Ten simple rules for executing an inherited research plan in computational biology

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Trainees often inherit complex computational biology projects with pre-defined plans but incomplete implementation details.
• These inherited plans can stem from grants, collaborations, or legacy systems, leading to an 'execution gap' for new researchers.
• Key elements like assumptions, evaluation criteria, and dependencies are frequently unspecified, hindering reliable execution.

Purpose of the Study:

• To provide a practical framework for stabilizing inherited computational biology projects.
• To address the under-articulated phase of executing inherited research plans in computational biology.
• To help trainees, supervisors, and collaborators navigate project complexities and ensure reproducibility.

Main Methods:

• Organizing familiar practices into a sequenced framework for inherited-plan execution.
• Focusing on stabilizing projects before workflows and decision paths become entrenched.
• Identifying and testing technical or organizational assumptions crucial for project success.

Main Results:

• A practice-oriented framework designed to reduce ambiguity in inherited projects.
• Guidance on clarifying project scope, negotiable elements, and necessary assumptions.
• Strategies for testing feasibility and documenting decisions for equitable execution.

Conclusions:

• Stabilizing inherited computational biology projects is crucial for reproducible research.
• The proposed framework helps trainees manage the complexities of pre-defined research plans.
• Implementing these rules supports efficient, transparent, and equitable project execution in real-world research settings.