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Merge pull request #15 from computer-aided-biotech/feat/new-manuscrip…
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feat: new manuscript structure
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BenjaSanchez authored Oct 15, 2020
2 parents 7ddc33e + 7b289cd commit 8852051
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10 changes: 10 additions & 0 deletions content/02.introduction.md
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## Introduction

- Importance of computation in biological research and scope.
- Importance to reproducibility in computational biology.
- Mentioning previous literature and some of their major takeaways and topics not fully covered in here (what kind of languages to learn, advanced text editors/IDEs,
- The computational biology best-practices continuum: reproducibility at the personal level, sharing our research with others and over time.
- Addressing the audience: computational biologists researchers – data analysts, workflow designers, software developers, mathematical modelers, etc.
- Structure of this manuscript: mention Figure 1 and Figure 2.
- **Figure 1:** Funnel structure for better computational biology.
- **Figure 2:** Types of computational biology projects per level.
54 changes: 0 additions & 54 deletions content/02.manuscript.md

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11 changes: 11 additions & 0 deletions content/03.personal-research.md
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## Level 1: Personal Research

Goal: How should you manage your computational biology project?

Topics:
- Literate programming: R Markdown, Jupyter Notebooks
- Version control: Git / GitHub (commits)
- Software versioning and environment managers (Conda, python-env)
- Modularize, snippets
- Coding style: variable naming, linter, pep8, commenting
- Programming practices: paradigms (object-oriented, procedural), assertions, pair-programming
10 changes: 10 additions & 0 deletions content/04.collaboration.md
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## Level 2: Collaboration

Goal: How to allow your collaborators to reproduce and interact with your research/software?

Topics:
- Sharing notebooks: R Markdowns and Jupyter Notebooks (Binder, Google CoLab)
- Sharing apps: Shiny apps, Dashboard.
- GitHub (branching, pull requests)
- Workflow automation: Snakemake (NextFlow, Make, Bash script)
- Sharing data and metadata
12 changes: 12 additions & 0 deletions content/05.community.md
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## Level 3: Community

Goal: How to develop and maintain a computational biology project with community feedback over time?

Topics:
- GitHub releases and semantic versioning
- Git Flow, GitHub Issues
- Continuous integration and unit tests
- Dependencies per user: pip-tools
- Sharing software as Python packages, Conda/Bioconda or containers (Docker, Singularity)
- Include license (MIT) and DOIs
- Documentation: read the docs.
5 changes: 5 additions & 0 deletions content/06.case-studies.md
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## Case studies

- Example of computational biology project 1: RNA-seq analysis (workflow)
- Example of computational biology project 2: Genome-scale metabolic model (systems biology project)
- Example of computational biology project 3: Software development (computational tool)
3 changes: 3 additions & 0 deletions content/07.conclusion.md
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## Conclusion

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3 changes: 3 additions & 0 deletions content/08.acknowledgments.md
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## Acknowledgments

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