← All .science files
pmid:39654143
TREMSUCS-TCGA - an integrated workflow for the identification of biomarkers for treatment success.
Gabor Balogh · Natasha Jorge · Célia Dupain · Maud Kamal · Nicolas Servant · Christophe Le Tourneau · Peter F Stadler · Stephan H Bernhart
Journal of integrative bioinformatics · 2024 doi:10.1515/jib-2024-0031
Paper
complete
Experiment
partial
Data
partial
Analysis
partial
Execution
absent
Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
paper object · 10 fields
authors array · 8 items
claims array · 2 items
[0] object · 6 fields
evidence array · 1 item
[0] object · 3 fields
gaps array · 0 items
experiment object · 8 fields
steps array · 3 items
[0] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
[1] object · 13 fields
parameters object · 0 fields
inputs array · 0 items
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
[2] object · 13 fields
parameters object · 0 fields
inputs array · 0 items
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
materials array · 0 items
instruments array · 0 items
reagents array · 0 items
conditions object · 0 fields
gaps array · 1 item
data object · 3 fields
assets array · 1 item
[0] object · 15 fields
structure object · 4 fields
gaps array · 0 items
analysis object · 4 fields
methods array · 4 items
[0] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
evidence object · 3 fields
[1] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[2] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 1 field
data_shapes array · 1 item
[3] object · 10 fields
dsl_operations array · 2 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
targets array · 0 items
gaps array · 1 item
capability object · 3 fields
requirements array · 1 item
[0] object · 6 fields
assay_families array · 9 items
gaps array · 0 items
readiness object · 8 fields
axes object · 3 fields
method object · 5 fields
required_operations array · 3 items
missing_operations array · 0 items
capability object · 4 fields
data object · 6 fields
accessions array · 0 items
resolved_accessions array · 0 items
statements array · 1 item
can_run object · 3 fields
dsl_operations array · 3 items
assets array · 1 item
steps array · 1 item
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
gaps array · 1 item
review object · 5 fields
machine_critique object · 5 fields
reader object · 5 fields
evidence_check object · 2 fields
usage object · 3 fields
stages array · 1 item
[0] object · 4 fields
gaps array · 3 items
provenance object · 7 fields
entities array · 3 items
activities array · 3 items
[0] object · 4 fields
used array · 3 items
[0] object · 3 fields
[1] object · 3 fields
[2] object · 3 fields
[1] object · 4 fields
agents array · 2 items
lineage object · 4 fields
gaps array · 6 items
assays array · 1 item
[0] object · 11 fields
required_operations array · 3 items
source_hashes object · 1 field
evidence array · 1 item
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39654143", "kind": "science-record", "revision": 4, "content_hash": "sha256:78f744a7d797278c1fdc5a3ecc9f3c0795f459d5b1da174fc934e3f13c0ce1b8", "created_at": "2026-11-01T20:29:56Z", "updated_at": "2026-11-01T22:55:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "TREMSUCS-TCGA - an integrated workflow for the identification of biomarkers for treatment success.", "doi": "10.1515/jib-2024-0031", "pmid": "39654143", "authors": ["Gabor Balogh", "Natasha Jorge", "Célia Dupain", "Maud Kamal", "Nicolas Servant", "Christophe Le Tourneau", "Peter F Stadler", "Stephan H Bernhart"], "journal": "Journal of integrative bioinformatics", "year": 2024, "abstract": "Many publicly available databases provide disease related data, that makes it possible to link genomic data to medical and meta-data. The cancer genome atlas (TCGA), for example, compiles tens of thousand of datasets covering a wide array of cancer types. Here we introduce an interactive and highly automatized TCGA-based workflow that links and analyses epigenomic and transcriptomic data with treatment and survival data in order to identify possible biomarkers that indicate treatment success. TREMSUCS-TCGA is flexible with respect to type of cancer and treatment and provides standard methods for differential expression analysis or DMR detection. Furthermore, it makes it possible to examine several cancer types together in a pan-cancer type approach. Parallelisation and reproducibility of all steps is ensured with the workflowmanagement system Snakemake. TREMSUCS-TCGA produces a comprehensive single report file which holds all relevant results in descriptive and tabular form that can be explored in an interactive manner. As a showcase application we describe a comprehensive analysis of the available data for the combination of patients with squamous cell carcinomas of head and neck, cervix and lung treated with cisplatin, carboplatin and the combination of carboplatin and paclitaxel. The best ranked biomarker candidates are discussed in the light of the existing literature, indicating plausible causal relationships to the relevant cancer entities.", "claims": [{"id": "claim-method", "statement": "For transcriptomic analysis, we employ DESeq2 [25], a tool for differential gene expression analysis based on the negative binomial distribution. For methylation data, we invoke metilene [26] which uses a binary segmentation algorithm combined with a two-dimensional statistical test for DMR detection.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39654143", "locator": "sections[5] \"Overview of the workflow\" paragraph 6", "quote": "For transcriptomic analysis, we employ DESeq2 [25], a tool for differential gene expression analysis based on the negative binomial distribution. For methylation data, we invoke metilene [26] which uses a binary segmentation algorithm combined with a two-dimensional statistical test for DMR detection."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data availability: The raw data can be obtained on request from the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39654143", "locator": "sections[22] \"Footnotes\" paragraph 2", "quote": "Data availability: The raw data can be obtained on request from the corresponding author."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "", "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:39654143"], "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:39654143", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1515/jib-2024-0031", "sha256": "sha256:fdfe7c46d89998b691fbb70e990d07820cdfe8695d43fd3f9b427259e450cdd5", "bytes": 52805, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1515/jib-2024-0031", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11698617", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "deseq2", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "For transcriptomic analysis, we employ DESeq2 [25], a tool for differential gene expression analysis based on the negative binomial distribution. For methylation data, we invoke metilene [26] which uses a binary segmentation algorithm combined with a two-dimensional statistical test for DMR detection."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan Meier"}}, {"id": "method-3", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {"p_value": "s estimated through the p-value of a Cox proportional h"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Cox proportional hazards"}}, {"id": "method-4", "family": "image_quantification", "specific": "segmentation", "label": "Image analysis / quantification", "dsl_operations": ["segment_objects", "quantify_intensity"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["image_stack"], "evidence": {"score": 0.8, "band": "high", "quote": "segmentation"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: deseq2."}, "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": ["Data availability: The raw data can be obtained on request from the corresponding author."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:39654143"], "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": "2026-11-01T19:41:20Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14230, "completion_tokens": 1587, "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:39654143", "type": "paper", "label": "pmid:39654143"}, {"id": "source:pmid:39654143", "type": "manuscript", "label": "PMID 39654143 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39654143", "type": "paper", "label": "pmid:39654143"}, {"id": "source:pmid:39654143", "type": "manuscript", "label": "PMID 39654143 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39654143", "type": "extraction", "used": ["source:pmid:39654143"], "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:39654143", "parent_hash": "sha256:56be84a7fd998530b1706249e54e44804792de552ad9b0b46436b9ac3fb4d6ed", "fork_of": null, "generation": 3}, "gaps": ["execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["For transcriptomic analysis, we employ DESeq2 [25], a tool for differential gene expression analysis based on the negative binomial distribution. For methylation data, we invoke metilene [26] which uses a binary segmentation algorithm combined with a two-dimensional statistical test for DMR detection."], "order_basis": "analysis_only", "id": "assay:1"}]}