SMART specifications


Unique identifier OMICS_19748
Alternative name Specific Methylation Analysis and Report Tool
Software type Package/Module
Interface Command line interface
Restrictions to use Academic or non-commercial use
Input data A methylation data matrix.
Input format TXT
Output data DMR and related information.
Output format TXT
Operating system Unix/Linux, Mac OS
Programming languages Python
License GNU General Public License version 2.0
Computer skills Advanced
Version 2.2.1
Stability Stable
Maintained Yes



Version Operating System DOI Size Download Info
32/64 bits
10.24347/OMIC_19748_2.2.1/lm8664 1.74 Mo get_app
32/64 bits
10.24347/OMIC_19748_2.2.1/lm8664 865.84 Ko get_app


SMART article

SMART institution(s)
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China; Department of Rehabilitation, the First Affiliated Hospital of Harbin Medical University, Harbin, China; School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, China
SMART funding source(s)
Supported by National Natural Science Foundation of China [61403112, 31371334, 81573021, 61402139 and 31371478]; Natural Scientific Research Fund of Heilongjiang Provincial [ZD2015003].

SMART review

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Hongbo Liu

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SMART2 is a newly developed tool for deep analysis of DNA methylation data detected by bisulfite sequencing platforms. This tool is focused on three main functions including de novo identification of differentially methylated regions (DMRs) (wikipedia) by genome segmentation, identification of DMRs from predefined regions of interest, and identification of differentially methylated CpG sites. 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.