Profiler: an open web platform for multi-omics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41324558", "kind": "science-record", "revision": 2, "content_hash": "sha256:79743785505d108478992c9b072a6bba7a6afeae6ec22bb0040601be697fb355", "created_at": "2026-11-01T20:31:20Z", "updated_at": "2026-11-01T21:36:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Profiler: an open web platform for multi-omics analysis.", "doi": "10.1093/bioinformatics/btaf644", "pmid": "41324558", "authors": ["Yanis Zirem", "Léa Ledoux", "Isabelle Fournier", "Michel Salzet"], "journal": "Bioinformatics (Oxford, England)", "year": 2026, "abstract": "MOTIVATION: High-throughput multi-omics technologies produce increasingly large and heterogeneous datasets that are difficult to analyze without advanced computational expertise. Existing bioinformatics tools are often fragmented or limited to specific omics types, hindering reproducibility and accessibility. There is a critical need for an integrated, user-friendly, and scalable platform capable of supporting multi-omics analyses across different data modalities.\nRESULTS: We present Profiler, an open-source, modular platform that unifies data import, quality control, preprocessing, statistical testing, machine and deep learning, biomarker discovery, pathway and drug-target enrichment, and survival modeling within a single reproducible environment. Built in Python with Streamlit, Profiler is available as both a web-based platform deployed on high-performance computing and a desktop version for local execution, enabling flexible usage across computational infrastructures. Profiler supports diverse omics modalities, including proteomics, transcriptomics, lipidomics, and electroencephalogram data. Through applications to glioblastoma proteomic, pancancer, and multi-omics datasets, Profiler reproduced known molecular subtypes, revealed potential therapeutic targets, and generated fully traceable analysis reports within minutes. By integrating advanced analytics behind an intuitive interface, Profiler democratizes multi-omics analysis and provides a robust, scalable foundation for systems biology and precision medicine research.\nAVAILABILITY AND IMPLEMENTATION: Profiler is open-source and freely available via its web platform (https://prism-profiler.univ-lille.fr) and GitHub (web version: https://github.com/yanisZirem/Profiler_v1_requests_datatests, desktop version: https://github.com/yanisZirem/prism-profiler), and archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17478158).", "claims": [{"id": "claim-method", "statement": "Our analysis used Kaplan–Meier survival curves and a Cox proportional hazards model to assess survival outcomes and influencing factors for the two distinct groups, A and B.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41324558", "locator": "sections[19] \"3.9 Survival and prognostic modeling\" paragraph 3", "quote": "Our analysis used Kaplan–Meier survival curves and a Cox proportional hazards model to assess survival outcomes and influencing factors for the two distinct groups, A and B."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Profiler is openly accessible at https://prism-profiler.univ-lille.fr. All datasets used in this study are available in the dedicated GitHub repository at https://github.com/yanisZirem/Profiler_v1_requests_datatests in the data_for_peerReview_paper folder. In addition to the datasets used in the article, the repository includes a wide range of real and simulated datasets designed to showcase Profiler’s capabilities across multiple omics platforms. It contains raw MS data acquired from Bruker and Waters instruments (Bruker_data and Waters_data), as well as processed output files from DIA-NN (DIA-NN_data) and MaxQuant (Maxquant_data). The Tabular_data_multi_omics directory offers structured toy datasets specifically created to help users get started with Profiler, test its different modules, and explore its full potential. These datasets, covering lipidomics, proteomics, transcriptomics, and metabolomics, are tailored for binary classification (e.g. aggressive vs. non-aggressive tumors) and multi-class tasks (e.g. tumor, necrotic, and healthy tissues). Additionally, the Survival_data folder contains clinical variables and lipid markers (clinical_and_LipidsMarkers.csv) for Cox regression modeling, as well as preprocessed patient data (statuts_patients.csv) for Kaplan–Meier survival analysis. All data are shared in accessible formats to encourage transparency, reproducibility, and broader adoption by the scientific and educational communities. Furthermore, the same repository provides a Tools directory that hosts the complementary open-source tool MSI2Profiler. The complete source code of the Profiler platform is available in the GitHub repository (https://github.com/yanisZirem/prism-profiler), which also provides detailed instructions for installing the Desktop version. The code is also archived on Zenodo (https://doi.org/10.5281/zenodo.17478158).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41324558", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "Profiler is openly accessible at https://prism-profiler.univ-lille.fr. All datasets used in this study are available in the dedicated GitHub repository at https://github.com/yanisZirem/Profiler_v1_requests_datatests in the data_for_peerReview_paper folder. In addition to the datasets used in the article, the repository includes a wide range of real and simulated datasets designed to showcase Profiler’s capabilities across multiple omics platforms. It contains raw MS data acquired from Bruker and Waters instruments (Bruker_data and Waters_data), as well as processed output files from DIA-NN (DIA-NN_data) and MaxQuant (Maxquant_data). The Tabular_data_multi_omics directory offers structured toy datasets specifically created to help users get started with Profiler, test its different modules, and explore its full potential. These datasets, covering lipidomics, proteomics, transcriptomics, and metabolomics, are tailored for binary classification (e.g. aggressive vs. non-aggressive tumors) and multi-class tasks (e.g. tumor, necrotic, and healthy tissues). Additionally, the Survival_data folder contains clinical variables and lipid markers (clinical_and_LipidsMarkers.csv) for Cox regression modeling, as well as preprocessed patient data (statuts_patients.csv) for Kaplan–Meier survival analysis. All data are shared in accessible formats to encourage transparency, reproducibility, and broader adoption by the scientific and educational communities. Furthermore, the same repository provides a Tools directory that hosts the complementary open-source tool MSI2Profiler. The complete source code of the Profiler platform is available in the GitHub repository (https://github.com/yanisZirem/prism-profiler), which also provides detailed instructions for installing the Desktop version. The code is also archived on Zenodo (https://doi.org/10.5281/zenodo.17478158)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Figure 9A, available as supplementary data at Bioinformatics online, with Kaplan–Meier curves, reveal a significant survival advantage for group A over group B, with a P-value of .00001, indicating this difference is statistically significant.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41324558", "locator": "sections[19] \"3.9 Survival and prognostic modeling\" paragraph 3", "quote": "Figure 9A, available as supplementary data at Bioinformatics online, with Kaplan–Meier curves, reveal a significant survival advantage for group A over group B, with a P-value of .00001, indicating this difference is statistically significant."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Figure 9A, available as supplementary data at Bioinformatics online, with Kaplan–Meier curves, reveal a significant survival advantage for group A over group B, with a P-value of .00001, indicating this difference is statistically significant.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41324558"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41324558", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btaf644", "sha256": "sha256:245b7b6e0731aa30ec56dd8f2cdc88e5a743eba996b825d03e2b3999fab63d06", "bytes": 74186, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btaf644", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12784250", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Our analysis used Kaplan–Meier survival curves and a Cox proportional hazards model to assess survival outcomes and influencing factors for the two distinct groups, A and B."}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["Profiler is openly accessible at https://prism-profiler.univ-lille.fr. All datasets used in this study are available in the dedicated GitHub repository at https://github.com/yanisZirem/Profiler_v1_requests_datatests in the data_for_peerReview_paper folder. In addition to the datasets used in the article, the repository includes a wide range of real and simulated datasets designed to showcase Profiler’s capabilities across multiple omics platforms. It contains raw MS data acquired from Bruker and Waters instruments (Bruker_data and Waters_data), as well as processed output files from DIA-NN (DIA-NN_data) and MaxQuant (Maxquant_data). The Tabular_data_multi_omics directory offers structured toy datasets specifically created to help users get started with Profiler, test its different modules, and explore its full potential. These datasets, covering lipidomics, proteomics, transcriptomics, and metabolomics, are tailored for binary classification (e.g. aggressive vs. non-aggressive tumors) and multi-class tasks (e.g. tumor, necrotic, and healthy tissues). Additionally, the Survival_data folder contains clinical variables and lipid markers (clinical_and_LipidsMarkers.csv) for Cox regression modeling, as well as preprocessed patient data (statuts_patients.csv) for Kaplan–Meier survival analysis. All data are shared in accessible formats to encourage transparency, reproducibility, and broader adoption by the scientific and educational communities. Furthermore, the same repository provides a Tools directory that hosts the complementary open-source tool MSI2Profiler. The complete source code of the Profiler platform is available in the GitHub repository (https://github.com/yanisZirem/prism-profiler), which also provides detailed instructions for installing the Desktop version. The code is also archived on Zenodo (https://doi.org/10.5281/zenodo.17478158)."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:41324558"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:07:02Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 17380, "completion_tokens": 1940, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:41324558", "type": "paper", "label": "pmid:41324558"}, {"id": "source:pmid:41324558", "type": "manuscript", "label": "PMID 41324558 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41324558", "type": "paper", "label": "pmid:41324558"}, {"id": "source:pmid:41324558", "type": "manuscript", "label": "PMID 41324558 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41324558", "type": "extraction", "used": ["source:pmid:41324558"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:41324558", "parent_hash": "sha256:a5ecb2b127c39ce2ea3b9f1930ebd1bf0c2e7b91bc24e700f0dd13c2b5c3bd31", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Our analysis used Kaplan–Meier survival curves and a Cox proportional hazards model to assess survival outcomes and influencing factors for the two distinct groups, A and B."], "order_basis": "analysis_only", "id": "assay:1"}]}