RNA-Seq、ATAC-Seq、单细胞分析等
1.ATAC-seq技术及数据分析
Buenrostro, J., Giresi, P., Zaba, L. et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10, 1213–1218 (2013).
https://doi.org/10.1038/nmeth.2688
2.RNA-seq和 ChIP-seq 整合分析
Hierarchical Mechanisms for Direct Reprogramming of Fibroblasts to Neurons. Cell, 2013
https://www.sciencedirect.com/science/article/pii/S0092867413011653
3.单细胞 RNA-seq 分析
Seurat 3.0：
Comprehensive Integration of Single-Cell Data. Cell, 2019
https://www.sciencedirect.com/science/article/pii/S0092867419305598
Monocle：
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32, 381–386 (2014).
https://www.nature.com/articles/nbt.2859
4.单细胞 ATAC-seq 分析
“APEC: an accesson-based method for single-cell chromatin accessibility analysis”, Genome Biology, 2020.
https://link.springer.com/article/10.1186/s13059-020-02034-y
5.单细胞空间转录组分析
Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33, 495–502 (2015).
https://doi.org/10.1038/nbt.3192
基因网络与蛋白互作网络
1.分析 MicroArray、RNA-Seq、或单细胞转录组数据，利用普通相关系数、WGCNA、GENIE3、SCENIC、或者其它方法构建基因表达调控网络。
普通相关系数：
Mentzen, W.I. and E.S. Wurtele, Regulon organization of Arabidopsis. BMC Plant Biol, 2008. 8: p. 99. WGCNA:
https://link.springer.com/article/10.1186/1471-2229-8-99
Amiri, A., et al., Transcriptome and epigenome landscape of human cortical development modeled in organoids. Science, 2018. 362(6420).
https://science.sciencemag.org/content/362/6420/eaat6720
Shinozaki, Y., et al., High-resolution spatiotemporal transcriptome mapping of tomato fruit development and ripening. Nat Commun, 2018. 9(1): p. 364.
https://www.nature.com/articles/s41467-017-02782-9
Genie3:
Zhou, P., et al., Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions. The Plant Cell, 2020. 32(5): p. 1377.
https://academic.oup.com/plcell/article/32/5/1377/6118116
SCENIC:
Aibar, S., et al., SCENIC: single-cell regulatory network inference and clustering. Nat Methods, 2017.
https://www.nature.com/articles/nmeth.4463
14(11): p. 1083-1086.
Davie, K., et al., A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain. Cell, 2018. 174(4): p. 982- 998.e20.
https://www.sciencedirect.com/science/article/pii/S0092867418307207
Suo, S., et al., Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas. Cell Reports, 2018.
25(6): p. 1436-1445.e3.
https://www.sciencedirect.com/science/article/pii/S2211124718316346
2.分析 ChIP-Seq 原始数据，鉴定转录因子在基因组上的结合位点，推导它们的结合模序(motif)，构建转录调控网络。
Song, L., et al., A transcription factor hierarchy defines an environmental stress response network. Science, 2016. 354(6312).
https://science.sciencemag.org/content/354/6312/aag1550
Lau, O.S., et al., Direct roles of SPEECHLESS in the specification of stomatal self-renewing cells. Science, 2014. 345(6204): p. 1605-9.
https://science.sciencemag.org/content/345/6204/1605
3.分析大规模蛋白互作网络，并将其应用于疾病研究等不同方面。
Li, J., et al., Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol Syst Biol, 2014. 10: p. 774.
https://www.embopress.org/doi/full/10.15252/msb.20145487