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Publications

2021 ~ current

*co-first authors

#co-corresponding authors

  1. Choi, J*., Shin, JY*., Kim, TK*., Kim, K., Kim, J., Jeon E., Park J., Han, Y.D., Kim, KA., Sim, T., Kim, H.K#., Kim, H.S#. Site-specific mutagenesis screening in KRASG12D mutant library to uncover resistance mechanisms to KRASG12D inhibitors. Cancer Letters, doi.org/10.1016/j.canlet.2024.216904, (2024).

  2. Seo, SY., Min, S., Lee, S., Seo, J.H., Park, J., Kim, H.K., Song, M., Baek, D., Cho, SR., Kim, H.H. Massively parallel evaluation and computational prediction of the activities and specificities of 17 small Cas9s. Nature Methods, 20(7), 999-1009, (2023)/

  3. Yoo, G*., Kim, H.K*., Park, J., Kwak, H., Cheong, Y., Kim, D., Kim, Jiyun., Kim, Jisung., Kim, H.H. Prediction of efficiencies for diverse prime editing systems in multiple cell types. Cell, (2023).

  4. Kim, H.K*., Yoo, G*., Park, J., Min, S., Lee, S., Yoon, S., Kim, H.H. Predicting the efficiency of prime-editing guide RNAs in human cells. Nature Biotechnology, 39(2), 198-206, (2021).

  5. Park, J., Lim, J.M., Jung, I., Heo, S.J., Park, J., Chang, Y., Kim, H.K., Jung, D., Yu, J.H., Min, S., Yoon, S., Cho, SR., Park, T., Kim, H.H. Recording of elapsed time and temporal information about biological events using Cas9. Cell, 184(4), 1047-1063, (2021)

2017 ~ 2020

  1. Kim, N*., Kim, H.K*., Lee, S., Seo, J.H., Choi, J.W., Park, J., Min, S., Yoon, S., Cho, S-R., Kim, H.H. Prediction of the sequence-specific cleavage activity of Cas9 variants. Nature Biotechnology, 38(11), 1328-1336, (2020).

  2. Song, M*., Kim, H.K*., Lee, S*., Kim, Y., Seo, S.Y., Park, J., Choi, J.W., Jang, H., Shin, J.H., Min, S., Quan, Z., Kim, J.H., Kang, H.C., Yoon, S., Kim, H.H. Sequence-specific prediction of the efficiencies of adenine and cytosine base editors. Nature Biotechnology, 38(9), 1037-1043.

  3. Kim, H.K., Lee, S., Kim, Y., Park, J., Min, S., Choi, J.W., Huang, T.P., Yoon, S., Liu, D.R., Kim, H.H. High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells. Nature Biomedical Engineering, 4(1), 111-124, (2020)

  4. Kim, H.K*., Kim, Y*., Lee, S., Min, S., Bae, J.Y., Choi, J.W., Park, J., Jung, D., Yoon, S., Kim, H.H. SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance. Science Advances, 5(11), eaax9249, (2019)

  5. Kim, H.K*., Min, S*., Song, M., Jung, S., Choi, J.W., Kim, Y., Lee, S., Yoon, S#., Kim, H.H#. Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity. Nature Biotechnology, 36(3), 239-241, (2018)

  6. Kim, H.K*., Song, M*., Lee, J., Menon, A.V., Jung, S., Kang, Y.M., Choi, J.W., Woo, E., Koh, H.C., Nam, J.W., Kim, H.H. In vivo high-throughput profiling of CRISPR-Cpf1 activity. Nature Methods, 14(2), 153-159, (2017).

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