Anim Biosci > Volume 34(8); 2021 > Article |
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Items | Microarray [99] | RNA-seq [56] |
---|---|---|
Principle | Hybridization | High-throughput sequencing |
Resolution | Several to 100 bps | Single base |
Reference genome required | Only knowledge about the microarray | The species or closely related species |
Different isoform | Limited | Yes |
Discover new transcript | Limited | Yes |
Non-coding RNA | Limited | Yes |
Attributes | Affinity enrichment-based | Restriction enzyme-based | Bisulfite conversion |
---|---|---|---|
Assays | MeDIP-seq [81], MBD-seq [70] | HELP-seq [64], MRE-seq [65] | WGBS [46], RRBS [67] |
Resolution | Approximately 150 bp | Single base | Single base |
Regions covered | Approximately 23 million CpGs | Approximately 2 million CpGs | >28 million CpGs (WGBS) approximately 2 million CpGs (85% of CpG islands and 60% of promoters; RRBS) |
Advantages |
Allows for rapid and specific assessment of the average methylation levels of large DNA regions, No mutation introduced, Cost-effective |
High sensitivity with lower costs, Simple approach, Cost-effective |
Evaluates methylation status of every CpG site |
Limitations |
Limited by the quality and specificity of the antibody or protein, Bias into hyper-methylated regions, Unpredictable absolute methylation level, No information on separate CpG dinucleotides |
Restricted to restriction enzyme-digestion sites, Requires large amount, high purity, and integrity of DNA |
High cost, High DNA input, DNA damage after bisulfite conversion |
MeDIP-seq, methylated DNA immunoprecipitation and sequencing; MBD-seq, methylated-CpG-binding protein sequencing; HELP-seq, HpaII tiny fragments Enrichment by Ligation-mediated PCR; MRE-seq, methylation-sensitive restriction enzyme; WGBS, whole-genome bisulfite sequencing; RRBS, reduced-representation bisulfite sequencing; CpGs, cytosine phosphate guanine.
Integration method | Analysis method | Characteristics | Elements | Reference |
---|---|---|---|---|
Statistical-based | Correlation | Simplicity and intuitiveness | Pearson, Spearman | [100] |
Clustering using data set connection | Distinguish clear and unique groups | Hierarchical, K-means, random forests | [101] | |
Highly dependent on the size between data sets | ||||
Multivariate | Powerfully applied in a metadata analysis | PCA, PLS | [102] | |
Predict various aspects or trends of a data set | ||||
Function-based | Reference database | Complex connections between various types of molecular elements | KEGG, GO, Reactome | [103] |
Differences exist in different species | ||||
Networking | Provides critical clusters, modules, and hubs | GCN, WGCNA | [104] | |
Complex connections between various types of molecular elements |