Levy, A., Raghu, R., Feathers, J. R., Grzadkowski, M., Poitevin, F., Johnston, J. D., Vallese, F., Clarke, O. B., Wetzstein, G., & Zhong, E. D. (2025). CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets. Nature Methods. https://doi.org/10.1038/s41592-025-02720-4

Hallou, A., He, R., Simons, B. D., & Dumitrascu, B. (2025). A computational pipeline for spatial mechano-transcriptomics. Nature Methods. https://doi.org/10.1038/s41592-025-02618-1

Börner, K., Blood, P. D., Silverstein, J. C., Ruffalo, M., Satija, R., Teichmann, S. A., Pryhuber, G. J., Misra, R. S., Purkerson, J. M., Fan, J., Hickey, J. W., Molla, G., Xu, C., Zhang, Y., Weber, G. M., Jain, Y., Qaurooni, D., Kong, Y., … Herr, B. W. (2025). Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage. Nature Methods. https://doi.org/10.1038/s41592-024-02563-5

Windolf, C., Yu, H., Paulk, A. C., Meszéna, D., Muñoz, W., Boussard, J., Hardstone, R., Caprara, I., Jamali, M., Kfir, Y., Xu, D., Chung, J. E., Sellers, K. K., Ye, Z., Shaker, J., Lebedeva, A., Raghavan, R., Trautmann, E., Melin, M., … Varol, E. (2025). DREDge: robust motion correction for high-density extracellular recordings across species. Nature Methods. https://doi.org/10.1038/s41592-025-02614-5

Olsen, T. R., Talla, P., Sagatelian, R. K., Furnari, J., Bruce, J. N., Canoll, P., Zha, S., & Sims, P. A. (2025). Scalable co-sequencing of RNA and DNA from individual nuclei. Nature Methods. https://doi.org/10.1038/s41592-024-02579-x

Sumanaweera, D., Suo, C., Cujba, A.-M., Muraro, D., Dann, E., Polanski, K., Steemers, A. S., Lee, W., Oliver, A. J., Park, J.-E., Meyer, K. B., Dumitrascu, B., & Teichmann, S. A. (2024). Gene-level alignment of single-cell trajectories. Nature Methods. https://doi.org/10.1038/s41592-024-02378-4

Fan, J. L., Nazaret, A., & Azizi, E. (2024). A thousand and one tumors: the promise of AI for cancer biology. Nature Methods, 21(8), 1403–1406. https://doi.org/10.1038/s41592-024-02364-w

Ahdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., Nowaczynski, A., Wang, B., Stepniewska-Dziubinska, M. M., Zhang, S., Ojewole, A., Guney, M. E., Biderman, S., Watkins, A. M., … AlQuraishi, M. (2024). OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nature Methods, 21(8), 1514–1524. https://doi.org/10.1038/s41592-024-02272-z

Rangan, R., Feathers, R., Khavnekar, S., Lerer, A., Johnston, J. D., Kelley, R., Obr, M., Kotecha, A., & Zhong, E. D. (2024). CryoDRGN-ET: deep reconstructing generative networks for visualizing dynamic biomolecules inside cells. Nature Methods, 21(8), 1537–1545. https://doi.org/10.1038/s41592-024-02340-4

Kawaoka, J., & Lomvardas, S. (2024). LiMCA: Hi-C gets an RNA twist. Nature Methods, 21(6), 934–935. https://doi.org/10.1038/s41592-024-02205-w

Chua, E. Y. D., Alink, L. M., Kopylov, M., Johnston, J. D., Eisenstein, F., & de Marco, A. (2024). Square beams for optimal tiling in transmission electron microscopy. Nature Methods, 21(4), 562–565. https://doi.org/10.1038/s41592-023-02161-x

Hou, W., & Ji, Z. (2024). Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. Nature Methods. https://doi.org/10.1038/s41592-024-02235-4

Evans, S. W., Shi, D.-Q., Chavarha, M., Plitt, M. H., Taxidis, J., Madruga, B., Fan, J. L., Hwang, F.-J., van Keulen, S. C., Suomivuori, C.-M., Pang, M. M., Su, S., Lee, S., Hao, Y. A., Zhang, G., Jiang, D., Pradhan, L., Roth, R. H., Liu, Y., … Lin, M. Z. (2023). A positively tuned voltage indicator for extended electrical recordings in the brain. Nature Methods, 20(7), 1104–1113. https://doi.org/10.1038/s41592-023-01913-z

Liu, L., Huang, Y., & Wang, H. H. (2023). Fast and efficient template-mediated synthesis of genetic variants. Nature Methods, 20(6), 841–848. https://doi.org/10.1038/s41592-023-01868-1

Cho, S., Discher, D. E., Leong, K. W., Vunjak-Novakovic, G., & Wu, J. C. (2022). Challenges and opportunities for the next generation of cardiovascular tissue engineering. Nature Methods, 19(9), 1064–1071. https://doi.org/10.1038/s41592-022-01591-3

Hammelman, J., Patel, T., Closser, M., Wichterle, H., & Gifford, D. (2022). Ranking reprogramming factors for cell differentiation. Nature Methods, 19(7), 812–822. https://doi.org/10.1038/s41592-022-01522-2

Wang, L., Xing, X., Zeng, X., Jackson, S. R., TeSlaa, T., Al-Dalahmah, O., Samarah, L. Z., Goodwin, K., Yang, L., McReynolds, M. R., Li, X., Wolff, J. J., Rabinowitz, J. D., & Davidson, S. M. (2022). Spatially resolved isotope tracing reveals tissue metabolic activity. Nature Methods, 19(2), 223–230. https://doi.org/10.1038/s41592-021-01378-y

AlQuraishi, M., & Sorger, P. K. (2021). Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms. Nature Methods, 18(10), 1169–1180. https://doi.org/10.1038/s41592-021-01283-4