VarSeq for somatic variant analysis
1. Preliminary Setup
2. Data Preparation
3. Project Configuration
4. Variant Calling
- Select appropriate variant calling algorithm.
- Load tumor and normal sample data into VarSeq.
- Configure algorithm parameters as per analysis requirements.
- Execute the variant calling process.
- Monitor progress and log any errors.
- Access the variant call results dashboard.
- Evaluate quality metrics such as depth, variant allele frequency.
- Apply filters based on predefined criteria.
- Select variants that meet quality thresholds.
- Document rationale for filtering decisions.
- Choose annotation databases relevant to your study.
- Load databases into VarSeq if external.
- Link variants to corresponding annotations.
- Review annotation results for completeness.
- Update annotation settings as necessary.
- Select tumor and normal samples for comparison.
- Run somatic variant detection algorithms.
- Review differences between tumor and normal variant calls.
- Classify variants as somatic based on detection criteria.
- Export somatic variant results for further analysis.
- Use alignment tools like BWA or Bowtie.
- Select the appropriate reference genome version.
- Adjust parameters for optimal alignment.
- Check for alignment quality metrics.
- Visualize alignments using tools like IGV.
- Calculate average coverage for both samples.
- Ensure minimum depth thresholds are met.
- Identify regions with low coverage or gaps.
- Use tools like GATK DepthOfCoverage.
- Document coverage statistics for reporting.
- Analyze the quality metrics of sequencing data.
- Customize calling parameters such as sensitivity and specificity.
- Consider tumor purity and ploidy in adjustments.
- Test different thresholds for variant calling.
- Review literature for recommended parameter settings.
- Review variant quality scores and read depth.
- Filter variants based on quality thresholds.
- Check for strand bias using allele frequency.
- Identify potential artifacts using known databases.
- Generate QC reports for analysis.
- Combine data from multiple samples for analysis.
- Use tools like Mutect2 for joint calling.
- Analyze shared variants across samples.
- Evaluate batch effects in variant calling.
- Document findings in a comparative analysis.
- Cross-reference variants with databases like dbSNP.
- Apply filters based on allele frequency thresholds.
- Use tools that incorporate population data for filtering.
- Generate a list of known germline variants.
- Ensure filtering criteria are clearly documented.
- Run multiple variant callers on the same dataset.
- Create a Venn diagram to visualize overlaps.
- Analyze discrepancies and concordance rates.
- Document consensus variants for downstream analysis.
- Use statistical measures to assess agreement.
- Create a log of discrepancies with details.
- Categorize discrepancies by type and impact.
- Review potential biological explanations.
- Prepare for further validation or analysis.
- Include discrepancies in final reports.
- Include summary statistics and variant lists.
- Detail methods and parameters used for calling.
- Provide visual representations of key findings.
- Ensure clarity and accessibility of report format.
- Distribute reports to relevant stakeholders.
- Select appropriate control samples for calibration.
- Run variant calling algorithms on control data.
- Adjust algorithm parameters based on control results.
- Validate performance using known variants.
- Document calibration process for transparency.
5. Variant Review & Filtering
- Consult clinical guidelines for relevance.
- Identify variants associated with specific cancers.
- Consider patient history and tumor type.
- Evaluate potential impact on treatment decisions.
- Set thresholds for allele frequency based on population data.
- Assess functional annotations from databases.
- Filter out benign variants based on evidence.
- Use bioinformatics tools to analyze variant impact.
- Use secondary analysis tools for confirmation.
- Cross-reference results with external databases.
- Confirm variant calls with alternate sequencing methods.
- Consider using machine learning models for validation.
- Maintain a detailed record of filtering criteria.
- Log reasons for variant inclusion or exclusion.
- Store documentation in a shared database.
- Ensure clarity for future reviews or audits.
- Search for variants in each database.
- Compile information on allele frequency and clinical significance.
- Check for updates or new findings regularly.
- Use curated annotations to inform decisions.
- Analyze variant distribution across samples.
- Identify subclonal variants and their implications.
- Consider the impact of heterogeneity on treatment.
- Use metrics such as variant allele fraction.
- Compare variant calls between tumor and normal samples.
- Confirm somatic status through filtering.
- Use bioinformatics tools for comparison.
- Document findings in the patient report.
- Identify variants with established clinical actions.
- Rank variants based on treatment relevance.
- Consider clinical trial opportunities for patients.
- Focus on variants linked to targeted therapies.
- Schedule meetings to review significant findings.
- Share insights and interpretations of variants.
- Discuss potential implications for patient management.
- Document feedback from clinical experts.
- Search for recent publications on variants.
- Summarize findings that may impact patient care.
- Stay updated on emerging research in oncology.
- Incorporate relevant literature into reports.
- Develop a classification system for variants.
- Assign categories based on clinical guidelines.
- Document supporting evidence for each category.
- Review classifications regularly for updates.
- Search clinical trial registries for relevant studies.
- Identify novel therapies linked to variants.
- Compile information on drug targets.
- Update findings in patient reports.
- Use visualization tools for comprehensive analysis.
- Display variant data across genomic landscapes.
- Highlight significant variants visually.
- Facilitate discussions with graphical representations.
- Create a centralized log for tracking variants.
- Include rationale for each variant decision.
- Update log continuously with new data.
- Ensure accessibility for future reviews.
6. Interpretation & Reporting
- Evaluate each variant against clinical databases.
- Determine pathogenicity using ACMG guidelines.
- Consider population frequency data.
- Assess potential impact on treatment options.
- Document findings in a clear, concise manner.
- Create visual representations of variant data.
- Include graphs, charts, and tables for clarity.
- Summarize key findings in bullet points.
- Ensure reports are user-friendly and accessible.
- Use consistent formatting for readability.
- Gather detailed family medical histories.
- Document phenotype observations related to variants.
- Cite relevant literature and studies.
- Link findings to clinical context.
- Incorporate any additional notes or observations.
- Familiarize with relevant reporting standards.
- Verify adherence to local and international guidelines.
- Include required disclaimers and acknowledgments.
- Maintain confidentiality and data protection.
- Review reports for compliance before submission.
7. Quality Control & Validation
- Verify data integrity and completeness.
- Check for discrepancies in sample identifiers.
- Ensure proper alignment and variant calling.
- Assess coverage depth and uniformity.
- Document any anomalies for further investigation.
- Select appropriate complementary assays.
- Design experiments to confirm variant presence.
- Use different detection methods for validation.
- Analyze results for consistency with initial data.
- Report any discrepancies and resolve them.
- Document analysis protocols and parameters.
- Re-run analyses with the same dataset.
- Compare results for consistency and accuracy.
- Identify any variables that may affect outcomes.
- Adjust protocols as needed based on findings.
- Identify colleagues or experts in the field.
- Prepare a summary of findings and methodologies.
- Request feedback on analysis and interpretations.
- Incorporate suggestions to enhance robustness.
- Document peer review insights for future reference.
Use in Manifestly