Bacmethy V 1.0.1

Summary

Bacmethy, a customizable pipeline based on Docker, is designed to calculate the enrichment significance of methylated and un(der)methylated motifs in the regulatory and coding regions of the genome. It also identifies genes that are co-affected by DNA methylation and transcription factor binding, enabling the prediction of transcriptional regulation effects by DNA methylation. By using Bacmethy, researchers can gain a more comprehensive understanding of bacterial epigenomes.
We offer three ways for utilizing Bacmethy: a web server, a Docker-based system, and a command-line tool.
Please cite our work by "Liu, Ji-Hong, Yizhou Zhang, Ning Zhou, Jiale He, Jing Xu, Zhao Cai, Liang Yang, and Yang Liu. 2024. “ Bacmethy: A Novel and Convenient Tool for Investigating Bacterial DNA Methylation Pattern and Their Transcriptional Regulation Effects.” iMeta e186. https://doi.org/10.1002/imt2.186."

Installation

1. WEB Sever users

Bacmethy Website The website includes methylation analysis and TFs binding prediction modules of Bacmethy.
No installation step is needed if using Bacmethy web server.

2. docker users

Docker image is provided for Windows or Mac users.

1. Make sure you have docker installed.
   

2. Use the command to get Bacmethy in docker:
   docker pull liujihong/Bacmethy:2.0
   docker run -t -i liujihong/Bacmethy:2.0 /bin/bash
   

3. re-enter docker.
   docker start docker attach

3. command-line tool users

1. install required softwares.
   a. PROKKA
   (Note: prokka v1.12 works fine. If you want to use a higher version of prokka, you may need to manually configure the environment such as: export LD_LIBRARY_PATH = your/path/to/conda/env/lib: $LD_LIBRARY_PATH)
   b. bedtools
   c. meme
   d. r-base
   (Note: The R packages ggplot2 and ggthemes need to be configured)
   e. circos [OPTION]
   f. seqkit [OPTION]
   

Make sure you have conda installed

wget -c https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh  
bash Miniconda2-latest-Linux-x86_64.sh  

creat a Bacmethy conda environment

conda -n Bacmethy  
conda activate Bacmethy  

conda install required softwares

conda install -c bioconda prokka    
conda install -c bioconda bedtools  
conda install -c bioconda meme  
conda install conda-forge::r-base  

2. Download the scripts from Github to your working directory.
   

   conda install git and clone script

   conda install git git clone https://github.com/LiuJih2021/Bacmethy.git cd ./Bacmethy/script/

Inputs

########

1. genome sequence file

genome.fa: The referece genome sequence file (fasta file for the complete genome sequence). Note: for step 2, "Prepare motifs files," it is essential to have complete genome sequences obtained from the same strain (same reference genome). This means that the genome sequences used for this step should be derived from the exact same bacterial strains. This ensures accuracy and consistency in the analysis of motifs.

2. motifs files

1. motifs.gff: Compliant with the GFF Version3 specification. Each template position/strand pair whose probability value exceeds the probability value threshold appears as a row. The template position is 1-based, per the GFF specifications. The strand column refers to the strand carrying the detected modification, which is the opposite strand from those used to detect the modification. The GFF confidence column is a Phred-transformed probability value of detection.
   
2. motifs.csv: Contains one row for each(reference,position, strand) pair that appeared in the Data Set with coverage at least x. x defaults to 3, but is configurable with the --minCoverage option. The reference position index is 1-based for compatibility with the GFF file in the R environment. Note that this output type scales poorly and is not recommended for large genomes; the HDF5 output should perform much better in these cases. ref: https://www.pacb.com/wp-content/uploads/SMRT_Tools_Reference_Guide_v10.1.pdf

3. PPM files [OPTION]

TF.meme: TFs (Transcription Factors) binding prediction is an optional function module of Bacmethy. To utilize this module, the TF matrix files in PPM format are needed (with a .meme suffix).

NOTE: Typically, you can obtain the genome.fa, motifs.gff, and motifs.csv files from a public database or through the SMRT-seq facility. The Pacbio SMRTseq facility is equipped with the pre-installed SMRTLink software.
Nevertheless, if you need to carry out SMRT-LINK analysis on your own, you may follow the instructions on the website.
PacBio provide HGAP4(Hierarchical Genome Assembly Process) to generate high quality de novo assemblies of genomes, using Continuous Long Reads.
Use Base Modification Analysis Application in SMRT-LINK or SMRT Tools to identify bacterial base modifications, and analyze the methyltransferase recognition motifs. Detection can be down using an in-silico control consisting of expected kinetic signals.
When using the "Base Modification Analysis Application", it's suggested to add the fraction and motif reauirements (-t kineticstools_compute_methyl_fraction=true -t run_find_motifs=true) to obtain methylation motif and methylation fraction information accurately.

Run Bacmethy

1. Run Bacmethy.sh

bash Bacmethy.sh -m motifs.gff -s motifs.csv -g genome.fasta -p PREFIX -t m6A 

Usage

bash Bacmethy.sh -m <motifs.gff> -s <motifs.csv> -g <genome.fa> -p <prefix> -t <m6A, m4C or m5C> [options] -d -b -a -r -T -n -G
requirement: locally installed PROKKA,  bedtools and MEME  softwares

required
    -m      FILE    a motif detected file in gff format from PacBio portal (required)
    -s      FILE    a motifs summary file (required)
    -g      FILE    a genome file which complete sequenced (required)
    -p      STRING  prefix of output (required; usually a strains name or a sample name)
    -t      STRING  a type of methylation type (required; one of m6A, m4C or m5C)

options
    -T      FOLDER  a floder contains TFs files in meme format (required; users can get from Calcute_PPM_console_final.py)
    -d      FLOAT   a undermethylation thresholds of fraction (default: 0.75)
    -i      FLOAT   a unmethylation thresholds of identificationQv (default: 40)
    -c      FLOAT   a unmethylation thresholds of coverage (default: 30)
    -b      INT     number of bps before TSS (default: 500)
    -a      INT     number of bps after TSS (default: 100)
    -r      FILE    FASTA or GBK file to use as 1st priority (default '')
    -n      INT     Number of CPUs to use (default '8')
    -G      NULL    Scan the DNA methylation sites on gene Coding region (default only scan Regulation Region)

Notice

The genome file used for Bacmethy analysis should be the same genome reference file which used in SMRTLink methylation motif detection analysis.
Additionally, there are two recommended options for better genome annotation:

option: -r add standard strains reference

The -r option allows users to add a reference file from a standard strain in the same species. This helps improve genome annotation.

option: -d methylation level direction

The -d option is used for specifying the methylation level direction. There are complex and dynamic epigenetic regulations involved in DNA methylation. To ensure the authenticity of methylation events, a sequencing coverage higher than 30 is required. The software classifies DNA methylation events into three levels: methylation, undermethylation, and unmethylation.

1. methylation: identificationQV >= 40
   
2. undermethylation: identificationQV >= 40 && fraction < 0.75
   
3. unmethylation: identifictionQV < 40
   The default threshold for undermethylation in Bacmethy is set to a fraction less than 0.75 and an identificationQV greater than or equal to 40.
   However, users can also set user-defined thresholds using the -d parameter to change the fraction and the -i parameter to alter the identificationQV threshold.
   

2. example

Bacmethy.sh -m motifs.gff -s motif_summary.csv -g genomic.fna -p K12 -t m6A -T /Your/Path/To/TF/meme

3. Running Time

Bacmethy uses parallel processing to decrease running time on multicore computers. Users can set Running CPU by parameter -n.

Outputs

structure of output files

• methylation
  • motif_CDS [OPTION]
  • motif_RR
    • motif_methylationType.methylation (whole modified base of this motif in this genome)
    • motif_methylationType.methylation_methylationGene.fasta (The Regulation Region in fasta format)
    • motif_methylationType.methylation_methylationGene.txt (methylated gene result)
    • motif_methylationType.motif.methylation_result.txt (methylation site distribution)
    • motif_methylationType.motif.methylation_rr_locustag.txt (The corresponding locus_tag in the Regulation Region at the methylation site)
• undermethylation
  • motif_CDS [OPTION]
  • motif_RR
• unmethylation
  • motif_CDS [OPTION]
  • motif_RR
• PREFIX (A folder that contains all the annotated information about your sample)
• *m6A_fraction.plot.pdf (The bar chart for fraction-frequence)
• *m6A_plot.pdf (The scatter plot for coverage-identificationQV)
• *m6A_motif (The raw data used for the plot)
• *m6A_motif_basemods.txt (The clean data used for the plot)

  Gene Features

  There are 3 gene features, promoter(default: 500bp upstream region before the ATG initiation codon), CDS_RR(default: 100bp downstream region after the initiation codon), Coding Region(Whole Gene coding region). And we defined both promoter and CDS as Regulation Region(RR) in Bacmethy.
  

  difference between CDS_RR and Coding Region
  

  Gene transcription initiation is a complex process regulated by multiple factors. The start of the gene body can also play a role in gene transcription initiation regulation. Hence, the term CDS_RR is used to describe methylation sites that occur at the start of the coding sequence (CDS) region, which is involved in the regulation of gene transcription.
  Some users may be interested specifically in methylation sites that occur within the gene body. For this purpose, Bacmethy provides a separate folder exclusively for these methylation sites, which are classified under the category of Coding Region.
  

methylation site distribution

motif_methylationType.motif.methylation_result.txt

methylated gene result

motif_methylationType.methylation_methylationGene.txt

the overlap of methylation site and TF binding

motif_methylationType.methylationLevel_TF.meme.txt

whole modified base of this motif in this genome

motif_methylationType.methylation
e.g. GATC_m6A_motif.methylation

Explanation

Using Calcute_PPM_console_final.py for generating meme format TFs file

Usage

python ./script/Calcute_PPM_console_final.py  TF.mat

Using Circos_data_prepare.sh for generating circos configuration files and figures

Usage (Run in the folder where the output files were generated)

bash /PATH/TO/Circos_data_prepare.sh PREFIX /PATH/TO/SCRIPT/FOLDER/

Copyright

Copyright © [2024] [Ji-Hong Liu]. All rights reserved.