Update Home.py

Home.py

@@ -8,14 +8,13 @@
     """
 ## A universal metabolomics tool
 
-This app is based on the [UmetaFlow](https://chemrxiv.org/engage/chemrxiv/article-details/634fb68fdfbd2b6abc5c5fcd) workflow for LC-MS data analysis. UmetaFlow is implemented as a [snakemake pipeline](https://github.com/NBChub/snakemake-UmetaFlow) and as a Python version in [Jupyter notebooks](https://github.com/eeko-kon/pyOpenMS_UmetaFlow) based on [pyOpenMS](https://pyopenms.readthedocs.io/en/latest/index.html).
+This app offers the powerful UmetaFlow **[1]** pipeline for untargeted metabolomics in an accessible user interface. Raw data pre-processing converts raw data to a feature quantification table by feature detection, alignment, grouping, adduct annotation and optional re-quantification of missing values. Features can be annotated by in-house libraries based on MS1 m/z and retention time matching as well as MS2 fragment spectrum similarity as well as with formula, structure and compound classes by SIRIUS. Furthermore, required input files for GNPS Feature Based Molecular Networking can be generated. Besides the untargeted pipeline, this app offers some additional features, such as an interface to explore raw data and metabolite identification and quantification via extracted ion chromatograms based on exact m/z values generated conveniently by an included m/z calculator. For downstream processing statistical analysis can be performed within the app or in the popular [FBmn STATS GUIde](https://github.com/axelwalter/streamlit-metabolomics-statistics) for statistical analyis of metabolomics data **[2]**.
 
-Here, we take the powerful UmetaFlow algorithms in a simple and easy graphical user interface. In contrast to the pipeline for automatic data processing,
-this app is tweaked a bit to be used with smaller to medium sample sets and some manual data interpretation. For example the automatic annotation of features via SIRIUS is omitted.
-Instead we export all the files necessary to run in the SIRIUS GUI tool and manually annotate the result tables via a unique identifier. This method of curated annotation can be interesting if you really want to be confident in your annotations.
-The same applies for GNPS, here you can export all the files required for Feature Based Molecular Networking and Ion Identity Networking.
+**[1]** Kontou, Eftychia E., et al. "UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis." Journal of Cheminformatics 15.1 (2023): 52**.
 
-Besides the core UmetaFlow algorithms in the **Metabolomics** tab, you will find additional tabs for **Extracted Ion Chromatograms** and **Statistics**. The data produced here is fully compatible with the web app for [statistical analyis of metabolomics](https://github.com/axelwalter/streamlit-metabolomics-statistics) data.
+**[2]** Shah, Abzer K. Pakkir, et al. "The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data." (2023).
+
+UmetaFlow is further implemented as a [snakemake pipeline](https://github.com/NBChub/snakemake-UmetaFlow) and as a Python version in [Jupyter notebooks](https://github.com/eeko-kon/pyOpenMS_UmetaFlow) based on [pyOpenMS](https://pyopenms.readthedocs.io/en/latest/index.html).
     """)
 
 if Path("UmetaFlow-App.zip").exists():