[New content] Port existing package descriptions from MPIB website to alphapept.org

content/ecosystem/_index.md

@@ -37,7 +37,7 @@ Explore available packages, and checkout **installation instructions**, **tutori
 {{< feature-card 
     title="alphapept" 
     icon="images/ecosystem/alphapept-package.png"
-    url="https://github.com/MannLabs/alphapept.git"
+    url="/ecosystem/packages/alphapept"
     description="An open search engine for data-dependent acquisition (DDA) experiments" 
     github="https://github.com/MannLabs/alphapept.git"
     docs="https://mannlabs.github.io/alphapept/"
@@ -47,7 +47,7 @@ Explore available packages, and checkout **installation instructions**, **tutori
 
 {{< feature-card 
     title="directLFQ" 
-    url="https://github.com/MannLabs/directlfq"
+    url="/ecosystem/packages/directlfq"
     description="Label-free quantification for accurate protein abundance measurements" 
     github="https://github.com/MannLabs/directlfq"
     doi="https://doi.org/10.1016/j.mcpro.2023.100581"
@@ -83,7 +83,7 @@ Explore available packages, and checkout **installation instructions**, **tutori
 {{< feature-card 
     title="alphapeptdeep" 
     icon="images/ecosystem/alphapeptdeep.png"
-    url="https://github.com/MannLabs/alphapeptdeep.git"
+    url="/ecosystem/packages/alphapeptdeep"
     description="Modular Deep learning for shotgun proteomics" 
     github="https://github.com/MannLabs/alphapeptdeep.git"
     docs="https://alphapeptdeep.readthedocs.io/en/latest/"
@@ -101,7 +101,7 @@ Explore available packages, and checkout **installation instructions**, **tutori
 
 {{< feature-card 
     title="alphatims" 
-    url="https://github.com/MannLabs/alphatims.git"
+    url="/ecosystem/packages/alphatims"
     description="Access and visualize LC-TIMS-Q-TOF data" 
     github="https://github.com/MannLabs/alphatims.git"
     doi="https://doi.org/10.1016/j.mcpro.2021.100149"
@@ -138,15 +138,15 @@ Explore available packages, and checkout **installation instructions**, **tutori
 
 {{< feature-card 
     title="alphaviz" 
-    url="https://github.com/MannLabs/alphaviz.git"
+    url="/ecosystem/packages/alphaviz"
     description="Browser-based interactive visualization of processed mass spectrometry data from Bruker instruments"
     github="https://github.com/MannLabs/alphaviz.git"
     doi="https://doi.org/10.1101/2022.07.12.499676"
 >}}
 
 {{< feature-card 
     title="alphamap" 
-    url="https://github.com/MannLabs/alphamap.git"
+    url="/ecosystem/packages/alphamap"
     description="Visual annotation of proteomics data with sequence specific knowledge"
     github="https://github.com/MannLabs/alphamap.git"
     docs="https://mannlabs.github.io/alphamap/"

content/ecosystem/packages/alphamap.md

@@ -0,0 +1,10 @@
+---
+title: "alphamap"
+description: "Visual annotation of proteomics data with sequence specific knowledge"
+date: 2024-07-01
+---
+
+Visual inspection is an integral part in analyzing and interpreting proteomics data. Our AlphaMap software enables the exploration of proteomic datasets on the peptide level. It is possible to evaluate the sequence coverage of any identified protein and its post-translational modifications (PTMs). AlphaMap further integrates all available UniProt sequence annotations as well as information about proteolytic cleavage sites. The functionality of AlphaMap can be accessed via an intuitive graphical user interface or—more flexibly—as a Python package that allows its integration into common analysis workflows for data visualization. AlphaMap produces publication-quality illustrations and can easily be customized to address a given research question. 
+
+- [**GitHub**](https://github.com/MannLabs/alphamap)
+- [**Publication**](https://doi.org/10.1093/bioinformatics/btab674)

content/ecosystem/packages/alphapept.md

@@ -0,0 +1,17 @@
+---
+title: "alphapept"
+description: "A modern, open source data dependent acquisition (DDA) proteomics search engine"
+date: 2024-07-01
+---
+
+<!-- Ported from MPIB website  -->
+AlphaPept is a modern search engine for proteomics data acquired with the data dependent acquisition scheme. It provides a novel ultra-fast data analysis toolbox for MS/MS data analysis. 
+
+This Python library is designed to engage both programmers and end-users by providing an intuitive graphical user interface as well as the opportunity to create highly customized and scalable workflows. Importantly, AlphaPept builds on Python and its scientific stack but is also meant as a collaborative development environment with low barriers to entry for the community to contribute. The code is highly modular and performance-optimized by using just-in-time compilation (JIT) using Numba and efficient parallelization as well as GPU computing. 
+
+We use AlphaPept to build the next generation of MS analysis workflows and rapidly test and implement new ideas as well as adapt algorithms.
+
+
+- [**GitHub**]()
+- [**Documentation**]()
+- [**Publication**]()

content/ecosystem/packages/alphapeptdeep.md

@@ -6,10 +6,14 @@ date: 2024-01-15
 
 **alphapeptdeep** is a cutting-edge software tool for analyzing data-independent acquisition (DIA) mass spectrometry data. It provides researchers with advanced algorithms and intuitive workflows for comprehensive proteome analysis.
 
-## Key Features
+AlphaPeptDeep is a Python framework which allows users to build models to predict any desired property of peptides from scratch with only a few lines of code. Our framework uses a flexible chemical composition encoding for PTMs, thus supporting all UniMod and user-defined modifications. Based on this framework, we built state-of-the-art models to predict fragment intensities, retention time and ion mobility of peptides. The package is built on the open-source and widely used [pytorch](https://pytorch.org) framework, enabling developers to easily extent and build upon our tools.
 
-- **Advanced DIA Analysis**: State-of-the-art algorithms for peptide identification and quantification
-- **High Throughput**: Optimized for large-scale proteomics studies
-- **Quality Control**: Built-in quality assessment and validation tools
-- **Flexible Workflows**: Customizable analysis pipelines for different experimental designs
-- **Integration Ready**: Compatible with popular proteomics software ecosystems
+## Applications 
+With transfer learning, it can well predict spectral libraries for multiplex-DIA ([Thielert  et. al. 2022](https://www.biorxiv.org/content/10.1101/2022.12.02.518917v1)). By using AlphaPeptDeep, we can easily build a model to predict what HLA peptides are present for individuals ([Wahle et al, 2024](https://doi.org/10.1016/j.mcpro.2023.100689)), and then predict personalized spectral libraries to boost HLA-DIA identifications. 
+
+
+
+
+- [**GitHub**](https://github.com/MannLabs/alphapeptdeep)
+- [**Documentation**](https://alphapeptdeep.readthedocs.io/en/latest/)
+- [**Publication**](https://doi.org/10.1038/s41467-022-34904-3)

content/ecosystem/packages/alphatims.md

@@ -0,0 +1,14 @@
+---
+title: "alphatims"
+description: "Read and interact with Bruker LC-TIMS-QTOF raw data"
+date: 2024-07-01
+---
+
+Our AlphaTims software allows researchers to investigate raw Bruker LC-TIMS-QTOF data with billions of data points.
+
+Due to the highly efficient indexing scheme, accessing arbitrary selections of these five-dimensional data becomes almost instantaneous. To ensure portability and not waste future resources we store the indexed data in industry standard HDF files. Owing to the excellent performance of the Holoviews ecosystem and in particular Datashader, visualization is just as fast and very intuitive. AlphaTims is freely available, fully open-source, and can be used through a graphical user interface, command-line interface, or Python module on all major operating systems. It is part of the AlphaPept ecosystem and was built on the same principles: Python language, excellent documentation, ease-of-use, performance, and low-threshold collaborative opportunities.
+
+
+- [**GitHub**](https://github.com/MannLabs/alphatims)
+- [**Publication**](https://doi.org/10.1016/j.mcpro.2021.100149)
+<!-- - [**Documentation**]() -->

content/ecosystem/packages/alphaviz.md

@@ -0,0 +1,10 @@
+---
+title: "alphaviz"
+description: "Browser-based interactive visualization of processed mass spectrometry data from Bruker instruments"
+date: 2024-07-01
+---
+
+AlphaViz is a cutting-edge browser-based interactive visualization tool that enables users to visualize raw mass spectrometry data acquired with Bruker instruments together with the results of different search engines. The AlphaViz dashboard facilitates easy quality control of analyzed samples and a clear inspection of raw data that matches selected peptides or proteins. This is particularly useful for the selection of potential candidates for follow-up studies. 
+
+- [**GitHub**](https://github.com/MannLabs/alphaviz)
+- [**Publication**](https://www.biorxiv.org/content/10.1101/2022.07.12.499676v1)

content/ecosystem/packages/directlfq.md

@@ -1,15 +1,15 @@
 ---
 title: "directLFQ"
 description: "Label-free quantification tool for accurate protein abundance measurements"
-date: 2024-01-15
+date: 2024-07-01
 ---
 
 **directLFQ** is a powerful label-free quantification tool designed for accurate protein abundance measurements in mass spectrometry-based proteomics. It implements advanced statistical methods to provide reliable quantitative results across different experimental conditions.
 
-## Key Features
 
-- **Accurate Quantification**: Advanced algorithms for precise protein abundance measurements
-- **Statistical Rigor**: Built-in statistical validation and normalization methods
-- **Missing Value Handling**: Sophisticated imputation strategies for incomplete data
-- **Batch Effect Correction**: Tools to handle technical variation across experiments
-- **Visualization**: Comprehensive plotting functions for data exploration
+Label free quantification is a central step to current proteomics workflows. At the same time, this crucial step has been one of the major bottlenecks for large proteomic studies. DirectLFQ is a novel ratio-based approach for efficient sample normalization and protein intensity calculation. Unlike existing label-free quantification algorithms, directLFQ scales linearly with sample numbers, allowing rapid analysis of large datasets in minutes. The method outperforms previous state-of-the-art algorithms in terms of speed while providing the same accuracy, quantifying 10,000 proteomes in 10 minutes and 100,000 proteomes in under 2 hours, making it 1000-fold faster.
+
+DirectLFQ is compatible with all major DDA and DIA search engines and freely available on Github.
+
+- [**GitHub**](https://github.com/MannLabs/directlfq)
+- [**Publication**](https://doi.org/10.1016/j.mcpro.2023.100581)