La, Y.F., He, X.Y., Zhang, L.P., et al., Comprehensive analysis of differentially expressed profiles of mRNA, lncRNA, and circRNA in the uterus of seasonal reproduction sheep, Genes (Basel), 2020, vol. 11, no. 3, р. 301. https://doi.org/10.3390/genes11030301
Article CAS PubMed PubMed Central Google Scholar
Sun, L., Zhang, P.J. and Lu, W.F., LncRNA MALAT1 regulates mouse granulosa cell apoptosis and 17β-estradiol synthesis via regulating miR-205/CREB1 axis, Biomed. Res. Int., 2021, р. 6671814. https://doi.org/10.1155/2021/6671814
Li, T., Hu, D., and Gong, Y.H., Identification of potential lncRNAs and co-expressed mRNAs in gestational diabetes mellitus by RNA sequencing, J. Matern.-Fetal Neonat. Med., 2021.https://doi.org/10.1080/14767058.2021.1875432
Su, T., Yu, H.L., Luo, G., et al., The interaction of lncRNA XLOC-2222497, AKR1C1, and progesterone in porcine endometrium and pregnancy, Int. J. Mol. Sci., 2020, vol. 21, no. 9, р. 3232. https://doi.org/10.3390/ijms21093232
Article CAS PubMed PubMed Central Google Scholar
Qi, M.R., Yu, B.X., Yu, H.Y., et al., Integrated analysis of a ceRNA network reveals potential prognostic lncRNAs in gastric cancer, Cancer Med., 2020, vol. 9, no. 5, pp. 1798—1817.
Article CAS PubMed PubMed Central Google Scholar
Shen, X.J., Xue, Y.J., Cong, H., et al., Circulating lncRNA DANCR as a potential auxiliary biomarker for the diagnosis and prognostic prediction of colorectal cancer, Biosci. Rep., 2020, vol. 40, no. 3, р. BSR20191481. https://doi.org/10.1042/BSR20191481
Article CAS PubMed PubMed Central Google Scholar
Wang, J.D., Zhou, H.S., Tu, X.X., et al., Prediction of competing endogenous RNA coexpression network as prognostic markers in AML, Aging, 2019, vol. 11, no. 10, pp. 3333—3347.
Article CAS PubMed PubMed Central Google Scholar
Feng, X., Li, F.Z., Wang, F., et al., Genome-wide differential expression profiling of mRNAs and lncRNAs associated with prolificacy in Hu sheep, Biosci. Rep., 2018, vol. 38, no. 2, р. BSR20171350.https://doi.org/10.1042/BSR20171350
Article CAS PubMed PubMed Central Google Scholar
La, Y.F., Tang, J.S., He, X.Y., et al., Identification and characterization of mRNAs and lncRNAs in the uterus of polytocous and monotocous Small Tail Han sheep (Ovis aries), Peer J., 2019, vol. 7, р. e6938. https://doi.org/10.7717/peerj.6938
Article CAS PubMed PubMed Central Google Scholar
Ling, Y.H., Xu, L.N., Zhu, L., et al., Identification and analysis of differentially expressed long non-coding RNAs between multiparous and uniparous goat (Capra hircus) ovaries, PLoS One, 2017, vol. 12, no. 9, р. e0183163. https://doi.org/10.1371/journal.pone.0183163
Article CAS PubMed PubMed Central Google Scholar
Chang, W.H., Cui, Z.L., and Wang, J.H., Identification of potential disease biomarkers in the ovaries of Dolang sheep from Xinjiang using transcriptomics and bioinformatics approaches, Indian J. Anim. Res., 2021, vol. 55, no. 4, pp. 412—419.
Shukla, P., Rajput, R., Kumar, R., et al., Biochemical composition of amniotic fluid during different stages of gestation in Gaddi sheep, Indian J. Anim. Res., 2019, vol. 53, no. 2, pp. 178—180.
Peter, J.A.C., Christopher, J.F., Naohisa, G., et al., The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants, Nucleic Acids Res., 2010, vol. 38, no. 6, pp. 1767—1771.
Kim, D., Langmead, B., and Salzberg, S.L., HISAT: a fast spliced aligner with low memory requirements, Nat. Methods, 2015, vol. 12, no. 4, pp. 357—360.
Article CAS PubMed PubMed Central Google Scholar
Benelli, M., Pescucci, C., Marseglia, G., et al., Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript, Bioinformatics, 2012, vol. 28, no. 24, pp. 3232—3239.
Article CAS PubMed Google Scholar
Shen, S.H., Park, J.W., Lu, Z.X., et al., rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data, Proc. Natl. Acad. Sci. U.S.A., 2014, vol. 111, no. 51, pp. E5593—E5601.
Article CAS PubMed PubMed Central Google Scholar
Langmead, B. and Salzberg, S.L., Fast gapped-read alignment with Bowtie 2, Nat. Methods, 2012, vol. 9, no. 4, pp. 357—369.
Article CAS PubMed PubMed Central Google Scholar
Li, B. and Dewey, C.N., RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome, BMC Bioinf., 2011. https://doi.org/10.1186/1471-2105-12-323
Kolde, R., Implementation of Heatmaps That Offers More Control over Dimensions and Appearance, Version 1.0.12, 2019.
Michael, I.L., Huber, W., and Anders, S., Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2, Genome Biol., 2014, vol. 15, no. 12, p. 550. https://doi.org/10.1186/s13059-014-0550-8
Benjamini, Y. and Yekutieli, D., The control of the false discovery rate in multiple testing under dependency, Ann. Stat., 2001, vol. 29, no. 4, pp. 1165—1188.
Xie, C., Mao, X.Z., Huang, J.J., et al., KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases, Nucleic Acids Res., 2011, vol. 39, web server issue, pp. W316—W322.
Livak, K.L. and Schmittgen, T.D., Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method, Methods, 2001, vol. 25, no. 4, pp. 402—408.
Article CAS PubMed Google Scholar
Li, X.Y., Ao, J.P. and Wu, J., Systematic identification and comparison of expressed profiles of lncRNAs and circRNAs with associated co-expression and ceRNA networks in mouse germline stem cells, Oncotarget, 2017, vol. 8, no. 16, pp. 26573—26590.
Article PubMed PubMed Central Google Scholar
Mohammad, R.B., Batool, H., Babak, A., et al., In silico prediction of long intergenic non-coding RNAs in sheep, Genome, 2016, vol. 59, no. 4, pp. 263—275.
Mohammad, R.B. and Seyed, A.S., Identification and expression analysis of long noncoding RNAs in Fat-Tail of sheep breeds, G3 (Bethesda), 2019, vol. 9, no. 4, pp. 1263—1276.
Bao, Y.J., Yao, X.L., Li, X.D., et al., INHBA transfection regulates proliferation, apoptosis and hormone synthesis in sheep granulosa cells, Theriogenology, 2021, no. 175, pp. 111—122.
Brewster, J.L., Martin, S.L., Toms, J., et al., Deletion of Dad1 in mice induces an apoptosis-associated embryonic death, Genesis, 2000, vol. 26, no. 4, pp. 271—278.
Article CAS PubMed Google Scholar
Lan, R.X., Ge, D.X., Liu, Y.Z., et al., Dcx expression defines a subpopulation of Gdf5+ cells with chondrogenic potentials in E12.5 mouse embryonic limbs, Biochem. Biophys. Rep., 2022, vol. 29, р. 101200. https://doi.org/10.1016/j.bbrep.2022.101200
Article CAS PubMed PubMed Central Google Scholar
Umer, S., Zhao, S.J., Sammad, A., et al., AMH: could it be used as a biomarker for fertility and superovulation in domestic animals, Genes (Basel), 2019, 10, no. 12, р. 1009. https://doi.org/10.3390/genes10121009
Article CAS PubMed PubMed Central Google Scholar
Françoise, M., Michel, G.D., Valery, M., et al., Oxytocin signaling in the early life of mammals: link to neurodevelopmental disorders associated with ASD, Curr. Top. Behav. Neurosci., 2018, no. 35, pp. 239—268.
Zhao, L., Zheng, X.L., Liu, J.F., et al., PPAR signaling pathway in the first trimester placenta from in vitro fertilization and embryo transfer, Biomed. Pharmacother., 2019, no. 118, р. 109251. https://doi.org/10.1016/j.biopha.2019.109251
Zhu, H.Z., Yan, H.Y., Ma, J., et al., CCAL1 enhances osteoarthritis through the NF-κB/AMPK signaling pathway, FEBS Open Bio, 2020, vol. 10, no. 12, pp. 2553—2563.
Article CAS PubMed PubMed Central Google Scholar
Marcy, A.K. and Staci, D.B., The inflammatory event of birth: how oxytocin signaling may guide the development of the brain and gastrointestinal system, Front. Neuroendocrinol., 2019, no. 55, р. 100794.https://doi.org/10.1016/j.yfrne.2019.100794
Vaidyanathan, R., and Hammock, E.A., Oxytocin receptor dynamics in the brain across development and species, Dev. Neurobiol., 2017, vol. 77, no. 2, pp. 143—157.
Article CAS PubMed Google Scholar
Silvia, R., Mateusz, C.A., Francesca, G., et al., Transient oxytocin signaling primes the development and function of excitatory hippocampal neurons, eLife, 2017, no. 6, р. e22466.https://doi.org/10.7554/eLife.22466
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