Description:

SMARTBSSeq 2.2.8

README for SMART
Time-stamp: <2018-01-31 10:35:00 Hongbo Liu>



Introduction
SMART2 is a newly developed tool for deep analysis of DNA methylation data detected by bisulfite sequencing platforms. This tool is focused on four main functions including de novo identification of differentially methylated regions (DMRs) by genome segmentation, identification of DMRs from predefined regions of interest, identification of differentially methylated CpG sites, and genome segmentation. In particular, SMART2 supports Case-Control DMR calling which is useful for disease methylome analsyis. It is known that DNA methylation plays important roles in the regulation of cell development and differentiation. DNA methylation/unmethylation mechanisms are common in all tissue/cell. However, different cell types with the same genome have different methylomes. Recently, high-throughput sequencing combining bisulfite treatment (Bisulfite-Seq) have been used to generate DNA methylomes from a wide range of human tissue/cell types at a genome-wide perspective. In order to de novo identify DMRs across different biological groups, entropy-based procedures facilitated the quantification of methylation specificity for each CpG and the determination of the Euclidean distance and similar entropy for each pair of neighboring CpGs. Subsequently, genome segmentation based on these quantified parameters segments the genome into primary segments comprising CpG sites with high methylation similarities across all groups. Further, the primary segments in close proximity and sharing similar methylation patterns were merged into larger segments of different types, including DMRs and non-DMRs which are identified based on methylation specificity and one-way ANOVA analysis. Eventually, the DMRs with specific hypo-/hypermethylation in the minority of groups, group-specific hypomethylation marks (HypoMarks) and the group-specific hypermethylation marks (HyperMarks), are identified using a statistical method. To facilitate the mining of methylation marks across cell types and species. In addition, SMART2 also supports the identification of DMRs from pre-defined regions of interest and differentially methylated CpG sites.
Detailed information about SMART2 is available at http://fame.edbc.org/smart.


New Features of SMART2

Provides more functions ~~~~~~~~De novo genome segmentation and identification of differentially methylated regions, predefined regions, and CpG sites.
Provides Case-Control DMR calling ~~~~~~~~SMART2 supports Case-Control DMR calling just by inputing Case-Control pairs via a case-control matrix
Provides statistical analysis ~~~~~~~One-way ANOVA analysis was added to identify the DMRs which is more reliable.
Supports various BS-Seq data ~~~~~~SMART2 supports data analysis for WGBS, RRBS, and targeting bisulfite sequencing techniques including TruSeqEPIC, SureSelect and CpGiant.
Supports replicate samples ~~~~~~~~~~SMART2 supports replicates for the same group such as samples from the same clinical group.
Supports missing value replacement ~~~~SMART2 supports the replacement of missing value via the median methylation value of other available samples from the same group.
Be applicable to any species ~~~~~~~~~~~~Re-designed algorithm workflow makes SMART2 is applicable for any species.
Multiprocessing speeds up analysis ~~~~~~~Re-designed algorithm workflow makes SMART2 is more quick for huge data analysis.



Install
$ pip install SMART-BS-Seq
Detailed information can be found in the file ‘INSTALL’ in the distribution.


Usage of SMART2
$ SMART MethylMatrix [-t {DeNovoDMR,DMROI,DMC,Segment}]
[-r REGION_OF_INTEREST] [-c CASE_CONTROL_MATRIX]
[-n PROJECT_NAME] [-o OUTPUT_FOLDER] [-MR MISS_VALUE_REPLACE]
[-AG PERCENTAGE_OF_AVAILABLE_GROUPS]
[-MS METHYLATION_SPECIFICITY] [-ED EUCLIDEAN_DISTANCE]
[-SM SIMILARITY_ENTROPY] [-CD CPG_DISTANCE] [-CN CPG_NUMBER]
[-SL SEGMENT_LENGTH] [-PD P_DMR] [-PM P_METHYLMARK]
[-PC P_DMR_CASECONTROL] [-AD ABSMEANMETHDIFFER] [-v] [-h]
If above command doesn’t work, you can try one of the following solutions:
(1) Add SMART command to system path
Linux$ export PATH=/usr/local/bin:$PATH
MacOS$ export PATH=/Library/Frameworks/Python.framework/Versions/2.7/bin:$PATH
Then rerun SMART command as described above.
(2) Run the source code which can be found in the installation directory
Installation directory of SMART:

Linux (Ubuntu 16.04): /usr/local/lib/python2.7/dist-packages/SMART/
macOS (Sierra 10.12): /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/SMART/

$ cd /usr/local/lib/python2.7/dist-packages/SMART/
$ python SMART.py MethylMatrix [-t {DeNovoDMR,DMROI,DMC,Segment}]
[-r REGION_OF_INTEREST] [-c CASE_CONTROL_MATRIX]
[-n PROJECT_NAME] [-o OUTPUT_FOLDER] [-MR MISS_VALUE_REPLACE]
[-AG PERCENTAGE_OF_AVAILABLE_GROUPS]
[-MS METHYLATION_SPECIFICITY] [-ED EUCLIDEAN_DISTANCE]
[-SM SIMILARITY_ENTROPY] [-CD CPG_DISTANCE] [-CN CPG_NUMBER]
[-SL SEGMENT_LENGTH] [-PD P_DMR] [-PM P_METHYLMARK]
[-PC P_DMR_CASECONTROL] [-AD ABSMEANMETHDIFFER] [-v] [-h]

Positional arguments

MethylMatrix
The input methylation file (such as /Example/MethylMatrix.txt) including methylation values in all samples to compare (REQUIRED). The methylation data should be arranged as a matrix in which each row represents a CpG site. The columns are tab-separated. The column names should be included in the first line, with the first three columns representing the location of CpG sites: chrome, start, end. The methylation values start from the fourth column. And the methylation value should be between 0 (unmethylated) to 1 (fully methylated). The missing values should be shown as -. The names of samples should be given as G1_1,G1_2,G2_1,G2_2,G3_1,G3_2,G3_3, in which Gi represents group i. The Methylation matrix can be build based on bed files (chrome start end betavalue) by bedtools as: bedtools unionbedg -i G1_1.bed G1_2.bed G2_1.bed G2_2.bed G3_1.bed G3_2.bed G3_3.bed -header -names G1_1 G1_2 G2_1 G2_2 G3_1 G3_2 G3_3 -filler - > MethylMatrix.txt. [Type: file]
The example data is also available here.




Optional arguments

-t {DeNovoDMR,DMROI,DMC,Segment}
Type of project including ‘DeNovoDMR’,’DMROI’, ‘DMC’ and ‘Segment’. DeNovoDMR means de novo identification of differentially methylated regions (DMRs) based on genome segmentation. DMROI means the comparison of the methylation difference in regions of interest (ROIs) across multiple groups. DMC means identification of differentially methylated CpG sites (DMCs). It should be noted DMC is time-consuming for whole-renome methylation data. Segment means de novo segmentation of genome based on DNA methylation in all samples [Type: string] [DEFAULT: ‘DeNovoDMR’]

-r Region_of_interest
Genome regions of interest in bed format without column names (such as /Example/Regions_of_interest.bed) for project type DMROI (REQUIRED only for DMROI). The regions in the file should be sorted by chromosome and then by start position (e.g., sort -k1,1 -k2,2n in.bed > in.sorted.bed). If this file is provided, SMART treat each region as a unit and compare its mean methylation across groups by methylation specificity and ANOVA analysis. This parameter is only for project type DMROI. DEFAULT: ‘’ [Type: string]

-c Case_control_matrix
Case-control matrix in txt format without column names (such as /Example/Case_control_matrix.txt) for project types DeNovoDMR and DMROI. In this file, each row represents a pairwise comparison between a case group (first column) and a control group (second column). Two columns should be tab-separated. If this file is provided, SMART will carry out DMR calling for each pairwise based on two sample t test. The results would be output to 7_DMR_Case_control.txt (DeNovoDMR) or 7_DMROI_Case_control.txt (DMROI) treat each region as a unit and compare its mean methylation across groups by methylation specificity and ANOVA analysis. This parameter is only for project types DeNovoDMR and DMROI. DEFAULT: ‘’ [Type: string]

-n Project_Name
Project name, which will be used to generate output file names. This parameter is for all project types. DEFAULT: “SMART” [Type: string]

-o Output_Folder
The folder in which the result will be output. If specified all output files will be written to that directory. This parameter is for all project types. [Type: folder] [DEFAULT: the directory named using project name and current time (such as SMART20140801132559) in the current working directory]

-MR Miss_Value_Replace
Replace the missing value with the mediate methylation value of available samples in the corresponding group. The user can control whether to replace missing value by setting this parameter from 0.01 (meaning methylation values are available in at least 1% of samples in a group) to 1.0 (meaning methylation values are available in 100% of samples in a group, i.e there is no missing values). This parameter is for all project types. [Type: float] [Range: 0.01 ~ 1.0] [DEFAULT: 0.5]

-AG Percentage_of_Available_Groups
Percentage of available groups after missing value replacement. The user can use this parameter to filter CpG sites those have not enough available methylation levels by setting this parameter from 0.01 (meaning methylation values are available in at least 1% of groups) to 1.0 (meaning methylation values are available in 100% of groups, i.e there is no missing values). This parameter is for all project types. [Type: float] [Range: 0.01 ~ 1.0] [DEFAULT: 1.0]

-MS Methylation_Specificity
Methylation Specificity Threshold for DMC or DMR calling. This parameter can be used to identify DMC or DMR as the CpG site or region with methylation specificity which is greater than the threshold. This parameter is for all project types. [Type: float] [Range: 0.2 ~ 1.0] [DEFAULT: 0.5]

-ED Euclidean_Distance
Euclidean Distance Threshold for methylation similarity between neighboring CpGs which is used in genome segmentation and de novo identification of DMR. The methylation similarity between neighboring CpGs is high if the Euclidean distance is less than the threshold. This parameter is only for project types DeNovoDMR and Segment. [Type: float] [Range: 0.01 ~ 0.5] [DEFAULT: 0.2]

-SM Similarity_Entropy
Similarity Entropy Threshold for methylation similarity between neighboring CpGs which is used in genome segmentation and de novo identification of DMR. The methylation similarity between neighboring CpGs is high if similarity entropy is less than the threshold. This parameter is only for project types DeNovoDMR and Segment. [Type: float] [Range: 0.01 ~ 1.0] [DEFAULT: 0.6]

-CD CpG_Distance
CpG Distance Threshold for the maximal distance between neighboring CpGs which is used in genome segmentation and de novo identification of DMR. The neighboring CpGs will be merged if the distance less than this threshold. This parameter is only for project types DeNovoDMR and Segment. [Type: int] [Range: 1 ~ 2000] [DEFAULT: 500]

-CN CpG_Number
Segment CpG Number Threshold for the minimal number of CpGs of merged segment and de novo identified DMR. The segments/DMRs with CpG number larger than this threshold will be output for further analysis. This parameter is only for project types DeNovoDMR and Segment. [Type: int] [Range: > 1] [DEFAULT: 5]

-SL Segment_Length
Segment Length Threshold for the minimal length of merged segment and de novo identified DMR. The segments/DMRs with a length larger than this threshold will be output for further analysis. This parameter is only for project types DeNovoDMR and Segment. [Type: int] [Range: > 1] [DEFAULT: 20]

-PD p_DMR
p value of one-way analysis of variance (ANOVA) which is carried out for identification of or DMCs or DMRs across multiple groups. The segments with p value less than this threshold are identified as DMC or DMR. This parameter is for all project types. [Type: float] [Range: 0 ~ 1] [DEFAULT: 0.05]

-PM p_MethylMark
p value of one sample t-test which is carried out for identification of Methylation mark in a specific group based on the identified DMRs. The DMRs with p value less than this threshold is identified as group- specific methylation mark (Hyper methylation mark or Hypo methylation mark). This parameter is only for project types DeNovoDMR and DMROI [Type: float] [Range: 0 ~ 1] [DEFAULT: 0.05]

-PC p_DMR_CaseControl
p value of two-sample t-test which is carried out for identification of DMRs between case group and control group. The region with p value less than this threshold is identified DMR. This parameter is only for project types DeNovoDMR and DMROI. [Type: float] [Range: 0 ~ 1] [DEFAULT: 0.05]

-AD AbsMeanMethDiffer
Absolute mean methylation difference between case group and control group. The region with absolute mean methylation difference more than this threshold is identified DMR. The DMR showing hyper methylation in case group is identified as Hyper mark and that showing hypo methylation in case group is identified as Hypo mark. This parameter is only for project types DeNovoDMR and DMROI. [Type: float] [Range: 0 ~ 1] [DEFAULT: 0.3]

-v, –version
Show program’s version number and exit

-h, –help
Show this help message and exit





Example

Example data
The example data can be found in the directory Example under the installation directory of SMART, and is also available here. In this example, 10,000 CpG sites in each of human chromosomes were extracted for the test of SMART. The user can use the following command to test SMART.
It should be noted that the location of installation directory of SMART may be different in different Operating System.

Linux (Ubuntu 16.04): /usr/local/lib/python2.7/dist-packages/SMART/
macOS (Sierra 10.12): /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/SMART/



Example command
Linux$ cd /usr/local/lib/python2.7/dist-packages/SMART/
macOS$ cd /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/SMART/
$ SMART ./Example/MethylMatrix_Test.txt -t DeNovoDMR -o ./Example/
$ SMART ./Example/MethylMatrix_Test.txt -t DeNovoDMR -c ./Example/Case_control_matrix.txt -o ./Example/
$ SMART ./Example/MethylMatrix_Test.txt -t DMROI -r ./Example/CpGisland_hg19.bed -o ./Example/
$ SMART ./Example/MethylMatrix_Test.txt -t DMROI -r ./Example/CpGisland_hg19.bed -c ./Example/Case_control_matrix.txt -o ./Example/
$ SMART ./Example/MethylMatrix_Test.txt -t DMC -o ./Example/
$ SMART ./Example/MethylMatrix_Test.txt -t Segment -o ./Example/



Output Files
The results for DeNovoDMR are given in the folder DeNovoDMR Folder including:

1_AvailableCpGs.txt ~ CpG sites with available methylation in all groups
2_MergedSegment.bed ~ Merged segments based on small segments for visualization in UCSC browser
3_MergedSegment_Methylation.txt ~ Merged segments with DNA methylation in samples and groups
4_DMR_Methylation.txt ~ DMRs identified by SMART
5_DMR_Group_Specificity.txt ~ Group specificity of DMRs
6_DMR_Methylmark.txt ~ Group specific methylation marks
7_DMR_Case_control.txt ~ DMRs between Case group and Control group
Summary.txt ~ Summary of SMART analysis

The results for DMROI are given in the folder DMROI Folder including:

1_AvailableCpGs.txt ~ CpG sites with available methylation in all groups
2_ROI.bed ~ ROIs in bed format for visualization in UCSC browser
3_ROI_Methylation.txt ~ ROIs and their methylation levels in samples and groups
4_DMROI_Methylation.txt ~ Differentially methylated ROIs with methylation values
5_DMROI_Group_Specificity.txt ~ Differentially methylated ROIs with group specificity
6_DMROI_Methylmark.txt ~ Group specific methylation marks of DifferMethlROIs
7_DMROI_Case_control.txt ~ DMROIs between Case group and Control group
Summary.txt ~ Summary of SMART analysis

The results for DMC are given in the folder DifferMethlCpG Folder including:

1_AvailableCpGs.txt ~ CpG sites with available methylation in all groups
2_DifferMethlCpGs.txt ~ Differentially methylated CpG sites
Summary.txt ~ Summary of SMART analysis

The results for Segment are given in the folder Segment Folder including:

1_AvailableCpGs.txt ~ CpG sites with available methylation in all groups
2_MergedSegment.bed ~ Merged segments based on small segments for visualization in UCSC browser
3_MergedSegment_Methylation_Sample.txt ~ Merged segments with DNA methylation in samples
4_MergedSegment_Methylation_Group.txt ~ Merged segments with DNA methylation in groups
Summary.txt ~ Summary of SMART analysis



Other useful links

SMART:
http://fame.edbc.org/smart/

Forum:
https://groups.google.com/forum/#!forum/smart-announcement

QDMR:
http://fame.edbc.org/qdmr/




Citation
Hongbo Liu et al. Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes. Nucleic Acids Res: 2016 ,44(1),75-94.


Contact

For any help:
you are welcome to write to Hongbo Liu (hongbo919@gmail.com) at http://cce.edbc.org/members/HongboLiu.html.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

Share

• Share on Facebook
• Share on Twitter