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RNA-Seq Analysis Data Collection Template

1. Raw Sequencing Data​

File Information​

  • FASTQ Files: Location and format of raw sequencing reads (.fastq, .fastq.gz, etc.)
  • File Naming Convention: How are your files named? (e.g., sample_condition_replicate_R1.fastq.gz)
  • File Organization: Are files organized in folders by sample, condition, or other structure?

Sequencing Strategy​

  • Read Type: Single-end (SE) or paired-end (PE) sequencing
  • Number of Files: Total count of FASTQ files
  • File Sizes: Approximate sizes of your largest files (for storage planning)

2. Sequencing Method Details​

Technical Specifications​

  • Sequencing Platform: Illumina, PacBio, Oxford Nanopore, or other
  • Sequencing Instrument: Specific model (e.g., Illumina NovaSeq 6000, HiSeq 4000)
  • Read Length: Average read length and length distribution
  • Sequencing Depth: Expected coverage per sample (e.g., 30M reads per sample)
  • Library Preparation Method:
    • Stranded or unstranded RNA-seq
    • Library preparation kit used
    • Any modifications to standard protocols

Quality Information​

  • RNA Integrity Number (RIN): Available RIN scores for samples
  • Quality Metrics: Any existing quality control data
  • Known Issues: Any batch effects, contamination concerns, or technical problems
  • Adapter Sequences: If known, specify adapter sequences for trimming

3. Species and Reference Genome Information​

Organism Details​

  • Species Name: Scientific name and common name
  • Taxonomic ID: NCBI taxonomy ID (if known)
  • Strain/Subspecies: If applicable, specify strain information

Reference Genome Preferences​

  • Genome Assembly Version: Specific version you want to use
  • Reference Database Preference:
    • RefSeq (NCBI's reference sequence database)
    • Ensembl (EMBL-EBI's genome database)
    • Other (specify)
  • Assembly Accession: Specific genome assembly ID (e.g., GCF_000001405.39 for human GRCh38.p13)
  • Annotation Version: GTF/GFF file version that matches the genome assembly
  • Annotation Source: Same database as genome (e.g., both from Ensembl) or different

Alternative References​

  • Custom Genome: If you have a custom reference genome
  • Transcriptome: If you prefer transcriptome-based quantification
  • Organism Database: Any specific organism annotation database (e.g., org.Hs.eg.db for human)

4. Sample Information​

Experimental Design​

  • Sample IDs: Unique identifiers for each sample
  • Sample Descriptions: Brief descriptions of what each sample represents
  • Experimental Conditions/Groups:
    • Treatment groups (e.g., control vs treated)
    • Time points (e.g., 0h, 6h, 24h)
    • Tissue types or cell lines
    • Any other experimental variables

Replicates and Statistics​

  • Replicate Information:
    • Number of biological replicates per condition
    • Number of technical replicates (if any)
    • Replicate type (biological vs technical)
  • Sample Relationships: How samples are related (e.g., same individual, different time points)

Metadata​

  • Batch Information: Any batch effects or technical variables
  • Sample Collection Details: Date, location, processing method
  • RNA Extraction Method: Kit used, storage conditions
  • Additional Variables: Age, sex, genotype, or other relevant metadata

Sample Sheet Format​

Please provide a tab-separated values (TSV) file with columns:

sample_id	condition	replicate	time_point	tissue	batch	other_metadata
sample_1	control	1	0h	liver	batch1	age_30
sample_2	treatment	1	6h	liver	batch1	age_30

5. Reference Files (if providing your own)​

Genome Files​

  • Reference Genome FASTA: Complete genome sequence file
  • Gene Annotation GTF/GFF: File containing gene models and transcript information
  • Transcriptome FASTA: If using transcriptome-based quantification
  • Index Files: Pre-built index files (if available)

Custom Annotations​

  • Custom Gene Sets: Any specific gene sets relevant to your study
  • Pathway Databases: Custom pathway annotations
  • Regulatory Elements: Promoter regions, enhancers, etc.
  • Custom Annotation Databases: Any organism-specific databases

6. Analysis Parameters​

Differential Expression Analysis​

  • Comparison Groups: Define the groups to be compared
    • Primary comparisons (e.g., treatment vs control)
    • Secondary comparisons (e.g., time course analysis)
  • Statistical Thresholds:
    • False discovery rate (FDR) cutoff (default: 0.05)
    • P-value cutoff (default: 0.05)
    • Log2 fold change threshold (default: 1.0)
  • Expression Thresholds:
    • Minimum read count per gene (default: 10)
    • Minimum expression level for inclusion

Quality Control Parameters​

  • Quality Score Thresholds: Minimum quality scores for base calling
  • Adapter Trimming: Preferences for adapter removal (not currently supported natively by the workflow)
  • Read Filtering: Minimum read length after trimming (not currently supported natively by the workflow)
  • Mapping Quality: Minimum mapping quality scores

Enrichment Analysis Preferences​

  • Gene Set Databases:
    • Gene Ontology (GO): Biological Process, Molecular Function, Cellular Component
    • KEGG pathways
    • Reactome pathways
    • Custom gene sets
  • Enrichment Methods:
    • Over-Representation Analysis (ORA)
    • Gene Set Enrichment Analysis (GSEA)
    • Other methods (specify)
  • Statistical Corrections: Multiple testing correction method (BH, Bonferroni, etc.)

7. Quality Control and Visualization Preferences​

Quality Control​

  • QC Thresholds: Acceptable quality scores and filtering criteria
  • Coverage Requirements: Minimum coverage for gene detection
  • Contamination Checks: Any specific contamination screening needed
  • Batch Effect Correction: Methods for handling batch effects

Visualization and Reporting​

  • QC Plots: Types of quality control plots desired
  • Expression Plots: Volcano plots, MA plots, heatmaps
  • Enrichment Visualizations: Network plots, pathway diagrams
  • Report Format: HTML reports, PDF summaries, interactive dashboards
  • Data Export: RDS files, TSV tables, and HTML reports for further analysis
Interactive Visualization Capabilities

The workflow provides interactive Shiny applications (GeneTonic, pcaExplorer) that enable:

  • Dynamic data exploration through interactive plots and tables
  • Plot customization with interactive controls
  • Direct download of visualizations in multiple formats (PNG, PDF, SVG)
  • Publication-ready figure creation from the interactive interfaces
Workflow Outputs

The workflow generates data files (RDS, TSV, HTML reports) but does not automatically create static plots or visualizations. However, the workflow provides interactive Shiny applications (GeneTonic, pcaExplorer) that allow you to:

  • Explore data interactively through dynamic visualizations
  • Download plots and figures in multiple formats (PNG, PDF, SVG)
  • Create publication-ready images directly from the interactive interfaces
  • Customize visualizations before downloading

You can also use the generated R data objects with R plotting libraries to create custom visualizations.

8. Analysis Goals and Expectations​

Research Questions​

  • Primary Objectives: Main research questions to be addressed
  • Secondary Analyses: Additional analyses of interest
  • Validation Plans: How results will be validated (qPCR, Western blot, etc.)

Output Requirements​

  • File Formats: Preferred output formats (CSV, TSV, Excel, R objects)
  • Data Sharing: Requirements for sharing results with collaborators
  • Reproducibility: Level of detail needed for methods section
  • Integration: Plans to integrate with other datasets or analyses

9. Additional Information​

Previous Analyses​

  • Previous RNA-seq: Any previous RNA-seq analyses on similar samples
  • Expected Results: Any known or expected gene expression changes
  • Control Genes: Housekeeping genes for normalization validation

Special Requirements​

  • Custom Scripts: Any custom analysis scripts to be incorporated
  • External Tools: Any specific tools or databases to be used
  • Publication Standards: Any journal-specific requirements for analysis
  • Data Privacy: Any restrictions on data handling or storage

Submission Checklist​

Please ensure you have provided:

  • Raw FASTQ files or access instructions
  • Sample metadata table (TSV format)
  • Species and genome assembly information
  • Experimental design details
  • Analysis parameters and thresholds
  • Quality control preferences
  • Output format requirements
  • Timeline and deadline information

Next Steps​

Once you've completed this template:

  1. Review with collaborators to ensure all information is accurate and complete
  2. Proceed to Configuration to set up your analysis parameters
  3. Follow the Installation guide to set up the workflow
  4. Use the Running guide to execute your analysis
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Need Help?

If you have questions about filling out this template or need assistance with any section, please contact us or open an issue on our GitHub repository.

Last updated on by benjibromberg