Interactive Dashboards for Genomic Data Exploration with Plotly and Dash

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Introduction

Genomic datasets are big, multidimensional, and interactive by nature—perfect candidates for dashboards. Plotly and Dash let you turn GWAS hits, RNA‑seq results, and sequence annotations into intuitive, web‑based apps that anyone on your team can explore without installing heavy tools or writing code. Dash’s Python-first API plus Plotly’s interactive charts keep your focus on the science, not the front end.

Why Plotly Dash for genomic data

  • Pure Python workflow: build full web apps—layouts, callbacks, and charts—without JavaScript.
  • Bioinformatics-ready components: Dash Bio ships specialized visuals (e.g., sequence viewers, volcano and Manhattan plots) that map directly to common genomics tasks. 
  • Interactivity out of the box: zoom, hover, select points, and trigger downstream updates for drill-down analysis.

Genomics-friendly components you can use today

  • VolcanoPlot for differential expression: visualize −log10(p) vs. effect size, set genome-wide thresholds, and let users filter by effect range to surface meaningful hits.
  • SequenceViewer for annotations: render FASTA sequences with selections and badges; connect selections to metadata panes or BLAST links.
  • Manhattan plots for GWAS scans: interactively pan/zoom across chromosomes and highlight significant loci with WebGL speed.

A minimal Dash app example

Below is a compact app that couples a Volcano plot with a sequence viewer. Replace the example CSV/FASTA with your own results and reference sequences.

# pip install dash dash-bio pandas
import pandas as pd
from dash import Dash, html, dcc, Input, Output
import dash_bio as dashbio
import urllib.request as urlreq

# Example data
df = pd.read_csv("https://git.io/volcano_data1.csv")  # gene, p, log2FC, etc.
fasta = urlreq.urlopen("https://git.io/sequence_viewer_P01308.fasta").read().decode("utf-8")
seq = fasta.split("\n", 1)[1].replace("\n", "")  # naive FASTA parse for demo

app = Dash(__name__)
app.layout = html.Div([
    html.H3("Genomic Explorer"),
    dcc.RangeSlider(-3, 3, 0.1, id="effect", value=[-1, 1]),
    dcc.Graph(id="volcano"),
    html.Div("Selected gene sequence:"),
    dashbio.SequenceViewer(id="seq", sequence=seq, title="Example Protein", badge=False)
])

@app.callback(Output("volcano", "figure"), Input("effect", "value"))
def update_volcano(effects):
    return dashbio.VolcanoPlot(
        dataframe=df,
        effect_size_line=effects,
        genomewideline_value=2.5
    )

if __name__ == "__main__":
    app.run_server(debug=True)

VolcanoPlot parameters like effect_size_line and genomewideline_value let users set scientific thresholds from the UI, while SequenceViewer exposes mouseSelection for downstream callbacks.

Performance and deployment tips

  • Precompute and cache: store p‑values, transforms, and annotations server-side (e.g., with Flask caching) to keep callbacks snappy on large cohorts.
  • Use WebGL-backed figures: Plotly’s high‑density rendering helps with hundreds of thousands of points typical in GWAS and eQTL studies.
  • Paginate detail views: show overview plots first; load read‑level or exon‑level details on demand to avoid transferring huge objects.
  • Ship it: start with local hosting; when ready, deploy on your platform of choice (e.g., containerized on cloud) and secure with auth.

Summary

Plotly and Dash make genomic data exploration approachable: scientists get interactive volcano and Manhattan plots for discovery, sequence viewers for context, and Python callbacks to connect everything. Start small—wire a single plot to a filter—then iterate toward a full notebook‑to‑dashboard workflow your collaborators will actually use.

Further reading
– Dash Bio documentation (components for genomics, sequences, and structure): https://dash.plotly.com/dash-bio
– Dash Bio VolcanoPlot examples and reference: https://dash.plotly.com/dash-bio/volcanoplot
– Dash Bio SequenceViewer examples and reference: https://dash.plotly.com/dash-bio/sequenceviewer

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