CMB_2025v15n2

Computational Molecular Biology 2025, Vol.15, No.2, 91-101 http://bioscipublisher.com/index.php/cmb 10 0 Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Aryamanesh N., 2022, A reproducible and dynamic workflow for analysis and annotation of scRNA-seq data, In: Epiblast stem cells: methods and protocols, New York, NY: Springer US, pp.101-140. https://doi.org/10.1007/978-1-0716-2281-0_10 Babcock B.R., and Weir S., 2023, scRNA-seq for microcephaly research [I]: single-cell droplet encapsulation, mRNA capture, and cDNA synthesis, In: Microcephaly: methods and protocols, New York, NY: Springer US, pp.83-97. https://doi.org/10.1007/978-1-0716-2752-5_8 Balsalobre A., and Drouin J., 2022, Pioneer factors as master regulators of the epigenome and cell fate, Nature Reviews Molecular Cell Biology, 23(7): 449-464. https://doi.org/10.1038/s41580-022-00464-z Cai L., Ma X., and Ma J., 2025, Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks, Briefings in Bioinformatics, 26(1): bbae711. https://doi.org/10.1093/bib/bbae711 Cao J., Chen X., Huang S., Shi W., Fan Q., Gong Y., Peng Y., Wu L., and Yang C., 2022, Microfluidics-based single cell analysis: from transcriptomics to spatiotemporal multi-omics, TrAC Trends in Analytical Chemistry, 158: 116868. https://doi.org/10.1016/j.trac.2022.116868 David F.P.A., Litovchenko M., Deplancke B., and Gardeux V., 2020, ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses, Nucleic Acids Research, 48(W1): W403-W414. https://doi.org/10.1093/nar/gkaa412 Durmaz A., and Scott J., 2022, Stability of scRNA-seq analysis workflows is susceptible to preprocessing and is mitigated by regularized or supervised approaches, Evolutionary Bioinformatics, 18: 11769343221123050. https://doi.org/10.1177/11769343221123050 Fan T., and Huang Y., 2021, Accessible chromatin reveals regulatory mechanisms underlying cell fate decisions during early embryogenesis, Scientific Reports, 11(1): 7896. https://doi.org/10.1038/s41598-021-86919-3 Felce C., Gorin G., and Pachter L., 2024, A biophysical model for ATAC-seq data analysis, bioRxiv, 2024: 577262. https://doi.org/10.1101/2024.01.25.577262 Finkbeiner C.R., Ortuño-Lizarán I., Sridhar A., Hooper M., Petter S., and Reh T.A., 2022, Single-cell ATAC-seq of fetal human retina and stem-cell-derived retinal organoids shows changing chromatin landscapes during cell fate acquisition, Cell Reports, 38(4): 110294. https://doi.org/10.1016/j.celrep.2021.110294 Hamrud E., Leese J., Thiery A.P., Buzzi A.L., Vigilante A., Briscoe J., and Streit A., 2025, A single cell ATAC-seq atlas uncovers dynamic changes in chromatin accessibility during cell fate specification at the neural plate border, bioRxiv, 2025: 652017. https://doi.org/10.1101/2025.05.03.652017 Hanamsagar R., Marcus R., Chamberlain M., de Rinaldis E., and Savova V., 2019, Optimum processing conditions for single cell RNA sequencing on frozen human PBMCs, The Journal of Immunology, 202(1_Supplement): 131.15. https://doi.org/10.4049/jimmunol.202.supp.131.15 Khan S.U., Huang Y., Ali H., Ali I., Ahmad S., Khan S., Hussain T., Ullah M., and Lu K., 2023, Single-cell RNA sequencing (scRNA-seq): advances and challenges for cardiovascular diseases, Current Problems in Cardiology, 49(2): 102202. https://doi.org/10.1016/j.cpcardiol.2023.102202 Kim E.D., Dorrity M.W., Fitzgerald B.A., Seo H., Sepuru K.M., Queitsch C., Mitsuda N., Han S., and Torii K.U., 2022, Dynamic chromatin accessibility deploys heterotypic cis/trans-acting factors driving stomatal cell-fate commitment, Nature Plants, 8(12): 1453-1466. https://doi.org/10.1038/s41477-022-01304-w Kumar R., and Sharma A., 2021, Transcription factor stoichiometry in cell fate determination, Journal of Genetics, 100(2): 27. https://doi.org/10.1007/s12041-021-01278-2 Larcombe M., Hsu S., Polo J., and Knaupp A.S., 2022, Indirect mechanisms of transcription factor-mediated gene regulation during cell fate changes, Advanced Genetics, 3(4): 2200015. https://doi.org/10.1002/ggn2.202200015 Lee M., Kaestner K., and Li M., 2023, Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data, Genome Biology, 24(1): 244. https://doi.org/10.1101/2023.02.01.526609 Leslie C., 2020, Chromatin state and scRNA-seq analysis defines a common differentiation trajectory towards T-cell exhaustion, In: Proceedings of the AACR special conference on tumor immunology and immunotherapy, Cancer Immunology Research, 8(3 Suppl): IA20. https://doi.org/10.1158/2326-6074.TUMIMM19-IA20

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