We thank Dr. Amy Skubitz and Dr. Kristin Boylan (University of Minnesota) for the pilot data sets and Dr. Paul Piehowski, Dr. Tao Liu, and Dr. Karin Rodland (Pacific Northwest National Laboratories (PNNL)) for their expertise in the sample collection, and processing of the PTF samples and generation of the TMT-labeled MS data used in this study. This project was funded in part by the Minnesota Ovarian Cancer Alliance (MOCA), the National Institutes of Health/National Cancer Institute Grant Number: 5R01CA262153 (A.P.N.S.), 1R21CA267707 (P.D.J and T.J.G.), and the National Institutes of Health/National Cancer Institute Grant Number: P30CA077598 (P.D.J. and T.J.G.).

The authors declare no conflict of interest.

Clinical metaproteomics research offers potential breakthroughs for clinical studies, but challenges in its implementation persist. The lower abundance of microbial proteins relative to the host proteins in most samples hinders the detection and characterization of non-host proteins6,10. Dependence on large protein sequence databases for accurate peptide and protein identification and quantification, along with complexities of taxonomically and functionally annotating identified peptides and proteins to enable quantitative and statistical analysis also presents obstacles1,3. This clinical metaproteomics workflow within the Galaxy bioinformatics platform addresses many of these challenges. This workflow empowers researchers to analyze their MS data, enabling protein sequence database generation tailored to their clinical samples and matching MS/MS data to protein sequence databases using multiple search algorithms. The inclusion of verification of putative microbial peptide matches to MS/MS spectra provides confidence in the identified peptides and inferred proteins and enables their quantification, visualization, and assignment of taxonomy and functional annotations, followed by statistical analyses.
Tandem Mass Tag (TMT) labeling offers several advantages in clinical metaproteomics and protein quantification in general. This includes a) the simultaneous analysis of multiple samples (as many as 18) in a single mass spectrometry experiment; b) high-quantitative precision; c) high-throughput quantitative capabilities enabling biomarker discovery31,32. However, this workflow can be and has also been used on label-free quantification samples6,20. This workflow, and particularly the generation of the large comprehensive database, does not require exact prior knowledge of microbial composition in the sample. The large comprehensive database should at minimum, include proteins from humans, contaminants, and potential microorganisms present in the sample. This initial database can be built through literature review or, if available, metagenomic information, such as small subunit ribosomal RNA (SSU rRNA) gene sequencing (e.g., 16S rRNA) to classify bacterial taxa20,21. Coupled with this information on sample composition, MS-based metaproteomics identifies and quantifies microbe-expressed proteins and, with parallel characterization of human proteins, provides insights into host-microbiome interactions in disease20,50.
Many standard database searching tools cannot effectively manage databases with millions of sequences frequently encountered in metaproteomics, which leads to decreased peptide identification sensitivity and increases the potential for false positives51. Through the use of MetaNovo, the workflow described here reduces the database size to contain only proteins from microbes most likely to be found in the sample. This reduced database can be searched against MS/MS data using the standard target-decoy analysis and standard database search algorithms with greater sensitivity and false discovery rate (FDR)-controlled protein identification33,52. The target-decoy approach is a common method to estimate FDR (proportion of incorrect PSMs) and consists of adding decoy sequences (scrambled or reversed target sequences) to the target search database53,54. Since these decoys are not expected to be present in the sample, any matches to them are assumed to be incorrect. In turn, the minimum acceptable score is adjusted to maintain the FDR at an acceptable level (usually 1%). This approach yields a list of corrected PSMs that inform what peptides and proteins are most likely present in the sample with high confidence54.
Pairing the target-decoy approach with two complementary database searching programs, SearchGUI/PeptideShaker and MaxQuant, increases the depth of identified peptides and inferred proteins. Additionally, the use of PepQuery2 for peptide verification addresses the challenge of assessing the quality of putative PSMs to microbial peptides, further reducing the potential for false positives and increasing confidence in results. The use of Unipept enables taxonomic and functional annotations while generating quantitative results compatible with MSstatsTMT. This enables statistical analysis for both microbial and human proteins quantified via MaxQuant (Supplementary Figure 1, Supplementary Figure 2, and Supplementary Figure 3). As a value-added feature, not only can this bioinformatics workflow identify and quantify human proteins without requiring additional analysis steps or computational time, but it also holds promising potential for use in clinical studies, such as those investigating secondary infections associated with human infectious diseases and host-microbe interactions underlying disease20,50. 
The current version of clinical metaproteomics workflow should be able to handle most of the contemporary mass spectrometry-based approaches. However, the flexibility of the Galaxy platform to accommodate newly developed software tools combined with creative modification of existing workflows will help overcome any current limitations and address any newer developments in the field. For example, in the Database Generation module, we use the MetaNovo tool to reduce the size of the large publicly available complex databases33. Users can use metagenomics-data-derived database generation methods to generate customized databases for searches. For the Discovery module, users should be able to use FragPipe software which is now available on the Galaxy Toolshed27,55. For the Verification module, users can add and use any additional quality control tools to ensure the veracity of detected microbial peptides. For the Quantification module, we plan to add FragPipe for quantification. We also anticipate the addition of data-independent acquisition (DIA) tools such as EncyclopeDIA56 and other DIA tools on GTN22,23 to the quantification module so that users can perform deeper reproducible quantitative metaproteomics analysis on their DIA-MS data. We currently use MSstats and Unipept for statistical analysis and visualization. In the future, we anticipate users will use the existing metaQuantome57 software as well as modules from the MetaLab58,59 suite of software for data interpretation.
Finally, the implementation of this bioinformatics workflow within the Galaxy ecosystem offers advantages, such as scalability for the management and analysis of large volumes of data requiring compute-intensive analyses. Galaxy is also geared towards flexibility to accommodate developing new software tools and data types that continuously emerge for MS-based proteomics and metaproteomics analysis. Lastly, adoption by the community is enabled by the comprehensive training resources available through the GTN22,24 and supported by publicly available Galaxy instances offering access to the tools and workflow. We are confident that this workflow will facilitate in-depth clinical metaproteomics studies with the potential to transform our understanding of human health and disease.

Mass spectrometry (MS)-based metaproteomics identifies and quantifies microbial and human proteins from clinical samples. This approach provides a new understanding of microbiome responses to disease and uncovers potential mediators of host-microbiome interactions1,2. Although metaproteomic analysis of clinical samples can uncover the microbiome&#39;s interactions with its host environment, the field still faces many challenges. One main challenge is the relatively high abundance of host (human) proteins, which hampers the identification of lower abundant microbial proteins. Moreover, MS-based metaproteomics depends on the use of very large protein sequence databases. These databases comprise microbial proteomes that are present in the sample, which can result in a large database containing millions of sequences. Following the generation of tandem mass spectrometry (MS/MS) spectra from tryptically digested proteins, the MS/MS spectra are searched against large protein sequence databases, matching a peptide sequence to each spectrum (peptide-spectrum match, or PSM). However, sensitivity decreases, and the potential for false positives increases with large databases used for metaproteomics3. Additionally, conserved protein sequences across taxa and insufficient annotation of encoded proteins limit taxonomic and functional annotations for detected peptides and proteins4,5. We present a bioinformatics workflow for effective metaproteomic analysis of clinical samples that addresses many of these challenges and provides accessible software resources for researchers to investigate host-microbiome dynamics underlying human disease.
Clinical metaproteomics has been used to investigate diverse sample types, including feces and vaginal swabs, among others, to decipher pathogenic mechanisms in diseases and conditions6,7,8,9,10,11,12,13,14,15,16,17,18,19,20. Here, we use a metaproteomic bioinformatics workflow to analyze a subset of MS/MS data from Pap test fluid (PTF) samples from ovarian cancer (OVCA) and non-OVCA patients21. The software tools and workflow are accessible via the Galaxy platform, which streamlines the development and execution of complex clinical metaproteomic workflows22,23,24,25. Galaxy is an open-source platform that is designed for bioinformatics and computational biology. It provides a web-based environment for the use of open-source tools and workflows where academic researchers can perform and share complex data analyses. A thriving global community of software developers, data scientists, and end-users maintains the Galaxy ecosystem, including the Galaxy Training Network (GTN; https://training.galaxyproject.org/),&#160;which offers online and on-demand training resources22,23,24,25,26,27. Our workflow aims to reveal a new understanding of host-microbe dynamics in clinical samples as well as generate novel, well-characterized peptide targets of interest for developing targeted MS-based clinical assays for further study of clinical samples6,20,28. Furthermore, this manuscript intends to highlight the clinical metaproteomics workflow methodology. More detailed and beginner-friendly guides are provided in the GTN (https://training.galaxyproject.org/) as it is a valuable resource that can be used in parallel with this manuscript for users seeking additional explanations not covered. The Galaxy community has authored numerous manuscripts to aid beginner users of the Galaxy platform20,21,22,23,24,25,26,27.
All supplementary tables (e.g., tool parameters) and figures (e.g., example plots) for this manuscript have been provided as separate files and are referenced accordingly. Current tool versions within Galaxy version 2.3.0 were used for this manuscript. Therefore, results may differ slightly depending on Galaxy and tool version updates. The Galaxy platform and its tools are open-source and can be used for academic research purposes.

<table><tbody><tr><td>Collapse Collection</td><td>GalaxyP</td><td>Galaxy Version 5.1.1</td><td>Combines a dataset list collection into a single file (in the order of the list)</td></tr><tr><td>Concatenate datasets</td><td>GalaxyP</td><td>Galaxy Version 0.1.1</td><td>Concatenate files tail-to-head</td></tr><tr><td>Cut</td><td>GalaxyP</td><td>Galaxy Version 1.0.2</td><td>Cut (select) specified columns from a file</td></tr><tr><td>FASTA Merge Files and Filter Unique Sequences</td><td>GalaxyP</td><td>Galaxy Version 1.2.0</td><td>Concatenate FASTA database files together</td></tr><tr><td>FastaCLI</td><td>GalaxyP</td><td>Galaxy Version 4.0.41+galaxy1</td><td>Appends decoy sequences to FASTA files</td></tr><tr><td>FASTA-to-Tablular</td><td>GalaxyP</td><td>Galaxy Version 1.1.0</td><td>Convert FASTA-formatted sequences to TAB-delimited format</td></tr><tr><td>Filter</td><td>GalaxyP</td><td>Galaxy Version 1.1.1</td><td>Filter columns using simple expressions</td></tr><tr><td>Filter Tabular</td><td>GalaxyP</td><td>Galaxy Version 3.3.0</td><td>Filter a tabular file via line filters</td></tr><tr><td>Galaxy Europe (EU) server</td><td>GalaxyP</td><td></td><td>https://usegalaxy.eu/</td></tr><tr><td>Group</td><td>GalaxyP</td><td>Galaxy Version 2.1.4</td><td>Group a file by a particular column and perform aggregate functions</td></tr><tr><td>Identification Parameters</td><td>GalaxyP</td><td>Galaxy Version 4.0.41+galaxy1</td><td>Set identification parameters for SearchGUI/PeptideShaker</td></tr><tr><td>Learning Pathway: Clinical metaproteomics workflows within Galaxy</td><td>GalaxyP</td><td></td><td>https://training.galaxyproject.org/training-material/learning-pathways/clinical-metaproteomics.html</td></tr><tr><td>MaxQuant</td><td>GalaxyP</td><td>Galaxy Version 2.0.3.0+galaxy0 (Discovery module); Galaxy Version 1.6.17.0+galaxy4 (Quantification module)</td><td>Quantitative proteomics software package for analysis of large mass spectrometric data files</td></tr><tr><td>MetaNovo</td><td>GalaxyP</td><td>Galaxy Version 1.9.4+galaxy4</td><td>Search MS/MS data against a FASTA database (of known proteins) to produce a targeted database (of matched proteins) for mass spectrometry analysis</td></tr><tr><td>msconvert</td><td>GalaxyP</td><td>Galaxy Version 3.0.20287.2</td><td>Convert and/or filter mass spectrometry files</td></tr><tr><td>MSstatsTMT</td><td>GalaxyP</td><td>Galaxy Version 2.0.0+galaxy1</td><td>R-based package for detection of differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments using tandem mass tag (TMT) labeling</td></tr><tr><td>PepQuery2</td><td>GalaxyP</td><td>Galaxy Version 2.0.2+galaxy0</td><td>Peptide-centric search engine for identification and/or validating known and novel peptides of interest</td></tr><tr><td>PeptideShaker</td><td>GalaxyP</td><td>Galaxy Version 2.0.33+galaxy1</td><td>Interpret results from SearchGUI for protein identification</td></tr><tr><td>Protein Database Downloader</td><td>GalaxyP</td><td>Galaxy Version 0.3.4</td><td>Download specified protein sequences as a FASTA file</td></tr><tr><td>Query Tabular</td><td>GalaxyP</td><td>Galaxy Version 3.3.0</td><td>Load tabular files intoa&amp;nbsp; SQLite database</td></tr><tr><td>Remove beginning</td><td>GalaxyP</td><td>Galaxy Version 1.0.0</td><td>Remove the specified number of (header) lines from a file</td></tr><tr><td>SearchGUI</td><td>GalaxyP</td><td>Galaxy Version 4.0.41+galaxy1</td><td>Run search engines on MGF peak lists and prepare results for input to Peptide Shaker</td></tr><tr><td>Select</td><td>GalaxyP</td><td>Galaxy Version 1.0.4</td><td>Select lines that match an expression</td></tr><tr><td>Unipept</td><td>GalaxyP</td><td>Galaxy Version 4.5.1</td><td>Retrieve UniProt entries and taxonomic information for tryptic peptides</td></tr><tr><td>UniProt</td><td>GalaxyP</td><td>Galaxy Version 2.3.0</td><td>Download proteome as a XML (UniProtXML) or FASTA file from UniProtKB</td></tr></tbody></table>

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult. Other significant challenges are attributed to using very large protein sequence databases, which impedes sensitivity and accuracy during peptide and protein identification from mass spectrometry data in addition to retrieving taxonomy and functional annotations and performing statistical analysis. To address these problems, we present an integrated bioinformatics workflow for mass spectrometry-based metaproteomics that combines custom protein sequence database generation, peptide-spectrum match generation and verification, quantification, taxonomic and functional annotations, and statistical analysis. This workflow also offers characterization of human proteins (while prioritizing microbial proteins), thus offering insights into host-microbe dynamics in disease. The tools and workflow are deployed in the Galaxy ecosystem, enabling the development, optimization, and dissemination of these computational resources. We have applied this workflow for metaproteomic analysis of numerous clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its utility via the analysis of residual fluid from cervical swabs. The complete workflow and accompanying training resources are accessible on the Galaxy Training Network to equip non-experts and experienced researchers with the necessary knowledge and tools to analyze their data.

The general protocol described here was demonstrated on MS/MS files obtained from a subset of PTF samples21. Do et al.21 analyzed four MS/MS files from PTF samples that were collected following procedures described by Boylan et al.29and Afiuni-Zadel et al.30. This workflow prioritizes microbial proteins but offers the flexibility for the characterization of human proteins in parallel with microbial proteins21. This workflow as well as variations, have been implemented in other studies6,20.
To curate a list of relevant species, Do et al. selected 118 taxonomic species from a previous study that investigated the cervical-vaginal microbiome30. This list comprises 117 bacterial species along with the yeast Candida albicans (Supplementary Table 3). Figure 1A and Figure 2A show the generation of a large comprehensive database with 2,595,745 protein sequences and MetaNovo database reduction that generated a database with 1,908 protein sequences33. After merging the MetaNovo database with human and contaminant databases, the reduced database contained 21,289 protein sequences (Figure 2A). This reduced database size is more manageable for database searching tools during peptide identification.
Figure 1B shows the use of SearchGUI/PeptideShaker and MaxQuant for the identification of peptide and protein sequences via sequence database searching22,34,35,36,38,48. By using both programs, a total of 196 distinct microbial peptides were identified with confident PSMs which was more than what was identified by each program (32 from SearchGUI/PeptideShaker and 184 from MaxQuant) (Figure 2B). Using two programs increases the range of detectable peptides, which aids in the identification of microbial peptides that are usually present in low abundance compared to host proteins.
Figure 1C and Figure 2C show the use of PepQuery2 to assess MS/MS evidence for the 196 candidate microbial sequences to confirm microbial origin, leading to the generation of a verified protein sequence database for peptide quantification. PepQuery2 verified 134 microbial peptides which were associated with 73 protein sequences (Figure 2C). The 73 protein sequences were merged with human and contaminant sequences, generating a "compact" database for MaxQuant quantification. Verification is a crucial quality control step that ensures only high-confident microbial proteins will be used for peptide quantification40,41.
MaxQuant quantification consisted of searching the MS/MS data against the compact database to generate a Peptides file with 3,203 peptides42 (Figure 1D and Figure 2D). This Peptides file was filtered and grouped to obtain 155 quantified microbial peptides. The Protein Groups file contained 1,313 protein groups, which included 61 microbial and 1,117 human protein groups. Since the Protein Groups file contains both human and microbial protein groups, this enables the characterization and visualization of both host- and microbe-expressed proteins.
As this workflow prioritizes microbial proteins, outputs for the microbial peptides and proteins will be highlighted. Unipept used its custom lowest common ancestor algorithm to perform taxonomic and functional analyses for the 155 quantified microbial peptides43,44,45(Figure 1E, Figure 2E, and Figure 3). Figure 3A shows the microbial taxonomy tree, which identified Lactobacillus as the most abundant genus. Figure 3B shows the microbial enzyme commission (EC) proteins tree, which classified chemical reactions catalyzed by proteins (from the MS/MS data) that serve as enzymes. The most prominent classification was class two for transferases (33 sequences). MSstatsTMT was employed for statistical analysis for the 61 microbial (and 1,117 human) protein groups quantified by MaxQuant42,46,47 (Figure 1E, Figure 2E, and Figure 4). MSstatsTMT generated volcano and comparison plots for the three sample groups (OVCA, healthy, benign) to identify differentially expressed proteins across sample groups. A volcano plot visualizes the log-fold changes and negative log10 of adjusted P-values for comparisons (ovca-healthy, ovca-benign, benign-healthy) for proteins, and a comparison plot visualizes log2-fold changes and variation of multiple comparisons for proteins. Figure 4A shows a microbial volcano plot comparing OVCA versus benign cases, in which three Lactobacillus proteins (labels ending with "_9LACO") were determined to be downregulated. Figure4B depicts an example of a comparison plot for a microbial protein.
<img alt="Clinical metaproteomics workflow diagram; includes database, discovery, verification, quantification." class="xfigimg" src="/files/ftp_upload/67581/67581fig02.jpg" data-altupdatedat="1788304762000" data-alt="Figure 2"> 
Figure 2: Outputs for Clinical Metaproteomics Workflow Modules. Outputs of the workflow include (A) Database Generation, (B) Discovery, (C) Verification, (D) Quantification, and (E) Data Interpretation. <a href="https://www.jove.com/files/ftp_upload/67581/67581fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Microbial taxonomy and enzyme classification trees; interactive data visualization; UniPept analysis results." class="xfigimg" src="/files/ftp_upload/67581/67581fig03.jpg" data-altupdatedat="1788304762000" data-alt="Figure 3"> 
Figure 3: Unipept Outputs. Using the 155 quantified microbial peptides, Unipept annotates these with taxonomic and functional information, generating a (A) taxonomy tree and a (B) enzyme commission protein tree. <a href="https://www.jove.com/files/ftp_upload/67581/67581fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Microbial expression analysis; Volcano plot, comparison plot; protein regulation, differential expression." class="xfigimg" src="/files/ftp_upload/67581/67581fig04.jpg" data-altupdatedat="1788304762000" data-alt="Figure 4"> 
Figure 4: MSstatsTMT Outputs. Excluding contaminant proteins selected quantified microbial and human protein groups are analyzed by MSstatsTMT to generate (A) a volcano plot (where gray denotes no regulation, red/blue for up-/down-regulated), and (B) a comparison plot. <a href="https://www.jove.com/files/ftp_upload/67581/67581fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1: Microbial protein volcano plots. Examples of volcano plots illustrating the log-fold changes and negative log10 of adjusted P-values for comparisons (ovca-healthy, ovca-benign, benign-healthy) for quantified microbial proteins. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Figure-1.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2: Microbial comparison plots. Examples of comparison plots visualizing log2 fold changes and variation of multiple comparisons for quantified microbial proteins. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Figure-2.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: Human protein volcano plots. Examples of volcano plots illustrating the log-fold changes and negative log10 of adjusted P-values for comparisons (ovca-healthy, ovca-benign, benign-healthy) for quantified human proteins. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Figure-3.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Table 1: Module inputs, software tools, and outputs. This table summarizes the inputs, outputs, and software tools for each module. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%201.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 2: Example Galaxy histories and workflows. This table compiles the Galaxy history links, for example, inputs and outputs as well as workflows. Estimated runtimes for each module are included but will vary based on input data size, tool dependencies, downtimes, maintenance, errors, etc. Note that link contents, such as the number of sequences in databases and tool versions, are current at the time of writing (June 2024) but may vary as Galaxy and associated tools and databases are updated. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%202.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 3: GTN resources. This table contains the GTN links where users will be able to access and perform the analysis as instructed. Note that links are up to date at the time of writing (June 2024) but may change as Galaxy, tools, and databases are updated. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%203.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 4: SearchGUI/PeptideShaker and MaxQuant parameters. This table lists the parameters for database searching for peptide identification. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%204.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 5: MaxQuant Experimental design template for discovery and quantification. This is a template of how the experimental design file should be formatted for MaxQuant peptide identification and quantification. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%205.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 6: PepQuery2 parameters. This table lists the verification criteria for PepQuery2. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%206.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 7: Unipept parameters. This table lists the parameters for Unipept to perform taxonomic and functional annotations. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%207.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 8: MSstatsTMT parameters. This table compiles the parameters for statistical analysis for quantified microbial and human proteins. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%208.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 9: Excerpt from MSstatsTMT annotation file. This table depicts an excerpt from the MSstatsTMT annotation file for each run (RAW file). The complete file (can be accessed via the Zenodo link) includes the same information for all four runs. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%209.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 10: MSstatsTMT comparison matrix file. This is an example of how the comparison matrix for MSstatsTMT should be formatted to include different sample groups. <a href="https://www.jove.com/files/ftp_upload/67581/Supplementary%20Table%2010.xlsx" target="_blank">Please click here to download this File.</a>

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Clinical metaproteomics offers insights into the human microbiome and its contributions to disease. We harnessed the computational power of the Galaxy platform to develop a modular bioinformatics workflow that facilitates complex mass spectrometry-based metaproteomic analysis and characterization of diverse clinical sample types relevant to studies of disease.

a-clinical-metaproteomics-workflow-implemented-within-galaxy

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the ...

Soumyadev Sarkar

The microbiome is ubiquitous and plays a vital role in health and the environment. Unraveling the complexities of the environmental microbiome, including air, soil, and aquatic samples, poses a significant challenge. Similarly, the human microbiome, encompassing skin, oral, and gut microbiomes, is crucial for human health and requires thorough investigation. Methods such as metagenomics, metatranscriptomics, and metabolomics are utilized to comprehend these microbiomes. This Methods Collection seeks protocols and original research articles in this field, aiming to explore cutting-edge research that enhances our understanding of microbial communities in health and the environment.&nbsp;

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A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease