Yehia Mokhtar Farag

• Farag, Yehia Mokhtar; Søgaard, Henrik Øvrebø & Barsnes, Harald (2026). Automatic Selection of Search Parameter Values for Mass Spectrometry-Based Search Engines. Journal of Proteome Research. ISSN 1535-3893. 25(2), s. 662–671. doi: 10.1021/acs.jproteome.5c00641. Fulltekst i vitenarkiv Vis sammendrag

  A crucial step in processing mass spectrometry-based proteomics data is identifying and quantifying the proteins in the sample. While the existing search engines can easily match tandem mass spectra to peptide sequences, selecting the most appropriate search parameters can often be challenging and time-consuming due to the diversity of the data sets and the long list of available parameter values to choose from. This study introduces QuickSearchProt─an algorithm aimed at assisting in the selection of search parameter values across search engines, considering not only the data set specifications but also the properties of the search algorithms. By relying on a small representative subset of the spectra, the algorithm can process most data sets within minutes, largely independent of the size of the original data set. The current implementation supports two common search engines, X! Tandem and Sage, and is designed to process data-dependent acquisition (DDA) proteomics data sets but due to its adaptability and scalability can easily be extended to additional search engines. QuickSearchProt, including a graphical user interface, the complete source code, and additional details are freely available at http://www.github.com/barsnes-group/QuickSearchProt.

• Farag, Yehia Mokhtar; Marcos, Carlos Horro; Vaudel, Marc & Barsnes, Harald (2021). PeptideShaker Online: A User-Friendly Web-Based Framework for the Identification of Mass Spectrometry-Based Proteomics Data. Journal of Proteome Research. ISSN 1535-3893. 20(12), s. 5419–5423. doi: 10.1021/acs.jproteome.1c00678. Fulltekst i vitenarkiv Vis sammendrag

  Mass spectrometry-based proteomics is a high-throughput technology generating ever-larger amounts of data per project. However, storing, processing, and interpreting these data can be a challenge. A key element in simplifying this process is the development of interactive frameworks focusing on visualization that can greatly simplify both the interpretation of data and the generation of new knowledge. Here we present PeptideShaker Online, a user-friendly web-based framework for the identification of mass spectrometry-based proteomics data, from raw file conversion to interactive visualization of the resulting data. Storage and processing of the data are performed via the versatile Galaxy platform (through SearchGUI, PeptideShaker, and moFF), while the interaction with the results happens via a locally installed web server, thus enabling researchers to process and interpret their own data without requiring advanced bioinformatics skills or direct access to compute-intensive infrastructures. The source code, additional documentation, and a fully functional demo is available at https://github.com/barsnes-group/peptide-shaker-online.

• Guldbrandsen, Astrid; Farag, Yehia Mohamed Mokhtar; Lereim, Ragnhild Reehorst; Berven, Frode & Barsnes, Harald (2019). Essential Features and Use Cases of the Cerebrospinal Fluid Proteome Resource (CSF-PR). I Santamaría, Erique & Fernández-Irigoyen, Joaquín (Red.), Cerebrospinal Fluid (CSF) Proteomics. Humana Press. ISSN 9781493997060. s. 377–391. doi: 10.1007/978-1-4939-9706-0_25. Fulltekst i vitenarkiv Vis sammendrag

  Every year, a large number of published studies present biomarkers for various neurological disorders. Many of these studies are based on mass spectrometry proteomics data and describe comparison of the abundance of proteins in cerebrospinal fluid between two or more disease groups. As the number of such studies is growing, it is no longer straightforward to obtain an overview of which specific proteins are increased or decreased between the numerous relevant diseases and their many subcategories, or to see the larger picture or trends between related diseases. To alleviate this situation, we therefore mined the literature for mass spectrometry–based proteomics studies including quantitative protein data from cerebrospinal fluid of patients with multiple sclerosis, Alzheimer’s disease, and Parkinson’s disease and organized the extracted data in the Cerebrospinal Fluid Proteome Resource (CSF-PR). CSF-PR is freely available online at http://probe.uib.no/csf-pr, is highly interactive, and allows for easy navigation, visualization, and export of the published scientific data. This chapter will guide the user through some of the most important features of the tool and show examples of the suggested use cases.

• Guldbrandsen, Astrid; Farag, Yehia Mohamed Mokhtar; Kroksveen, Ann Cathrine; Oveland, Eystein; Lereim, Ragnhild Reehorst & Opsahl, Jill Anette [Vis alle 9 forfattere av denne artikkelen] (2016). CSF-PR 2.0: an interactive literature guide to quantitative cerebrospinal fluid mass spectrometry data from neurodegenerative disorders. Molecular & Cellular Proteomics. ISSN 1535-9476. 16(2), s. 300–309. doi: 10.1074/mcp.O116.064477. Fulltekst i vitenarkiv Vis sammendrag

  The rapidly growing number of biomedical studies supported by mass spectrometry based quantitative proteomics data has made it increasingly difficult to obtain an overview of the current status of the research field. A better way of organizing the biomedical proteomics information from these studies and making it available to the research community is therefore called for. In the presented work, we have investigated scientific publications describing the analysis of the cerebrospinal fluid proteome in relation to multiple sclerosis, Parkinson's disease and Alzheimer's disease. Based on a detailed set of filtering criteria we extracted 85 data sets containing quantitative information for close to 2000 proteins. This information was made available in CSF-PR 2.0 (http://probe.uib.no/csf-pr-2.0), which includes novel approaches for filtering, visualizing and comparing quantitative proteomics information in an interactive and user-friendly environment. CSF-PR 2.0 will be an invaluable resource for anyone interested in quantitative proteomics on cerebrospinal fluid.

• Aasebø, Elise; Mjaavatten, Olav; Vaudel, Marc; Farag, Yehia Mohamed Mokhtar; Selheim, Frode & Berven, Frode [Vis alle 8 forfattere av denne artikkelen] (2016). Freezing effects on the acute myeloid leukemia cell proteome and phosphoproteome revealed using optimal quantitative workflows. Journal of Proteomics. ISSN 1874-3919. 145, s. 214–225. doi: 10.1016/j.jprot.2016.03.049. Fulltekst i vitenarkiv
• Farag, Yehia Mohamed Mokhtar; Berven, Frode; Jonassen, Inge; Petersen, Kjell & Barsnes, Harald (2015). Distributed and interactive visual analysis of omics data. Journal of Proteomics. ISSN 1874-3919. 129, s. 78–82. doi: 10.1016/j.jprot.2015.05.029. Fulltekst i vitenarkiv
• Guldbrandsen, Astrid; Vethe, Heidrun; Farag, Yehia Mohamed Mokhtar; Oveland, Eystein; Garberg, Hilde Kristin & Berle, Magnus Foldal [Vis alle 10 forfattere av denne artikkelen] (2014). In-depth characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR). Molecular & Cellular Proteomics. ISSN 1535-9476. 13(11), s. 3152–3163. doi: 10.1074/mcp.M114.038554. Fulltekst i vitenarkiv Vis sammendrag

  In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.

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• Farag, Yehia Mohamed Mokhtar (2025). User-friendly analysis and interactive visual exploration approaches for proteomics data. Universitetet i Bergen. ISSN 9788230843352. Fulltekst i vitenarkiv Vis sammendrag

  The vast number of proteomics studies, the lack of standardized procedures, and the incomplete reported results often limit data exploration and reuse. Additionally, proteomics studies provide a wide range of information regarding both the underlying research and the generated results. Utilizing user-friendly and interactive visual data exploration can help mitigate many of these challenges. This thesis proposes novel approaches for organizing and unifying data from published proteomics studies while integrating complementary information using existing proteomics knowledgebases. Thus, it facilitates cross-study comparisons, enhances data accessibility and reusability, and finally allows for easy exploration of the data. Interactive visual exploration for proteomics studies is put forward as a central concept to simplify data complexity and improve data interpretation. Dividing the data into levels and providing coordinated interactive visualization for each level allows for straightforward data exploration, reduced complexity, and better data interpretation. New methods and utilities that simplify and improve proteomics analysis and thus maximize the knowledge gained are also explored. This encompasses handling the data and the analysis processes in more straightforward and user-friendly ways that do not require programming, command line skills, or software installation, while adjusting and supporting the analysis process, ensuring efficient data analysis and improved data interpretation. The outcomes of this study propose innovative strategies for managing and analyzing proteomics data in an interactive and user-friendly manner. Such strategies aim to improve data analysis, interpretation, and storage, thereby expanding biomedical knowledge, especially its application in enhancing diagnostics and treatment approaches.

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