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SMART-BS-Seq 1.4.2.20150517

Specific Methylation Analysis and Report Tool for BS-Seq data

Time-stamp: <2015-05-17 11:28:37 Hongbo Liu>

Introduction

It is known that DNA methylation plays important roles in 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. To characterize the genome regions that consist of continuous CpGs with similar methylation specificity, we developed the Specific Methylation Analysis and Report Tool (SMART) based on the quantified methylation specificity, Euclidean distance and similarity entropy, for identifying and characterizing sets of genome segments comprising continuous CpGs with similar methylation specificities. For a given set of multiple methylomes profiled using BS-Seq, 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, continuous scanning based on these quantified parameters segments the genome into primary segments comprising CpG sites with high methylation similarities across all cell types. Further, the primary segments in close proximity and sharing similar methylation patterns were merged into larger segments of different types, including high specificity (HighSpe), low specificity (LowSpe) and almost no cell-specificity (NoSpe) segments. Eventually, the High/LowSpe segments with specific hypo-/hypermethylation in the minority of cell types, cell-type-specific hypomethylation marks (HypoMarks) and cell-type-specific hypermethylation marks (HyperMarks), were identified using a statistical method. To facilitate the mining of methylation marks (MethyMarks) across cell types and species, all algorithms used in this procedure were integrated into a Specific Methylation Analysis and Report Tool (SMART), which is also available at http://fame.edbc.org/smart.

Install

Please check the file ‘INSTALL’ in the distribution.

Usage of SMART

usage:SMART MethyDir CytosineDir [-h] [-n PROJECTNAME] [-o OUTPUTFOLDER] [-v]

positional arguments

MethyDir

The directory (such as /liuhb/BSSeq/) of the folder including methylation data files formated in wig.gz (such as H1.wig.gz). REQUIRED.

CytosineDir

The directory (such as /liuhb/CLoc_hg19/) of the folder including cytosine location files for all chromesomes formated in txt.gz (such as chr1.txt.gz). REQUIRED.

optional arguments

-h, –help

show this help message and exit

-n PROJECTNAME

Project name, which will be used to generate output file names. DEFAULT: “SMART”

-o OUTPUTFOLDER

If specified all output files will be written to that directory. Default: the directory named using projectname and currenttime (such as SMART20140801132559) in the current working directory.

-v, –version

show program’s version number and exit

Example

Example data

The example data can be found in the directory Example under the installation directory of SMART. It should be noted that the location of installation directory of SMART may be different in different Operating System. The Cytosines and their methylation level in 50kb regions from chr3 and chr6 were extracted for test of SMART. User can use following command to test SMART.

Example command

For Linux:

The main function SMART may be in /usr/local/bin/, and example data may be in ../python2.7/dist-packages/SMART/Example. The following referece may be useful for test of SMART:

SMART /usr/local/lib/python2.7/dist-packages/SMART/Example/BSSeq_fortest/ /usr/local/lib/python2.7/dist-packages/SMART/Example/CLoc_hg19_fortest/ -n Test -o /usr/local/lib/python2.7/dist-packages/SMART/Example/Example_Results/
For windows:

The main function SMART may be in ..\Python27\Scripts\, and example data may be in ..\Python27\Lib\site-packages\SMART\Example. The following referece may be useful for test of SMART:

cd  ..\Python27\Scripts\
python SMART ..\Python27\Lib\site-packages\SMART\Example\BSSeq_fortest\ ..\Python27\Lib\site-packages\SMART\Example\CLoc_hg19_fortest\ -n Test -o ..\Python27\Lib\site-packages\SMART\Example\Example_Results\

Output Files

  1. Folder SplitedMethy is a a output directory to store the splited Methylation data. The methylation data are stored in different chromosome sub-folders. In each sub-folder, the methylation data for all samples are included.

  2. Folder MethylationSpecificity is a output directory to store the methylation levels and specifity for each C which is common across all samples. These files are stored in chromosomes. In this folder, MethylationSpecificity.wig.gz includes the methylation specifity of all common C. And this file can be uploaded to UCSC browser for visualization.

  3. Folder MethylationSegment includes three sub-folders: GenomeSegment, GenomeSegmentMethy, and MergedGenomeSegment. The sub-folder GenomeSegment stores all small segments identified by SMART in each chromosome. And the sub-folder GenomeSegmentMethy stores the methylation levels of each small segments across all samples which may be useful for users’ local further analysis. The sub-folder MergedGenomeSegment stores the larger segments merged based on the small segments in each chromosome. The final results are generated based on these merged segments.

  4. Folder FinalResults includes all intresting results which may be concerned by users. In this folder, there are six files.

    -The first file 1SmallSegmentBed.txt.gz stores all small segments in bed format, which can be uploaded to UCSC browser for visualization.

    -The second file 2MergedSegmentBed.txt.gz stores all merged segments in bed format, which can be uploaded to UCSC browser for visualization.

    -The third file 3MergedSegment.txt stores all merged segments in txt format, which is useful for local further analysis.

    -The fourth file 4MergedSegmentwithmethylation.txt stores the methylation levels of all merged segments across all samples, which is useful for local further analysis.

    -The fifth file 5MergedHighLowSpeSegmentwithspecificity.txt stores the methylation specificity and p values of t-test for each merged HighSpe/LowSpe segement, which is useful for further analysis on cell-type-specificity for each HighSpe/LowSpe segement. The positive p value represents the segment is hyper-methylated in the corresbonding cell-type, while the negative p value represents the segment is hypo-methylated in the corresbonding cell-type.

    -The sixth file 6CellTypeSpecificMethymarkPvalue.txt is a reformated file for the fifth file. In this file, only the HighSpe/LowSpe segements which show significant hypo- or hyper-methylation in some cell-types are remained. This file is usefull for users to select and analyze cell-type-specific methylation marks including HypoMarks and HyperMarks.

Contact

For any help:you are welcome to write to Hongbo Liu (hongbo919@gmail.com) at http://cce.edbc.org/members/HongboLiu.html.
 
File Type Py Version Uploaded on Size
SMART-BS-Seq-1.4.2.20150517.linux-x86_64.tar.gz (md5)
built for Linux-3.8.0-29-generic-x86_64-with-glibc2.4
"dumb" binary any 2015-05-17 293KB
SMART-BS-Seq-1.4.2.20150517.tar.gz (md5) Source 2015-05-17 274KB
SMART-BS-Seq-1.4.2.20150517.win-amd64.exe (md5) MS Windows installer any 2015-05-17 519KB
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