References and other readings
The following references are assigned as reading, cited, and/or provided as further resources in the course.
Abdill, Richard J., Emma Talarico, and Laura Grieneisen. 2024. “A How-to Guide for Code Sharing in Biology.” PLOS Biology 22 (9): e3002815. https://doi.org/10.1371/journal.pbio.3002815.
Allesina, Stefano, and Madlen Wilmes. 2019. Computing Skills for Biologists: A Toolbox. Princeton University Press. https://doi.org/10.1515/9780691183961.
Andrews, S. 2010. FASTQC. A Quality Control Tool for High Throughput Sequence Data.
Aragona, Maria, Anita Haegi, Maria Teresa Valente, et al. 2022. “New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?” Journal of Fungi 8 (7): 737. https://doi.org/10.3390/jof8070737.
Bryan, Jennifer. 2017. Excuse Me, Do You Have a Moment to Talk about Version Control? https://doi.org/10.7287/peerj.preprints.3159v2.
Buffalo, Vince. 2015. Bioinformatics Data Skills [Reproducible and Robust Research With Open Source Tools]. First edition. O’Reilly.
Claude Science, and Amir Shanehsazzadeh. 2026. Autonomous de Novo Protein Binder Design with Claude. Technical report. Anthropic. https://www-cdn.anthropic.com/30bf50e22a01388bb29bf077ee3f244531594b7a.pdf.
Cooper, Natalie, Adam T. Clark, Nicolas Lecomte, Huijie Qiao, and Aaron M. Ellison. 2024. “Harnessing Large Language Models for Coding, Teaching and Inclusion to Empower Research in Ecology and Evolution.” Methods in Ecology and Evolution 15 (10): 1757–63. https://doi.org/10.1111/2041-210X.14325.
Dewar, James M. 2026. Large Language Models for Science and Research: A Practical Guide. Version 1.1. Zenodo. https://doi.org/10.5281/zenodo.19371506.
Di Tommaso, Paolo, Maria Chatzou, Evan W. Floden, Pablo Prieto Barja, Emilio Palumbo, and Cedric Notredame. 2017. “Nextflow enables reproducible computational workflows.” Nature Biotechnology 35 (4): 316–19. https://doi.org/10.1038/nbt.3820.
Ewels, Philip A., Alexander Peltzer, Sven Fillinger, et al. 2020. “The Nf-Core Framework for Community-Curated Bioinformatics Pipelines.” Nature Biotechnology 38 (3): 276–78. https://doi.org/10.1038/s41587-020-0439-x.
Garrigós, Marta, Guillem Ylla, Josué Martínez-de la Puente, Jordi Figuerola, and María José Ruiz-López. 2025. “Two Avian Plasmodium Species Trigger Different Transcriptional Responses on Their Vector Culex pipiens.” Molecular Ecology 34 (15): e17240. https://doi.org/10.1111/mec.17240.
Goh, Wilson Wen Bin, Annikka Polster, Limsoon Wong, and Marija Cvijovic. 2026. “Rethinking Bioinformatics Expertise in the Era of Artificial Intelligence.” Npj Digital Medicine 9 (1). https://doi.org/10.1038/s41746-026-02777-1.
Grünwald, Niklaus J., Clive H. Bock, Jeff H. Chang, et al. 2024. “Open Access and Reproducibility in Plant Pathology Research: Guidelines and Best Practices.” Phytopathology® 114 (5): 910–16. https://doi.org/10.1094/PHYTO-12-23-0483-IA.
Heyden, Sophie von der, Luciano B. Beheregaray, Sarah Fitzpatrick, Catherine E. Grueber, Yibo Hu, and Alison G. Nazareno. 2025. “Advancing Species Conservation and Management Through Omics Tools.” Molecular Ecology Resources 25 (5): e14123. https://doi.org/10.1111/1755-0998.14123.
King, Rachel A., Laurel Abowd, Carlo W. Broderick, et al. 2026. “Ten Simple Rules for Effective Use of Generative AI for Code Development in Environmental Science.” PLOS Computational Biology 22 (8): e1014627. https://doi.org/10.1371/journal.pcbi.1014627.
Krueger, Felix. 2026. Trim Galore. V. 2.3.0. Released. https://github.com/FelixKrueger/TrimGalore.
Lee, Jun-Yeong. 2023. “The Principles and Applications of High-Throughput Sequencing Technologies.” Development & Reproduction 27 (1): 9–24. https://doi.org/10.12717/DR.2023.27.1.9.
Leek, Jeffrey T., and Leah R. Jager. 2017. “Is Most Published Research Really False?” Annual Review of Statistics and Its Application 4 (1): 109–22. https://doi.org/10.1146/annurev-statistics-060116-054104.
Lenharo, Mariana. 2026. “Is AI Ruining Our Skills? Early Results Are in — and They’re Not Good.” Nature 655 (8121): 15–16. https://doi.org/10.1038/d41586-026-01947-1.
Mahmood, Umer, Xiaodong Li, Yonghai Fan, et al. 2022. “Multi-Omics Revolution to Promote Plant Breeding Efficiency.” Frontiers in Plant Science 13 (December). https://doi.org/10.3389/fpls.2022.1062952.
Manzoni, Claudia, Demis A Kia, Jana Vandrovcova, et al. 2018. “Genome, Transcriptome and Proteome: The Rise of Omics Data and Their Integration in Biomedical Sciences.” Briefings in Bioinformatics 19 (2): 286–302. https://doi.org/10.1093/bib/bbw114.
Markowetz, Florian. 2015. “Five Selfish Reasons to Work Reproducibly.” Genome Biology 16 (1): 1–4. https://doi.org/10.1186/s13059-015-0850-7.
Mölder, Felix, KP Jablonski, B Letcher, et al. 2025. “Sustainable Data Analysis with Snakemake [Version 3; Peer Review: 2 Approved].” F1000Research 10 (33). https://doi.org/10.12688/f1000research.29032.3.
Nizamani, Mir Muhammad, Qian Zhang, Ghulam Muhae-Ud-Din, and Yong Wang. 2023. “High-Throughput Sequencing in Plant Disease Management: A Comprehensive Review of Benefits, Challenges, and Future Perspectives.” Phytopathology Research 5 (1): 44. https://doi.org/10.1186/s42483-023-00199-5.
Noble, William Stafford. 2009. “A Quick Guide to Organizing Computational Biology Projects.” PLOS Computational Biology 5 (7): e1000424. https://doi.org/10.1371/journal.pcbi.1000424.
O’Neil, Shawn T. 2019. A Primer for Computational Biology. Oregon State University. https://open.oregonstate.education/computationalbiology/.
Pearson, Helen. 2025. “Universities Are Embracing AI: Will Students Get Smarter or Stop Thinking?” Nature 646 (8086): 788–91. https://doi.org/10.1038/d41586-025-03340-w.
Perkel, Jeffrey M. 2019. “Workflow Systems Turn Raw Data into Scientific Knowledge.” Nature 573 (7772): 149–50. https://doi.org/10.1038/d41586-019-02619-z.
Pinto, Yishay, and Ami S. Bhatt. 2024. “Sequencing-Based Analysis of Microbiomes.” Nature Reviews Genetics 25 (12): 829–45. https://doi.org/10.1038/s41576-024-00746-6.
Poinsignon, Thibault, Pierre Poulain, Mélina Gallopin, and Gaëlle Lelandais. 2023. Working with Omics Data: An Interdisciplinary Challenge at the Crossroads of Biology and Computer Science. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3195-9_10.
Sen Gupta, Alex. 2025. “PhD Training Needs a Reboot in an AI World.” Nature 647 (8088): 27–28. https://doi.org/10.1038/d41586-025-03572-w.
Sharma, Mrinank, Meg Tong, Tomasz Korbak, et al. 2023. Towards Understanding Sycophancy in Language Models. October 20. https://doi.org/10.48550/arXiv.2310.13548.
Turpin, Miles, Julian Michael, Ethan Perez, and Samuel R. Bowman. 2023. Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting. May 7. https://doi.org/10.48550/arXiv.2305.04388.
Wang, Nian, George W. Sundin, Leonardo De La Fuente, et al. 2024. “Key Challenges in Plant Pathology in the Next Decade.” Phytopathology® 114 (5): 837–42. https://doi.org/10.1094/PHYTO-04-24-0137-KC.
Wickham, Hadley. 2014. “Tidy Data.” Journal of Statistical Software 59 (10): 1–23. https://doi.org/10.18637/jss.v059.i10.
Wilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K. Teal. 2017. “Good Enough Practices in Scientific Computing.” PLOS Computational Biology 13 (6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510.
Yoon, Peter H., Januka S. Athukoralage, Emmanuel Ameisen, Eric Kauderer-Abrams, Nicholas T. Perry, and Matthew G. Durrant. 2026. Autonomous AI Agents Discover Reverse Transcriptases with Tandem Repeat Arrays. Technical report. Anthropic. https://www-cdn.anthropic.com/22573675ada52a8ca8a97a1a4b4326b2f208a071.pdf.
Zhang, Muru, Ofir Press, William Merrill, Alisa Liu, and Noah A. Smith. 2023. How Language Model Hallucinations Can Snowball. May 22. https://doi.org/10.48550/arXiv.2305.13534.
Zhang, Zichen, Sami Khanal, Amy Raudenbush, Kelley Tilmon, and Christopher Stewart. 2022. “Assessing the Efficacy of Machine Learning Techniques to Characterize Soybean Defoliation from Unmanned Aerial Vehicles.” Computers and Electronics in Agriculture 193 (February): 106682. https://doi.org/10.1016/j.compag.2021.106682.
Zhu, Haocheng, Dandan Jiang, Qiao Zhang, et al. 2026. “Programmable Design of Synthetic Plant Immune Receptors for Pathogen Protein Recognition.” Science 393 (6815): eaee1792. https://doi.org/10.1126/science.aee1792.