Jabado, O. J. et al. Greene SCPrimer: a rapid comprehensive tool for designing degenerate primers from multiple sequence alignments. Nucleic Acids Res. 34, 6605–6611 (2006).
Kreer, C. et al. openPrimeR for multiplex amplification of highly diverse templates. J. Immunol. Methods 480, 112752 (2020).
Duitama, J. et al. PrimerHunter: a primer design tool for PCR-based virus subtype identification. Nucleic Acids Res. 37, 2483–2492 (2009).
Brodin, J. et al. A multiple-alignment based primer design algorithm for genetically highly variable DNA targets. BMC Bioinform. 14, 255 (2013).
Varliero, G., Wray, J., Malandain, C. & Barker, G. PhyloPrimer: a taxon-specific oligonucleotide design platform. PeerJ 9, e11120 (2021).
Metsky, H. C. et al. Designing sensitive viral diagnostics with machine learning. Nat. Biotechnol. 40, 1123–1131 (2022).
Gupta, A. & Zou, J. Feedback GAN for DNA optimizes protein functions. Nat. Mach. Intell. 1, 105–111 (2019).
Repecka, D. et al. Expanding functional protein sequence spaces using generative adversarial networks. Nat. Mach. Intell. 3, 324–333 (2021).
Shin, J.-E. et al. Protein design and variant prediction using autoregressive generative models. Nat. Commun. 12, 2403 (2021).
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Strokach, A. & Kim, P. M. Deep generative modeling for protein design. Curr. Opin. Struct. Biol. 72, 226–236 (2022).
Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41, 1099–1106 (2023).
Taskiran, I. I. et al. Cell type directed design of synthetic enhancers. Nature 626, 212–220 (2024).
de Almeida, B. P. et al. Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo. Nature 626, 207–211 (2024).
Gosai, S. J. et al. Machine-guided design of synthetic cell type-specific cis-regulatory elements. Preprint at https://www.biorxiv.org/content/10.1101/2023.08.08.552077v1 (2023).
Zhao, D. et al. Imperfect guide-RNA (igRNA) enables CRISPR single-base editing with ABE and CBE. Nucleic Acids Res. 50, 4161–4170 (2022).
Gootenberg, J. S. et al. Nucleic acid detection with CRISPR–Cas13a/C2c2. Science 356, 438–442 (2017).
Arizti-Sanz, J. et al. Simplified Cas13-based assays for the fast identification of SARS-CoV-2 and its variants. Nat. Biomed. Eng. 6, 932–943 (2022).
Kaminski, M. M., Abudayyeh, O. O., Gootenberg, J. S., Zhang, F. & Collins, J. J. CRISPR-based diagnostics. Nat. Biomed. Eng. 5, 643–656 (2021).
Killoran, N., Lee, L. J., Delong, A., Duvenaud, D. & Frey, B. J. Generating and designing DNA with deep generative models. Preprint at https://arxiv.org/abs/1712.06148 (2017).
Zrimec, J. et al. Controlling gene expression with deep generative design of regulatory DNA. Nat. Commun. 13, 5099 (2022).
Sample, P. J. et al. Human 5′ UTR design and variant effect prediction from a massively parallel translation assay. Nat. Biotechnol. 37, 803–809 (2019).
Porto, W. F. et al. In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design. Nat. Commun. 9, 1490 (2018).
Fox, R. Directed molecular evolution by machine learning and the influence of nonlinear interactions. J. Theor. Biol. 234, 187–199 (2005).
Mantena S. et al. BADGERS: a package for designing artificial Cas13a diagnostic guides. GitHub https://github.com/broadinstitute/badgers-cas13 (2024).
Granados, A., Peci, A., McGeer, A. & Gubbay, J. B. Influenza and rhinovirus viral load and disease severity in upper respiratory tract infections. J. Clin. Virol. 86, 14–19 (2017).
Dayarathna, S. et al. Are viral loads in the febrile phase a predictive factor of dengue disease severity? Preprint at https://www.medrxiv.org/content/10.1101/2023.07.31.23293412v1 (2023).
Yuan, L. et al. A single mutation in the prM protein of Zika virus contributes to fetal microcephaly. Science 358, 933–936 (2017).
Apinjoh, T. O., Ouattara, A., Titanji, V. P. K., Djimde, A. & Amambua-Ngwa, A. Genetic diversity and drug resistance surveillance of plasmodium falciparum for malaria elimination: is there an ideal tool for resource-limited sub-Saharan Africa? Malar. J. 18, 217 (2019).
Carter, T. E. et al. Evaluation of dihydrofolate reductase and dihydropteroate synthetase genotypes that confer resistance to sulphadoxine-pyrimethamine in Plasmodium falciparum in Haiti. Malar. J. 11, 275 (2012).
Quan, H. et al. High multiple mutations of Plasmodium falciparum-resistant genotypes to sulphadoxine–pyrimethamine in Lagos, Nigeria. Infect. Dis. Poverty 9, 91 (2020).
Yoshida, N., Yamauchi, M., Morikawa, R., Hombhanje, F. & Mita, T. Increase in the proportion of Plasmodium falciparum with kelch13 C580Y mutation and decline in pfcrt and pfmdr1 mutant alleles in Papua New Guinea. Malar. J. 20, 410 (2021).
Hyde, J. E. Drug-resistant malaria. Trends Parasitol. 21, 494–498 (2005).
Harvey, W. T. et al. SARS-CoV-2 variants, spike mutations and immune escape. Nat. Rev. Microbiol. 19, 409–424 (2021).
Brookes, D., Park, H. & Listgarten, J. Conditioning by adaptive sampling for robust design. In Proc. 36th International Conference on Machine Learning (eds Chaudhuri, K. & Salakhutdinov, R.) vol. 97, 773–782 (PMLR, 2019).
Sinai, S. et al. AdaLead: a simple and robust adaptive greedy search algorithm for sequence design. Preprint at https://arxiv.org/abs/2010.02141 (2020).
Tambe, A., East-Seletsky, A., Knott, G. J., Doudna, J. A. & O’Connell, M. R. RNA binding and HEPN-nuclease activation are decoupled in CRISPR–Cas13a. Cell Rep. 24, 1025–1036 (2018).
Abudayyeh, O. O. et al. RNA targeting with CRISPR–Cas13. Nature 550, 280–284 (2017).
Abudayyeh, O. O. et al. C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector. Science 353, aaf5573 (2016).
Meeske, A. J. & Marraffini, L. A. RNA guide complementarity prevents self-targeting in type VI CRISPR systems. Mol. Cell 71, 791–801.e3 (2018).
Welch, N. L. et al. Multiplexed CRISPR-based microfluidic platform for clinical testing of respiratory viruses and identification of SARS-CoV-2 variants. Nat. Med. 28, 1083–1094 (2022).
Bock, C. et al. High-content CRISPR screening. Nat. Rev. Methods Primers 2, 9 (2022).
Huang, X., Yang, D., Zhang, J., Xu, J. & Chen, Y. E. Recent advances in improving gene-editing specificity through CRISPR–Cas9 nuclease engineering. Cells 11, 2186 (2022).
Saha, K. Accounting for diversity in the design of CRISPR-based therapeutic genome editing. Nat Genet. 55, 6–7 (2023).
Shanehsazzadeh, A. et al. Unlocking de novo antibody design with generative artificial intelligence. Preprint at https://www.biorxiv.org/content/10.1101/2023.01.08.523187v1 (2023).
Goodfellow, I. J. et al. Generative adversarial networks. Preprint at https://arxiv.org/abs/1406.2661 (2014).
Mirza, M. & Osindero, S. Conditional generative adversarial nets. Preprint at https://arxiv.org/abs/1411.1784 (2014).
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. & Courville, A. Improved training of Wasserstein GANs. Preprint at https://arxiv.org/abs/1704.00028 (2017).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
Yoshida, M. et al. Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides. Chem 4, 533–543 (2018).
Sinai, S. & Kelsic, E. D. A primer on model-guided exploration of fitness landscapes for biological sequence design. Preprint at https://arxiv.org/abs/2010.10614 (2020).
Abadi, M., et al. TensorFlow: large-scale machine learning on heterogeneous systems (2015).
Federhen, S. The NCBI taxonomy database. Nucleic Acids Res. 40, D136–D143 (2012).
Shu, Y. & McCauley, J. GISAID: global initiative on sharing all influenza data—from vision to reality. Euro Surveil. 22, 30494 (2017).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Pickett, B. E. et al. ViPR: an open bioinformatics database and analysis resource for virology research. Nucleic Acids Res. 40, D593–D598 (2011).
Hodcroft, E. B. SARS-CoV-2 mutations and variants of interest. CoVariants https://covariants.org/ (2020).
Gangavarapu, K. et al. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations. Nat. Methods 20, 512–522 (2023).
Kellner, M. J., Koob, J. G., Gootenberg, J. S., Abudayyeh, O. O. & Zhang, F. SHERLOCK: nucleic acid detection with CRISPR nucleases. Nat. Protoc. 14, 2986–3012 (2019).
