style changes and plot update

RNAdist/dashboard/app.py

@@ -74,7 +74,7 @@ def get_navbar():
 
                 dbc.Collapse(
                     [
-                        dbc.NavItem(dbc.NavLink(f"{page['name']}", href=page["relative_path"], id={"type": f"nav-item", "index": idx}), className="p-1") for
+                        dbc.NavItem(dbc.NavLink(f"{page['name']}", href=page["relative_path"], id={"type": f"nav-item", "index": idx}), className="p-1 nav-link-white") for
                         idx, page in enumerate(dash.page_registry.values())
                     ],
                     is_open=False,

RNAdist/dashboard/assets/tableFix.css

@@ -4,6 +4,18 @@
 	background: #fff;
 }
 
+.nav-link {
+    color: white !important;
+}
+
+.warning {
+    border-left: 4px solid var(--bs-info);
+    background-color: var(--bs-info-bg);
+    padding: 10px;
+    margin: 10px 0;
+    border-radius: 4px;
+}
+
 
 .dash-spreadsheet-container .dash-spreadsheet-inner table .dash-cell.active {
     background-color: inherit !important;

RNAdist/dashboard/index.md

@@ -2,8 +2,29 @@
 
 This is the RNAdist Webserver used to calculate nucleotide distances on the ensemble of RNA secondary structures.
 
-In the Navbar at the top you can find and Input field to set your session token. Everything you compute here will be
-stored using this token. If you want to access your data later store your token somewhere.
+## What does it do
+The thermodynamic ensemble of RNA secondary structures refers to the collection of all possible conformations an RNA 
+molecule can adopt, each associated with a specific probability determined by its free energy.
 
-Note that data will be stored no longer than 7 days and there is the possibility that it gets deleted even earlier 
-depending on website traffic. Further all our jobs will be public. Meaning everyone that knows your token can access it.
+Each RNA secondary structure can be represented as a graph, where nucleotides are nodes and connections are edges. 
+For simplicity, both backbone links and hydrogen bonds between base pairs are assigned an edge distance of 1, allowing 
+the structure to be analyzed using standard graph-theoretical methods.
+
+By sampling structures from the RNA thermodynamic ensemble, one can compute the distances between any two nucleotides
+$i$ and $j$ across the sampled graphs. This generates a distribution of distances, reflecting how often different spatial 
+separations occur in the ensemble and providing insights into the flexibility and connectivity of the RNA molecule.
+
+
+## How to use
+Use the Navbar at the top to enter your session token. All computations will be associated with this token, so make 
+sure to save it if you want to access your data later.
+
+Next, go to the [**Submission**](/submission) page to enter your RNA sequence and folding/sampling parameters. 
+Once your job is complete, it will appear as "finished" in the submissions table.
+
+Finally, visit the [**Visualization**](/visualization) page to explore nucleotide distance distributions and view the 
+sampled RNA structures.
+<div class="warning">
+<strong>Warning:</strong> Note that data will be stored no longer than 7 days and there is the possibility that it gets deleted even earlier 
+depending on website traffic. Further all our jobs will be public. Meaning everyone that knows your token can access it.
+</div>

RNAdist/dashboard/pages/index.py

@@ -21,7 +21,7 @@ def welcome_layout(text):
                     [
                         html.Div(
                             [
-                                dcc.Markdown(text, dangerously_allow_html=True, ),
+                                dcc.Markdown(text, dangerously_allow_html=True, mathjax=True),
                             ]
                         )
                     ]

RNAdist/dashboard/pages/visualization.py

@@ -487,7 +487,8 @@ def plot_histo(seq_hash, i, j, switch):
         fig = plot_distances_with_running_j(matrix, i-1, mfe=mfe)
         fig.update_layout(legend=dict(orientation="h"))
     else:
-        fig = distance_histo_from_matrix(matrix, i-1, j-1)
+        fig = distance_histo_from_matrix(matrix, i-1, j-1, vertical_spacing=0, row_heights=[0.8, 0.2])
+        fig.update_layout(showlegend=False)
     fig.update_layout({"margin": {"b": 20, "r": 10, "t": 10, "l": 10}})
     if not switch:
         fig.update_layout(DARK_LAYOUT)

RNAdist/plots/sampling_plots.py

@@ -3,6 +3,8 @@
 import re
 import time
 
+from plotly.subplots import make_subplots
+
 
 def empty_figure(annotation: str = None):
     fig = go.Figure()
@@ -39,18 +41,52 @@ def empty_figure(annotation: str = None):
     )
     return fig
 
-def distance_histo_from_matrix(distances, i, j, color:str ="#00a082"):
-    fig = go.Figure()
+def distance_histo_from_matrix(distances, i, j, color:str ="#00a082", **kwargs):
+    total_counts = distances[0, 0, 0]
+
+    hist = distances[i, j]
+    q1 = histogram_quantile(hist, 0.25, total_counts)
+    median = histogram_quantile(hist, 0.5, total_counts)
+    q3 = histogram_quantile(hist, 0.75, total_counts)
+    iqr = q3 - q1
+    d_min = hist.nonzero()[0].min()
+    d_max = hist.nonzero()[0].max()
+    lower_whisker = max(d_min, q1 - 1.5 * iqr)
+    upper_whisker = min(d_max, q3 + 1.5 * iqr)
+    x = np.arange(hist.shape[-1])
+    weighted_sum = np.sum(hist * x)
+    ed = weighted_sum / total_counts
+
+    fig = make_subplots(rows=2, shared_xaxes=True, **kwargs)
     fig.add_trace(
         go.Bar(
-            x=np.arange(distances.shape[-1]),
-            y=distances[i, j] / distances[i, j].sum(),
+            x=x,
+            y=hist / hist.sum(),
             marker=dict(color=color),
-        )
+            name="Histogram",
+        ),
+        row=1, col=1
+    )
+    fig.add_trace(
+        go.Box(
+            lowerfence=[float(lower_whisker)],
+            q1=[float(q1)],
+            median=[float(median)],
+            q3=[float(q3)],
+            upperfence=[float(upper_whisker)],
+            mean=[float(ed)],
+            name=f'Box',
+            marker=dict(color=color),
+            y=["Distribution"],
+            boxpoints=False  # hide individual points
+        ),
+        row=2, col=1
     )
     fig.update_layout(
-        xaxis=dict(title="Distance [nt]"),
-        yaxis=dict(title="Probability")
+        xaxis2=dict(title="Distance [nt]", showgrid=True),
+        xaxis=dict(showgrid=True),
+        yaxis=dict(title="Probability", showgrid=True),
+        yaxis2=dict(showticklabels=False),
     )
     return fig