RNA Sequencing Data Analysis

Displaying 1 - 11 of 11CSV
Wang, Z., Liu, Z., Fang, Y., Zhang, H. H., Sun, X., Hao, N., Que, J., & Ding, H. (2025). Training data diversity enhances the basecalling of novel RNA modification-induced nanopore sequencing readouts. Nature Communications, 16(1). https://doi.org/10.1038/s41467-025-55974-z
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Allen, B. S., Kidd, M., Sue, P. K., & Filkins, L. M. (2025). A Committee-Based Diagnostic Stewardship Model for Pathogen Metagenomic Sequencing in Children. The Journal of Applied Laboratory Medicine, 10(1), 59–65. https://doi.org/10.1093/jalm/jfae084
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Wang, Z., Fang, Y., Liu, Z., Hao, N., Zhang, H. H., Sun, X., Que, J., & Ding, H. (2024). Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-51639-5
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Schrauwen, I., Rajendran, Y., Acharya, A., Öhman, S., Arvio, M., Paetau, R., Siren, A., Avela, K., Granvik, J., Leal, S. M., Määttä, T., Kokkonen, H., & Järvelä, I. (2024). Optical genome mapping unveils hidden structural variants in neurodevelopmental disorders. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-62009-y
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Malamon, J. S., Farrell, J. J., Xia, L. C., Dombroski, B. A., Das, R. G., Way, J., Kuzma, A. B., Valladares, O., Leung, Y. Y., Scanlon, A. J., Lopez, I. A. B., Brehony, J., Worley, K. C., Zhang, N. R., Wang, L.-S., Farrer, L. A., Schellenberg, G. D., Lee, W.-P., & Vardarajan, B. N. (2024). A comparative study of structural variant calling in WGS from Alzheimer’s disease families. Life Science Alliance, 7(5), e202302181. https://doi.org/10.26508/lsa.202302181
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Sepich-Poore, G. D., McDonald, D., Kopylova, E., Guccione, C., Zhu, Q., Austin, G., Carpenter, C., Fraraccio, S., Wandro, S., Kosciolek, T., Janssen, S., Metcalf, J. L., Song, S. J., Kanbar, J., Miller-Montgomery, S., Heaton, R., Mckay, R., Patel, S. P., Swafford, A. D., … Knight, R. (2024). Robustness of cancer microbiome signals over a broad range of methodological variation. Oncogene, 43(15), 1127–1148. https://doi.org/10.1038/s41388-024-02974-w
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Masuda, I., Yamaki, Y., Detroja, R., Tagore, S., Moore, H., Maharjan, S., Nakano, Y., Christian, T., Matsubara, R., Lowe, T. M., Frenkel-Morgenstern, M., & Hou, Y.-M. (2022). tRNA methylation resolves codon usage bias at the limit of cell viability. Cell Reports, 41(4), 111539. https://doi.org/10.1016/j.celrep.2022.111539
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Hayeck, T. J., Stong, N., Baugh, E., Dhindsa, R., Turner, T. N., Malakar, A., Mosbruger, T. L., Shaw, G. T.-W., Duan, Y., Ionita-Laza, I., Goldstein, D., & Allen, A. S. (2022). Ancestry adjustment improves genome-wide estimates of regional intolerance. Genetics. https://doi.org/10.1093/genetics/iyac050
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Li, Z., Wang, D., Liao, H., Zhang, S., Guo, W., Chen, L., Lu, L., Huang, T., & Cai, Y.-D. (2022). Exploring the Genomic Patterns in Human and Mouse Cerebellums Via Single-Cell Sequencing and Machine Learning Method. Frontiers in Genetics, 13. https://doi.org/10.3389/fgene.2022.857851
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Joseph, T. A., Chlenski, P., Litman, A., Korem, T., & Pe’er, I. (2022). Accurate and robust inference of microbial growth dynamics from metagenomic sequencing reveals personalized growth rates. Genome Research, 32(3), 558–568. https://doi.org/10.1101/gr.275533.121
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