Merge branch 'michabirklbauer:master' into master

Author: michabirklbauer

app/layout.jsx

@@ -7,14 +7,17 @@ export const metadata = {
   // Define your metadata here
   // For more information on metadata API, see: https://nextjs.org/docs/app/building-your-application/optimizing/metadata
   title: 'pyXLMS',
-  description: 'User guide and documentation for pyXLMS'
+  description: 'User guide and documentation for pyXLMS',
+  authors: [{ name: 'Micha' }, { name: 'Birklbauer', url: 'https://github.com/michabirklbauer' }],
+  creator: 'Micha Birklbauer',
+  publisher: 'Micha Birklbauer'
 }
 
 const navbar = (
   <Navbar
     logo={
       <>
-        <img src="https://github.com/hgb-bin-proteomics/pyXLMS/raw/master/docs/logo/logo.png" width="48" style={{padding: '0px 10px 0px 0px'}}/>
+        <img src="/logo.png" width="48" height="48" style={{padding: '0px 10px 0px 0px'}}/>
         <h1>pyXLMS</h1>
       </>
     }
@@ -46,6 +49,7 @@ export default async function RootLayout({ children }) {
           docsRepositoryBase="https://github.com/michabirklbauer/pyXLMS-docs/tree/master"
           footer={footer}
           // ... Your additional layout options
+          sidebar={{ defaultMenuCollapseLevel: 1 }}
         >
           {children}
         </Layout>

content/docs/index.mdx

@@ -1,10 +1,26 @@
 # Introduction
 
+<br/>
+<div style={{ textAlign: 'justify' }}>
+
 **pyXLMS** is a python package and web application with graphical user interface that aims to simplify and streamline the intermediate step of
 connecting crosslink search engine results with down-stream analysis tools, enabling researchers even without bioinformatics knowledge to
 conduct in-depth crosslink analyses and shifting the focus from data transformation to data interpretation and therefore gaining biological
 insight.
 
+</div>
+
+![Workflow of pyXLMS"](/workflow.png)
+
+<div style={{ textAlign: 'left' }}>
+
+_**Figure 1:** Overview of available functionality in pyXLMS as of version 1.4.3 [August 2025]._
+
+</div>
+
+<br/>
+<div style={{ textAlign: 'justify' }}>
+
 Currently pyXLMS supports input from seven different crosslink search engines:
 [MaxLynx (part of MaxQuant)](https://www.maxquant.org/),
 [MeroX](https://www.stavrox.com/),
@@ -26,3 +42,5 @@ Down-stream analysis is facilitated by functionality that is directly available
 pyMOL (via [pyXlinkViewer](https://github.com/BobSchiffrin/PyXlinkViewer)),
 ChimeraX (via [XMAS](https://github.com/ScheltemaLab/ChimeraX_XMAS_bundle)),
 or [IMP-X-FDR](https://github.com/vbc-proteomics-org/imp-x-fdr).
+
+</div>

content/docs/plotting/plot_peptide_pair_distribution.mdx

@@ -9,7 +9,7 @@ from pyXLMS import __version__
 print(f"Installed pyXLMS version: {__version__}")
 ```
 <Callout emoji="✓">
-```ansi
+```
     Installed pyXLMS version: 1.3.0
 ```
 </Callout>

content/docs/webapp.mdx

@@ -22,7 +22,17 @@ Additionally, the web application can also be run locally or self-hosted. **This
   - ```bash copy
     cd pyXLMS
     ```
-- Running the [streamlit](https://streamlit.io/) app locally:
+- **\[Recommended\]** Running the [streamlit](https://streamlit.io/) app via [uv](https://docs.astral.sh/uv/) locally:
+  - ```bash copy
+    cd gui
+    ```
+  - ```bash copy
+    pip install uv
+    ```
+  - ```bash copy
+    uv run -- streamlit run streamlit_app.py
+    ```
+- _or_ running the [streamlit](https://streamlit.io/) app natively locally:
   - ```bash copy
     cd gui
     ```

content/index.mdx

@@ -5,7 +5,7 @@ _a python package to process protein cross-linking data_
 
 <div style={{ textAlign: 'justify' }}>
 
-<img src="https://github.com/hgb-bin-proteomics/pyXLMS/raw/master/docs/logo/logo_padded_shadow.png" alt="pyXLMS logo" align="left" width={200} style={{padding: '5px 20px 10px 0px',}}/>
+<Image src="/logo_padded_shadow.png" alt="pyXLMS logo" align="left" width={200} height={200} style={{padding: '5px 20px 10px 0px',}}/>
 
 **pyXLMS** is a python package and web application with graphical user interface that aims to simplify and streamline the intermediate step of
 connecting crosslink search engine results with down-stream analysis tools, enabling researchers even without bioinformatics knowledge to