References and other readings

Author
Affiliation

Jelmer Poelstra

CFAES Bioinformatics Core, Ohio State University

Published

August 25, 2025


The following references are assigned as reading, cited, and/or provided as further resources in the course.

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References

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.
Alam, Khairul, and Banani Roy. 2025. “From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics,” August. https://doi.org/10.48550/arXiv.2507.20122.
Allesina, Stefano. 2019. Computing Skills for Biologists: A Toolbox. Princeton, NJ: 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, Luca Riccioni, Laura Orzali, Salvatore Vitale, Laura Luongo, and Alessandro Infantino. 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.
Awan, Ali R., Mehrdad Oveisi, and Mohammad M. Karimi. 2025. “Prompt-Based Bioinformatics: A New Interface for Multi-Omics Analysis.” Nature Reviews Genetics, August, 1–2. https://doi.org/10.1038/s41576-025-00889-0.
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. Beijing: O’Reilly.
Community, The Turing Way. 2025. The Turing Way: A Handbook for Reproducible, Ethical and Collaborative Research. 10.5281/ZENODO.15213042.
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.
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.
Dijk, Erwin L. van, Delphine Naquin, Kévin Gorrichon, Yan Jaszczyszyn, Rania Ouazahrou, Claude Thermes, and Céline Hernandez. 2023. “Genomics in the Long-Read Sequencing Era.” Trends in Genetics 39 (9): 649–71. https://doi.org/10.1016/j.tig.2023.04.006.
Ewels, Philip A., Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso, and Sven Nahnsen. 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.
Ewels, Philip, Måns Magnusson, Sverker Lundin, and Max Käller. 2016. “MultiQC: Summarize Analysis Results for Multiple Tools and Samples in a Single Report.” Bioinformatics 32 (19): 3047–48. https://doi.org/10.1093/bioinformatics/btw354.
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.
Gibney, Elizabeth. 2025. “Scientists Flock to DeepSeek: How They’re Using the Blockbuster AI Model.” Nature, January. https://doi.org/10.1038/d41586-025-00275-0.
Grünwald, Niklaus J., Clive H. Bock, Jeff H. Chang, Alessandra Alves De Souza, Emerson M. Del Ponte, Lindsey J. du Toit, Anne E. Dorrance, 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.
Guest, Olivia, Marcela Suarez, Barbara Müller, Edwin van Meerkerk, Arnoud Oude Groote Beverborg, Ronald de Haan, Andrea Reyes Elizondo, et al. 2025. “Against the Uncritical Adoption of ’AI’ Technologies in Academia,” September. https://doi.org/10.5281/zenodo.17065099.
Helmy, Mohamed, Lingling Jin, Amr Alhossary, Tamer Mansour, Diogo Pellagrina, and Kumar Selvarajoo. 2025. “Ten Simple Rules for Optimal and Careful Use of Generative AI in Science.” PLOS Computational Biology 21 (10): e1013588. https://doi.org/10.1371/journal.pcbi.1013588.
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.
Konkel, Zachary, and Jason C. Slot. 2023. “Mycotools: An Automated and Scalable Platform for Comparative Genomics.” BioRxiv. https://doi.org/10.1101/2023.09.08.556886.
Krueger, Felix. 2021. Trimgalore. 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.
Lubiana, Tiago, Rafael Lopes, Pedro Medeiros, Juan Carlo Silva, Andre Nicolau Aquime Goncalves, Vinicius Maracaja-Coutinho, and Helder I. Nakaya. 2023. “Ten Quick Tips for Harnessing the Power of ChatGPT in Computational Biology.” PLOS Computational Biology 19 (8): e1011319. https://doi.org/10.1371/journal.pcbi.1011319.
Mahmood, Umer, Xiaodong Li, Yonghai Fan, Wei Chang, Yue Niu, Jiana Li, Cunmin Qu, and Kun Lu. 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, John Hardy, Nicholas W Wood, Patrick A Lewis, and Raffaele Ferrari. 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, MB Hall, PC van Dyken, CH Tomkins-Tinch, V Sochat, 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.
Pereira, Rute, Jorge Oliveira, and Mário Sousa. 2020. “Bioinformatics and computational tools for next-generation sequencing analysis in clinical genetics.” Journal of Clinical Medicine 9 (1). https://doi.org/10.3390/jcm9010132.
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.
Piccolo, Stephen R., Paul Denny, Andrew Luxton-Reilly, Samuel Payne, and Perry G. Ridge. 2023. “Many Bioinformatics Programming Tasks Can Be Automated with ChatGPT.” PLOS Computational Biology 19 (9): e1011511. https://doi.org/10.1371/journal.pcbi.1011511.
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.” In, 313–30. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3195-9_10.
Rahman, Chowdhury Rafeed, and Limsoon Wong. 2023. “How Much Can ChatGPT Really Help Computational Biologists in Programming?” December. https://doi.org/10.48550/arXiv.2309.09126.
Rai, Amit, Mami Yamazaki, and Kazuki Saito. 2019. “A New Era in Plant Functional Genomics.” Current Opinion in Systems Biology, Gene regulation, 15 (June): 58–67. https://doi.org/10.1016/j.coisb.2019.03.005.
Wang, Nian, George W. Sundin, Leonardo De La Fuente, Jaime Cubero, Satyanarayana Tatineni, Marin T. Brewer, Quan Zeng, 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.
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.
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.