Update citations

_bibliography/citations-eu.bib

@@ -5079,6 +5079,23 @@ @article{gobel_myc_2023
  year = {2023}
 }
 
+@article{goclowski_galaxy_2025,
+ abstract = {Bioinformatics is fundamental to biomedical sciences, but its mastery presents a steep learning curve for bench biologists and clinicians. Learning to code while analyzing data is difficult. The curve may be flattened by separating these two aspects and providing intermediate steps for budding bioinformaticians. Single-cell analysis is in great demand from biologists and biomedical scientists, as evidenced by the proliferation of training events, materials, and collaborative global efforts like the Human Cell Atlas. However, iterative analyses lacking reinstantiation, coupled with unstandardized pipelines, have made effective single-cell training a moving target.To address these challenges, we present a Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for single-cell RNA sequencing (scRNA-seq) analysis, which offers parallel analytical methods using a graphical interface (buttons) or code. With clear, interoperable materials, MIGHTS facilitates smooth transitions between environments. Bridging the biologist–programmer gap, MIGHTS emphasizes interdisciplinary communication for effective learning at all levels. Real-world data analysis in MIGHTS promotes critical thinking and best practices, while FAIR data principles ensure validation of results. MIGHTS is freely available, hosted on the Galaxy Training Network, and leverages Galaxy interfaces for analyses in both settings. Given the ongoing popularity of Python-based (Scanpy) and R-based (Seurat \& Monocle) scRNA-seq analyses, MIGHTS enables analyses using both.MIGHTS consists of 11 tutorials, including recordings, slide decks, and interactive visualizations, and a demonstrated track record of sustainability via regular updates and community collaborations. Parallel pathways in MIGHTS enable concurrent training of scientists at any programming level, addressing the heterogeneous needs of novice bioinformaticians.},
+ author = {Goclowski, Camila L and Jakiela, Julia and Collins, Tyler and Hiltemann, Saskia and Howells, Morgan and Loach, Marisa and Manning, Jonathan and Moreno, Pablo and Ostrovsky, Alex and Rasche, Helena and Tekman, Mehmet and Tyson, Graeme and Videm, Pavankumar and Bacon, Wendi},
+ doi = {10.1093/gigascience/giae107},
+ issn = {2047-217X},
+ journal = {GigaScience},
+ keywords = {{\textgreater}UseGalaxy.eu},
+ month = {January},
+ pages = {giae107},
+ shorttitle = {Galaxy as a gateway to bioinformatics},
+ title = {Galaxy as a gateway to bioinformatics: {Multi}-{Interface} {Galaxy} {Hands}-on {Training} {Suite} ({MIGHTS}) for {scRNA}-seq},
+ url = {https://doi.org/10.1093/gigascience/giae107},
+ urldate = {2025-01-11},
+ volume = {14},
+ year = {2025}
+}
+
 @article{godbole_multiomic_2024,
  abstract = {Medulloblastomas (MBs) are malignant pediatric brain tumors that are molecularly and clinically heterogenous. The application of omics technologies—mainly studying nucleic acids—has significantly improved MB classification and stratification, but treatment options are still unsatisfactory. The proteome and their N-glycans hold the potential to discover clinically relevant phenotypes and targetable pathways. We compile a harmonized proteome dataset of 167 MBs and integrate findings with DNA methylome, transcriptome and N-glycome data. We show six proteome MB subtypes, that can be assigned to two main molecular programs: transcription/translation (pSHHt, pWNT and pG3myc), and synapses/immunological processes (pSHHs, pG3 and pG4). Multiomic analysis reveals different conservation levels of proteome features across MB subtypes at the DNA methylome level. Aggressive pGroup3myc MBs and favorable pWNT MBs are most similar in cluster hierarchies concerning overall proteome patterns but show different protein abundances of the vincristine resistance-associated multiprotein complex TriC/CCT and of N-glycan turnover-associated factors. The N-glycome reflects proteome subtypes and complex-bisecting N-glycans characterize pGroup3myc tumors. Our results shed light on targetable alterations in MB and set a foundation for potential immunotherapies targeting glycan structures.},
  author = {Godbole, Shweta and Voß, Hannah and Gocke, Antonia and Schlumbohm, Simon and Schumann, Yannis and Peng, Bojia and Mynarek, Martin and Rutkowski, Stefan and Dottermusch, Matthias and Dorostkar, Mario M. and Korshunov, Andrey and Mair, Thomas and Pfister, Stefan M. and Kwiatkowski, Marcel and Hotze, Madlen and Neumann, Philipp and Hartmann, Christian and Weis, Joachim and Liesche-Starnecker, Friederike and Guan, Yudong and Moritz, Manuela and Siebels, Bente and Struve, Nina and Schlüter, Hartmut and Schüller, Ulrich and Krisp, Christoph and Neumann, Julia E.},
@@ -7273,6 +7290,20 @@ @article{kohler_msstatsshiny_2023
  year = {2023}
 }
 
+@phdthesis{kohler_novel_2024,
+ author = {Köhler, Anja R.},
+ copyright = {info:eu-repo/semantics/openAccess},
+ keywords = {{\textgreater}UseGalaxy.eu},
+ language = {en},
+ note = {Accepted: 2025-01-09T12:48:00Z
+ISBN: 9781914065286},
+ title = {Novel approaches to investigate the cellular effects of epigenome modifications},
+ type = {{doctoralThesis}},
+ url = {http://elib.uni-stuttgart.de/handle/11682/15507},
+ urldate = {2025-01-12},
+ year = {2024}
+}
+
 @article{kojima_cytochrome_2024,
  abstract = {Austocystin D is a natural compound that induces cytochrome P450 (CYP) monooxygenase-dependent DNA damage and growth inhibition in certain cancer cell lines. Cancer cells exhibiting higher sensitivity to austocystin D often display elevated CYP2J2 expression. However, the essentiality and the role of CYP2J2 for the cytotoxicity of this compound remain unclear. In this study, we demonstrate that CYP2J2 depletion alleviates austocystin D sensitivity and DNA damage induction, while CYP2J2 overexpression enhances them. Moreover, the investigation into genes involved in austocystin D cytotoxicity identified POR and PGRMC1, positive regulators for CYP activity, and KAT7, a histone acetyltransferase. Through genetic manipulation and analysis of multiomics data, we elucidated a role for KAT7 in CYP2J2 transcriptional regulation. These findings strongly suggest that CYP2J2 is crucial for austocystin D metabolism and its subsequent cytotoxic effects. The potential use of austocystin D as a therapeutic prodrug is underscored, particularly in cancers where elevated CYP2J2 expression serves as a biomarker.},
  author = {Kojima, Yukiko and Fujieda, Saki and Zhou, Liya and Takikawa, Masahiro and Kuramochi, Kouji and Furuya, Toshiki and Mizumoto, Ayaka and Kagaya, Noritaka and Kawahara, Teppei and Shin-ya, Kazuo and Dan, Shingo and Tomida, Akihiro and Ishikawa, Fuyuki and Sadaie, Mahito},
@@ -10984,6 +11015,26 @@ @article{perez-sisques_intellectual_2024
  year = {2024}
 }
 
+@article{perez_investigating_2025,
+ abstract = {Within ovarian cancer research, patient-derived xenograft (PDX) models recapitulate histologic features and genomic aberrations found in original tumors. However, conflicting data from published studies have demonstrated significant transcriptional differences between PDXs and original tumors, challenging the fidelity of these models. We employed a quantitative mass spectrometry-based proteomic approach coupled with generation of patient-specific databases using RNA-seq data to investigate the proteogenomic landscape of serially-passaged PDX models established from two patients with distinct subtypes of ovarian cancer. We demonstrate that the utilization of patient-specific databases guided by transcriptional profiles increases the depth of human protein identification in PDX models. Our data show that human proteomes of serially passaged PDXs differ significantly from their patient-derived tumor of origin. Analysis of differentially abundant proteins revealed enrichment of distinct biological pathways with major downregulated processes including extracellular matrix organization and the immune system. Finally, we investigated the relative abundances of ovarian cancer-related proteins identified from the Cancer Gene Census across serially passaged PDXs, and found their protein levels to be unstable across PDX models. Our findings highlight features of distinct and dynamic proteomes of serially-passaged PDX models of ovarian cancer.},
+ author = {Perez, Jesenia M. and Duda, Jolene M. and Ryu, Joohyun and Shetty, Mihir and Mehta, Subina and Jagtap, Pratik D. and Nelson, Andrew C. and Winterhoff, Boris and Griffin, Timothy J. and Starr, Timothy K. and Thomas, Stefani N.},
+ copyright = {2025 The Author(s)},
+ doi = {10.1038/s41598-024-84874-3},
+ issn = {2045-2322},
+ journal = {Scientific Reports},
+ keywords = {{\textgreater}UseGalaxy.eu, Cancer, Cancer genomics, Proteomics},
+ language = {en},
+ month = {January},
+ note = {Publisher: Nature Publishing Group},
+ number = {1},
+ pages = {813},
+ title = {Investigating proteogenomic divergence in patient-derived xenograft models of ovarian cancer},
+ url = {https://www.nature.com/articles/s41598-024-84874-3},
+ urldate = {2025-01-08},
+ volume = {15},
+ year = {2025}
+}
+
 @article{perezriverol_scalable_2019,
  abstract = {The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, bioinformatics analysis is becoming increasingly complex and convoluted, involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are designed as single-tiered software application where the analytics tasks can't be distributed, limiting the scalability and reproducibility of the data analysis. In this paper we summarise the key steps of metabolomics and proteomics data processing, including the main tools and software used to perform the data analysis. We discuss the combination of software containers with workflows environments for large scale metabolomics and proteomics analysis. Finally, we introduce to the proteomics and metabolomics communities a new approach for reproducible and large-scale data analysis based on BioContainers and two of the most popular workflow environments: Galaxy and Nextflow. This article is protected by copyright. All rights reserved},
  author = {Perez‐Riverol, Yasset and Moreno, Pablo},