feat: register alex walter (#110)

* add person record for Axel Walter

* update publications

src/.vitepress/data/persons/walter-axel.mjs

@@ -0,0 +1,41 @@
+import { TeamID } from "../..";
+import { definePerson } from "../..";
+
+export default definePerson({
+    avatar: '/images/persons/walter-axel.png',
+    name: 'Axel Walter',
+    address: [
+        "Room C322",
+        "Sand 14, Tübingen, Germany 72076"
+    ],
+    email: "[email protected]",
+    team: TeamID.ABI,
+    role: 'PostDoc',
+    socialLinks: [
+        { icon: "github", link: "https://github.com/axelwalter" }
+    ],
+    interests: [
+        "Metabolomics",
+        "Accessible Bioinformatics Workflows",
+        "Microbiology",
+        "LC-MS Method Development"
+    ],
+
+    education: [
+        {
+            year: [2012, 2017],
+            value: "University of Tübingen"
+        },
+        {
+            year: [2017, 2021],
+            value: "PhD in Microbiology, University of Tübingen",
+        },
+    ],
+
+    biography: [
+        {
+            year: 2021,
+            value: "PostDoc, Applied Bioinformatics at the University Tübingen",
+        },
+    ],
+    });

src/.vitepress/data/publications/pub.bib

@@ -1,3 +1,13 @@
+@article{pakkir2024statistical,
+  title={Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data},
+  author={Pakkir Shah, Abzer K and Walter, Axel and Ottosson, Filip and Russo, Francesco and Navarro-Diaz, Marcelo and Boldt, Judith and Kalinski, Jarmo-Charles J and Kontou, Eftychia Eva and Elofson, James and Polyzois, Alexandros and others},
+  journal={Nature protocols},
+  pages={1--71},
+  abstract={Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography–tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography–tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks (https://github.com/Functional-Metabolomics-Lab/FBMN-STATS). Additionally, the protocol is accompanied by a web application with a graphical user interface (https://fbmn-statsguide.gnps2.org/) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.},
+  year={2024},
+  date = {2024-09-20},
+  publisher={Nature Publishing Group UK London}
+}
 @article{pmid38909050,
 title = {A study on interoperability between two Personal Ħealth Ŧrain infrastructures in leukodystrophy data analysis},
 author = {S Welten and M de Arruda Botelho Herr and L Hempel and D Hieber and P Placzek and M Graf and S Weber and L Neumann and M Jugl and L Tirpitz and K Kindermann and S Geisler and L O Bonino da Silva Santos and S Decker and N Pfeifer and O Kohlbacher and T Kirsten},
@@ -78,6 +88,7 @@ @article{pmid37173725
 volume = {15},
 number = {1},
 pages = {52},
+abstract = {Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.},
 keywords = {},
 pubstate = {published},
 tppubtype = {article}