README file created by Yu He on Oct 18, 2019
Please contact [email protected] with questions or for clarifications.
This folder "DeepHM" contains the code for running DeepH&M in the server. Download the code, which will create DeepHM folder .
a. After downloading the code in the folder, download mm9 datasets (for processing genomic features) and sample datasets (MeDIP-seq, MRE-seq, hmC-Seal, TAB-seq and WGBS) and MethPipe tool at the link http://wangftp.wustl.edu/~yhe/DeepHM_datasets/dataset.tar.gz. Uncompress the compressed .tar.gz file in DeepHM folder using command "tar -xzvf dataset.tar.gz". This will generate three folders: mm9, data, MethPipe. Folder mm9 lists files for processing genomic features for mm9. Folder data lists sample data for MeDIP-seq, MRE-seq, hmC-Seal, TAB-seq and WGBS data. Folder MethPipe lists the tool for running mlml. Below list detailed descriptions for files in folder mm9 and data.
1. folder mm9. The default species is mm9. If doing training for a different species, you need to provide following files for that species.
cpgIsland.bed: cpg island coordinates. Each column lists the coordinate for a cpg island (chr, start, end)
hmc_interval: hmc intervals used for dividing cpgs into multiple windows for data balance
genomeSeq.fa: mm9 genome sequence
cpg_no_chrM: cpg coordinates (no ChrM). Each column lists the coordinate for a cpg (chr, start, end)
gc5Base.sort.bedGraph: gc percent. Each column lists the coordinate and gc perecent for the interval (chr, start, end, gc_percent)
chrom_sizes: chromosome size. Each column lists chromosome name and chromosome size
ACGT_sites.bed: coordinates for ACGT restriction sites
GCGC_sites.bed: coordinates for GCGC restriction sites
CCGC_sites.bed: coordinates for CCGC restriction sites
CCGG_sites.bed: coordinates for CCGG restriction sites
2. folder data. All files are sorted.
hmC-Seal.bedGraph: bedGraph file for hmc-seal data. Each colum lists the coordinate and number of reads overlapping the interval
MeDIP.bedGraph: bedGraph file for medip-seq data
MRE.bed: bed file for mre-seq data. Each column is a read
TAB_data: tab-seq data. Each colum lists coordinates, 5hmC level, coverage
WGBS_data: wgbs data. Each colum lists coordinates, total methylation level, coverage
b. Dependencies for DeepHM include TensorFlow 1.0 (using GPU server), BedTools, bash, python3 and R. Please make sure these are installed and working before trying to run DeepHM.
c. After finishing the setup, follow the steps below to run DeepH&M.
Run following scripts in DeepHM folder by first setting
export WD=$(pwd)
which set $WD a global variable refering to DeepHM directory.
1: process genomic features for mm9 or others
Script: bash process_genome.sh mm9_folder genome_process_folder
Inputs:
mm9_folder: genome folder which stores mm9 data
genome_process_folder: output folder where processed mm9 data will be stored
2: process tab-seq, wgbs, medip,hmcSeal and mre data.
Script: bash process_methylation_data.sh mm9_folder data_folder data_process_folder genome_process_folder
Inputs:
mm9_folder: genome folder which stores mm9 data
data_folder: data folder which stores tab-seq, wgbs, medip,hmcSeal and mre data
data_process_folder: output folder where processed data will be stored
genome_process_folder: processed genome folder which stores output data from step 1
3: select high coverage data, normalize and balance data .
Script: bash tune_data.sh data_process_folder data_tune_folder mm9_folder tab_coverage wgbs_coverage
Inputs:
data_process_folder: processed data folder which stores output data from step 2
data_tune_folder: output folder where tuned data will be stored
mm9_folder: genome folder which stores mm9 data
tab_coverage: coverage cutoff for picking high cov tab-seq data
wgbs_coverage: coverage cutoff for picking high cov wgbs-seq data
4: train cpg and dna model .
Script: bash train_cpg_dna_model.sh data_tune_folder train_folder genome_process_folder
Inputs:
data_tune_folder: tuned data folder which stores output data from step 3
train_folder: output folder where training data will be stored
genome_process_folder: processed genome folder which stores output data from step 1
Outputs:
The best cpg_model and dna_model are printed in the end and required for step 5 and step 6 later.
5: train joint model .
Script: bash train_joint_model.sh train_folder cpg_model dna_model genome_process_folder
Inputs:
train_folder: training data folder which stores output data from step 4
cpg_model: trained cpg model from step 4 output
dna_model: trained dna model from step 4 output
genome_process_folder: processed genome folder which stores output data from step 1
Outputs:
The best joint_model are printed in the end and required for step 6 later.
6: predict from cpg, dna and joint model . For predicting on current dataset, use following script.
Script: bash predict_model.sh train_folder cpg_model dna_model joint_model pred_folder data_process_folder data_tune_folder mm9_folder genome_process_folder
Inputs:
train_folder: training data folder which stores output data from step 4
cpg_model: trained cpg model from step 4 output
dna_model: trained cpg model from step 4 output
joint_model: trained cpg model from step 5 output
pred_folder: output folder where prediction data will be stored
data_process_folder: processed data folder which stores output data from step 2
data_tune_folder: tuned data folder which stores output data from step 3
mm9_folder: genome folder which stores mm9 data
genome_process_folder: processed genome folder which stores output data from step 1
Outputs:
all_coord_pred_mc_hmc_final: predicted 5mc and 5hmc levels. Each column is chr, start, end, predicted 5mc, predicted 5hmc
7: predict from cpg, dna and joint model . For predicting on new dataset, use following scripts.
Scripts:
1).process data for new dataset. Input variables are same as above.
bash process_methylation_data.sh mm9_folder data_folder data_process_folder genome_process_folder
2).predict data for new dataset using trained model. Input variables are same as above.
bash predict_model.sh train_folder cpg_model dna_model joint_model pred_folder data_process_folder data_tune_folder mm9_folder genome_process_folder
d. An example to train DeepHM model using provided mm9 and data folder:
bash process_genome.sh mm9 genome_process_folder
bash process_methylation_data.sh mm9 data data_process_folder genome_process_folder
bash tune_data.sh data_process_folder data_tune_folder mm9 25 20
bash train_cpg_dna_model.sh data_tune_folder train_folder genome_process_folder
bash train_joint_model.sh train_folder cpg_model-epoch20 dna_model-epoch30 genome_process_folder
bash predict_model.sh train_folder cpg_model-epoch20 dna_model-epoch30 joint_model-epoch40 pred_folder data_process_folder data_tune_folder mm9 genome_process_folder
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