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Gene x study x variant map
An interactive bubble chart — genes on one axis, studies on the other, bubble size scaled to sample size, colored by gene. Hover any bubble for the variant, inheritance pattern, phenotype, and year.
OMICS EVIDENCE SYNTHESIS
Where genomics evidence gets organized across studies, not just plotted once.
Extract GWAS, RNA-seq, or rare-variant case-report data and explore it across your whole study set — gene x study x variant maps, a Manhattan plot, phenotype and inheritance breakdowns, and cross-study variant aggregation.
4+
Visualization types
GWAS, RNA-seq, case reports
Data types supported
p < 5×10⁻⁸
Genome-wide significance
The omics dashboard
Built for exploring genomics literature across studies
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Manhattan plot
A chromosome-grouped -log10(p) view of your extracted variants, with the standard genome-wide significance line at p = 5×10⁻⁸ — grouped by chromosome, not exact base-pair position.
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Variant significance scatter
Every extracted variant plotted by effect size and significance, with a nominal-significance threshold line, so outliers and borderline findings are easy to spot across your study set.
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Phenotype & inheritance breakdowns
Bar and pie breakdowns of phenotype, variant type, and inheritance pattern (recessive, dominant, X-linked) for rare-variant and case-report literature.
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Cross-study variant & gene aggregation
Variants and genes are grouped across your extracted studies with a sample-size-weighted mean effect and the best (minimum) p-value observed — a lightweight cross-study rollup, not a full inverse-variance meta-analysis.
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Pooling guardrails
EvidenceFlow recognizes case-report and rare-variant data and steers you toward the omics dashboard instead of standard statistical pooling, since that kind of data can't be validly pooled the same way as a clinical trial.
Exploration first, honest about what it isn't
This dashboard is built for mapping genomics literature across studies, not for replacing a dedicated GWAS meta-analysis pipeline. The variant and gene aggregation here uses a sample-size-weighted mean effect and best p-value across studies — it does not run inverse-variance weighting or compute heterogeneity statistics the way EvidenceFlow's clinical meta-analysis engine does for dichotomous and continuous outcomes. If you need that level of statistical rigor for a clinical outcome, that's what the meta-analysis engine is for.
See the clinical meta-analysis engine →Related capabilities
Systematic Review Software
Import, screen, and extract structured data from genomics literature the same way as any other systematic review.
Meta-Analysis Software
Full inverse-variance, Mantel-Haenszel, and Peto pooling with heterogeneity statistics — for clinical dichotomous and continuous outcomes.
Evidence Synthesis Software
The full evidence pipeline this omics dashboard is one part of, from literature search to reporting.
FAQ
Frequently asked questions
Does EvidenceFlow run a full GWAS meta-analysis?+
No — it aggregates variants across your extracted studies using a sample-size-weighted mean effect and the best (minimum) p-value observed, then visualizes the result. That's a lighter-weight cross-study rollup, not an inverse-variance-weighted meta-analysis with heterogeneity statistics. For that level of rigor on clinical outcomes, use the dedicated meta-analysis engine.
What genomics data types are supported?+
GWAS-style variant data (beta/odds ratio and p-value per variant), RNA-seq gene-level data (log fold change and significance per gene), and rare-variant or Mendelian genetics case reports (inheritance pattern, phenotype, variant notation).
How does it know what kind of omics study I've extracted?+
A simple heuristic based on which fields are present — p-values and effect estimates suggest a GWAS-style study, while phenotype and inheritance-pattern fields suggest a rare-variant case report — which decides which extraction template and dashboard charts apply.
What does the Manhattan plot actually show?+
Extracted variants grouped by chromosome, plotted by -log10(p-value), with the standard genome-wide significance line at p = 5x10-8. Variants are grouped by chromosome rather than placed at their exact base-pair position.
Can rare-variant case-report data be pooled the same way as GWAS data?+
No, and EvidenceFlow won't try to — it detects case-report and rare-variant data and directs you to the omics dashboard for exploration instead of standard statistical pooling, since case reports can't be validly combined the way trial-level data can.
Explore your genomics literature across studies
Enter GWAS, RNA-seq, or rare-variant case data and see it mapped across genes, studies, and variants — the omics dashboard is free to use; AI-assisted extraction is a paid upgrade.