BBSR Best Practices Flyers

Welcome to the repository for best practices flyers developed by the CU Cancer Center Biostatistics and Bioinformatics Shared Resource (BBSR) Bioinformatics Team at the University of Colorado Anschutz Medical Campus. Our mission is to support cutting-edge cancer research by providing investigators with the knowledge and tools needed to design robust sequencing experiments and access world-class analysis support.

🔗 Learn more about our services: BBSR Bioinformatics

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🧬 Available Best Practice Guides

RNA Sequencing (Bulk RNA-seq)

Comprehensive guidance for bulk RNA sequencing experiments, covering experimental design, library preparation considerations, sequencing depth recommendations, and differential expression analysis approaches. Ideal for transcriptome profiling, gene expression studies, and biomarker discovery.

Single-Cell RNA Sequencing (scRNA-seq)

Essential best practices for single-cell RNA sequencing projects, including cell capture strategies, quality control considerations, recommended read depths, and computational analysis workflows. Perfect for heterogeneity studies, cell type identification, and developmental biology research.

DNA Sequencing (DNA-seq)

Expert recommendations for whole genome sequencing (WGS), whole exome sequencing (WES), and targeted sequencing approaches. Covers variant calling strategies, coverage requirements, quality metrics, and germline vs. somatic variant detection considerations.

ChIP-seq, CUT&RUN, and CUT&Tag

Detailed guidelines for chromatin profiling experiments, including traditional ChIP-seq and newer low-input methods like CUT&RUN and CUT&Tag. Includes antibody validation tips, sequencing depth guidance, and peak calling analysis strategies for transcription factor binding and histone modification studies.

Spatial Transcriptomics Omics

Navigate the exciting field of spatial transcriptomics and proteomics with expert guidance on technology selection (Visium, Xenium, CosMx, etc.), sample preparation requirements, experimental design considerations, and spatial analysis workflows. Essential for tissue architecture studies and microenvironment research.

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🌟 General Best Practices for Sequencing Experiments

Regardless of which omics technology you're using, following these core principles will maximize the quality and impact of your research:

1. Plan Early, Consult Often

• Engage with BBSR before starting your experiment – Early consultation can save time, money, and prevent costly experimental design mistakes
• Schedule a consultation to discuss sample size, experimental design, and analysis goals
• Consider pilot studies for new or complex experimental designs

2. Prioritize Experimental Design

• Biological replicates are essential – Technical replicates cannot substitute for biological variation
• Include appropriate controls for your specific experiment type
• Use randomization and blocking strategies to minimize batch effects
• Consider confounding factors (age, sex, tissue heterogeneity) in your design

3. Quality Control at Every Step

• Assess sample quality before library preparation (RNA integrity, DNA purity, cell viability)
• Monitor library quality and quantification accuracy
• Review sequencing QC metrics before proceeding to analysis
• Implement computational QC filtering appropriate to your data type

4. Understand Depth vs. Breadth Trade-offs

• More samples with adequate depth beats fewer samples with excessive depth
• Follow technology-specific sequencing depth recommendations
• Consider your biological question when determining coverage needs
• Budget for additional sequencing if pilot data suggests it's needed

5. Document Everything

• Maintain detailed metadata for all samples (collection date, processing batch, technical variables)
• Document all experimental procedures and deviations
• Keep track of reagent lot numbers and equipment used
• This information is critical for troubleshooting and publication

6. Plan for Data Analysis

• Discuss analysis approaches with bioinformaticians before generating data
• Ensure adequate computational resources and storage are available
• Understand the analysis timeline – complex analyses take time
• Plan for data visualization and interpretation support

7. Follow FAIR Data Principles

• Make your data Findable, Accessible, Interoperable, and Reusable
• Deposit raw sequencing data in appropriate public repositories (GEO, SRA, dbGaP)
• Include comprehensive metadata with your submissions
• Share analysis code and workflows to promote reproducibility

8. Budget Appropriately

• Include costs for: sample preparation, sequencing, data storage, and bioinformatics analysis
• BBSR can provide cost estimates for analysis support
• Consider multi-year costs for large datasets requiring ongoing storage

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💼 BBSR Services & Support

The BBSR Bioinformatics Team offers comprehensive support for all stages of your omics research:

• Pre-submission consultations for grant applications and experimental design
• Custom analysis pipelines tailored to your specific research questions
• Data visualization and interpretation support
• Seminars on bioinformatics tools and best practices
• Long-term collaborative partnerships for complex projects

Contact Us

👉 Visit us: https://medschool.cuanschutz.edu/bioinformaticssr