 2873542]	[]	[2872651, 2872859, 2872909, 2873578]	[]	[2911116, 2911389, 2911597, 2911647, 2911953, 2912071, 2912304, 2912316]	[2934543, 2934768, 2934826, 2935354, 2935627, 2935648, 2935835, 2935885, 2936191]	[]	[2934768, 2934826, 2935092, 2935627, 2936191, 2936228]	[]	[2934827, 2935628, 2935649, 2935836, 2936192, 2936229]	[2934479, 2934762, 2935563, 2935584, 2935771, 2936127]	[2934489, 2934714, 2934772, 2935300, 2935573, 2935594, 2935781, 2936137]	[]	[]
group_1004	[]	[2986640, 2986681, 2986802, 2986969, 2988160]	[]	[2888828, 2888869, 2888990, 2889157, 2890348]	[2879682, 2879723, 2879844, 2880011, 2881202]	[2879526, 2879567, 2879688, 2879855, 2881046]	[]	[2879562, 2879603, 2879724, 2879891, 2881082]	[2800569]	[2918309, 2918350, 2918471, 2918638, 2919829, 2919919]	[2942547, 2942588, 2942709, 2944067]	[2942599]	[2942547, 2942588, 2942709]	[2942550, 2942591, 2942712, 2944070]	[]	[2942483, 2942524]	[2942493, 2942534, 2943482, 2944013]	[]	[]
ompC	[]	[]	[]	[]	[]	[]	[]	[2889656, 2889662]	[2809144]	[2928048, 2928088, 2928433, 2928439]	[2952274, 2952314, 2952641, 2952647]	[]	[]	[]	[]	[2952210, 2952250, 2952577, 2952583]	[2952587, 2952593]	[]	[]
...

Statistics

Additionally, methylation statistics from MethylomeMiner are merged to provide an overview across all genomes in pangenome dataset. Therefore, two CSV files are created:

• all_methylations_statistics.csv - table of filtered methylation counts showing the number of methylation sites detected in coding and non-coding regions, along with their totals and the overall number of methylations

  genome	4mC_coding_count	4mC_non_coding_count	4mC_total_count	5mC_coding_count	5mC_non_coding_count	5mC_total_count	6mA_coding_count	6mA_non_coding_count	6mA_total_count	total_count
  KP1248	20	18	38	6860	7973	14833	11027	11756	22783	37654
  KP1267	113	141	254	17397	20724	38121	28528	30944	59472	97847
  KP1344	6	15	21	3651	4483	8134	5799	6805	12604	20759

• all_methylations_statistics_per_ref.csv - as above, but the counts are divided according to reference sequences in the annotation

  genome	reference_seq	4mC_coding_count	4mC_non_coding_count	4mC_total_count	5mC_coding_count	5mC_non_coding_count	5mC_total_count	6mA_coding_count	6mA_non_coding_count	6mA_total_count	total_count
  KP825	chromosome_contig_1	33	33	66	8874	10033	18907	14967	15399	30366	49339
  KP825	plasmid_contig_2	2	1	3	252	333	585	418	459	877	1465
  KP825	plasmid_contig_3	3	2	5	299	443	742	412	528	940	1687
  KP1248	chromosome_contig_1	19	17	36	6482	7474	13956	10514	11148	21662	35654
  KP1248	plasmid_contig_2	0	1	1	119	141	260	152	197	349	610
  KP1248	plasmid_contig_3	1	0	1	259	358	617	361	411	772	1390
  KP1267	chromosome_contig_1	112	138	250	16958	20098	37056	27883	30075	57958	95264
  KP1267	plasmid_contig_2	1	3	4	439	626	1065	645	869	1514	2583
  KP1344	chromosome_contig_3	6	13	19	3332	4046	7378	5329	6255	11584	18981
  KP1344	plasmid_contig_1	0	2	2	319	437	756	470	550	1020	1778

Examples

• To run set custom filtering thresholds for MethylomeMiner

  PanMethylomeMiner --input_bed_dir path\to\bed_dir --input_annot_dir path\to\annot_dir --roary_file path\to\gene_presence_absence.csv --min_coverage 36 --min_percent_modified 95
  

• To change type of values in panmethylome matrix to positions

  PanMethylomeMiner --input_bed_dir path\to\bed_dir --input_annot_dir path\to\annot_dir --roary_file path\to\gene_presence_absence.csv --matrix_values positions
  

• To set up working directory

  PanMethylomeMiner --input_bed_dir path\to\bed_dir --input_annot_dir path\to\annot_dir --roary_file path\to\gene_presence_absence.csv --work_dir path\to\output_dir
  

• To request all results from MethylomeMiner to be saved to files

  PanMethylomeMiner --input_bed_dir path\to\bed_dir --input_annot_dir path\to\annot_dir --roary_file path\to\gene_presence_absence.csv --write_all_results
  

• To generate compact heatmap in png file format for genes with 95 % presence

  PanMethylomeMiner --input_bed_dir path\to\bed_dir --input_annot_dir path\to\annot_dir --roary_file path\to\gene_presence_absence.csv --heatmap_type compact
  

• To generate full heatmap in svg file format for genes with 95 % presence

  PanMethylomeMiner --input_bed_dir path\to\bed_dir --input_annot_dir path\to\annot_dir --roary_file path\to\gene_presence_absence.csv --heatmap_type full --heatmap_file_format svg
  

• To generate both heatmaps (compact and full) in svg file format for genes with 100 % presence in analyzed genomes = core genes

  PanMethylomeMiner --input_bed_dir path\to\bed_dir --input_annot_dir path\to\annot_dir --roary_file path\to\gene_presence_absence.csv --heatmap_type both --heatmap_file_format svg --heatmap_min_percent_presence 100
  

Citation

If you use MethylomeMiner in your research, please cite the following article:

Jakubickova, M., Sabatova, K., Zbudilova, M., Bezdicek, M., Lengerova, M., Vitkova, H. MethylomeMiner: A novel tool for high-resolution analysis of bacterial methylation patterns from nanopore sequencing. Computational and Structural Biotechnology Journal, 27 (2025), pp. 4753–4759. https://doi.org/10.1016/j.csbj.2025.10.047

Formal citation metadata are available in the CITATION.cff file and via the Cite this repository button.

Contact

MethylomeMiner's maintainers:

• Katerina Sabatova - @KSabatova
• Marketa Jakubickova - @MakretaJakubickova

License

See the LICENSE file for license rights and limitations (GNU General Public License v3.0).