Merge pull request #3436 from plotly/fix-image-urls

Fix bioinformatics image urls

Author: HammadTheOne

doc/python/bio-alignment-chart.md

@@ -29,10 +29,10 @@ jupyter:
     order: 1
     page_type: u-guide
     permalink: python/alignment-chart/
-    thumbnail: thumbnail/alignment-chart.png
+    thumbnail: thumbnail/alignment_chart.png
 ---
 
-## Alignment Viewer (link to dash alignment section below)
+## Alignment Viewer
 
 The Alignment Viewer (MSA) component is used to align multiple genomic or proteomic sequences from a FASTA or Clustal file. Among its extensive set of features, the multiple sequence alignment viewer can display multiple subplots showing gap and conservation info, alongside industry standard colorscale support and consensus sequence. No matter what size your alignment is, Alignment Viewer is able to display your genes or proteins snappily thanks to the underlying WebGL architecture powering the component. You can quickly scroll through your long sequence with a slider or a heatmap overview.
 

doc/python/bio-manhattanplot.md

@@ -29,16 +29,16 @@ jupyter:
     name: Manhattan Plot
     order: 1
     page_type: u-guide
-    permalink: python/manhattan-plot/ 
-    thumbnail: thumbnail/manhttan-plot.png
+    permalink: python/manhattan-plot/
+    thumbnail: thumbnail/manhttan_plot.png
 ---
 
 ## Manhattan Plot
 ManhattanPlot allows you to visualize genome-wide association studies (GWAS) efficiently. Using WebGL under the hood, you can interactively explore overviews of massive datasets comprising hundreds of thousands of points at once, or take a closer look at a small subset of your data. Hover data and click data are accessible from within the Dash app.
 
 ```python
 import pandas as pd
-import dash_bio 
+import dash_bio
 
 df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/manhattan_data.csv')
 
@@ -53,7 +53,7 @@ Change the color of the points that are considered significant.
 
 ```python
 import pandas as pd
-import dash_bio 
+import dash_bio
 
 
 df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/manhattan_data.csv')

doc/python/bio-volcano-plot.md

@@ -30,15 +30,15 @@ jupyter:
     order: 1
     page_type: u-guide
     permalink: python/volcano-plot/
-    thumbnail: thumbnail/volcano-plot.png
+    thumbnail: thumbnail/volcano_plot.png
 ---
 
 ## VolcanoPlot
 Volcano Plot interactively identifies clinically meaningful markers in genomic experiments, i.e., markers that are statistically significant and have an effect size greater than some threshold. Specifically, volcano plots depict the negative log-base-10 p-values plotted against their effect size.
 
 ```python
 import pandas as pd
-import dash_bio 
+import dash_bio
 
 
 df = pd.read_csv(
@@ -57,7 +57,7 @@ Change the size of the points on the scatter plot, and the widths of the effect
 
 ```python
 import pandas as pd
-import dash_bio 
+import dash_bio
 
 df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/volcano_data1.csv')