Update DDA_Quant.py

webinterface/pages/DDA_Quant.py

@@ -74,7 +74,52 @@ def generate_input_field(self, input_format: str, content: dict):
 
     def _main_page(self):
         """Format main page."""
-        st.title("Proteobench")
+        st.title("Module 2: DDA quantification")
+        st.header("Description of the module")
+        st.markdown("""
+                    This module compares the MS1-level quantification tools for
+                    data-dependent acquisition (DDA). The raw files provided for
+                    this module are presented in the comprehensive LFQ benchmark
+                    dataset from [Van Puyvelde et al., 2022](https://www.nature.com/articles/s41597-022-01216-6).
+                    The samples contain tryptic peptides from Homo sapiens,
+                    Saccharomyces cerevisiae, and Escherichia coli, mixed in different
+                    ratios (condition A and condition B), with three replicates of each
+                    condition. With these samples, we calculate three metrics:  
+                    - To estimate the sensitivity of the workflows, we report the
+                    number of unique precursors (charged modified sequence) quantified
+                    in all 6 runs.  
+                    - To estimate the accuracy of the workflows, we report the weighted
+                    sum of precursor deviation from expected ratios.  
+                    - To estimate the precision of the workflows, we report the weighted
+                     average of the interquartile range (IQR) of the precursors ratio.  
+
+                    ProteoBench plots these three metrics to visualize workflow outputs
+                     from different tools, with different versions, and/or different
+                    sets of parameters for the search and quantification.
+                    The full description of the pre-processing steps and metrics
+                    calculation is available here: LINK.
+                    """)
+        st.header("Downloading associated files")
+        st.markdown("""
+                    The raw files used for this module were acquired on an Orbitrap
+                    Q-Exactive H-FX (ThermoScientific). They can be downloaded from the
+                    proteomeXchange repository PXD028735. You can download them here:
+                    [LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.raw](https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.raw)
+                    [LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.raw](https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.raw)
+                    [LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.raw](https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.raw)
+                    [LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.raw](https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.raw)
+                    [LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.raw](https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.raw)
+                    [LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.raw](https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.raw)  
+
+                    **It is imperative not to rename the files once downloaded!**
+                    """)
+        st.markdown("""
+                    Download the fasta file here: [TODO]  
+                    The fasta file provided for this module contains the three species
+                    present in the samples and contaminant proteins
+                    ([Frankenfield et al., JPR](https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00145))
+                    """)
+
         st.header("Input and configuration")
 
         with st.form(key="main_form"):