ArticleNature biotechnology2023
Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data.
Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
58 citing papers in PubMed, 69 citations in OpenAlex.
- Gut Colonization With Vancomycin-Resistant Enterococcus Shapes the Gut Microbiome in the Intensive Care Unit.The Journal of infectious diseases · 2025Trial
- Gut microbial community and host intestinal gene expression with combined fish oil and soluble corn fiber compared with corn oil and maltodextrin: A randomized crossover trial in healthy older individuals.The American journal of clinical nutrition · 2025Trial
- A phase 2 randomized, placebo-controlled trial of inulin for the prevention of gut pathogen colonization and infection among patients admitted to the intensive care unit for sepsis.Critical care (London, England) · 2025Trial
- Personalized whole-body modeling links gut microbiota to metabolic perturbations in Alzheimer's disease.Gut microbes · 2026Article
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Spatial Heterogeneity of Intratumoral Microbiota and Its Roles in Tumor-Microbiota Interactions and Therapeutic Implications.Pathogens (Basel, Switzerland) · 2026Review
- Uterine microbiome signatures associated with endometriosis.BMC biology · 2026Article
- The gastric ecosystem: assessing resident microbiome vs. transient microorganisms in obesity and following bariatric interventions.NPJ biofilms and microbiomes · 2026Review
- Identify contaminants with decontam on the QIIME 2 Framework.Microbiology resource announcements · 2026Article
- The Phylum Fusobacteriota Is Associated with Colorectal Cancer-Specific Mortality: Results from the Translational Research Program in Cancer Differences across Populations.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- The microbiome across the prostate disease continuum: from health and BPH to prostatitis/CPPS and cancer.Oncogene · 2026Review
- Potential of Clinical Care-Collected Gut Biopsies for Advancing Personalised Medicine in Inflammatory Bowel Disease.United European gastroenterology journal · 2026Review
- CroCoDeEL: accurate control-free detection of cross-sample contamination in metagenomic data.Nature communications · 2026Article
- Intratumoral Microorganisms in Tumors: Current Understanding and Emerging Therapeutic Strategies.MedComm · 2026Review
- Lung microbiota analysis in early-stage lung adenocarcinoma.Microbiology spectrum · 2026Article
- A generalizable cross-continent prediction of esophageal squamous cell carcinoma using the oral microbiome.Communications medicine · 2026Article
- Associations of epidemiologic risk factors with Fusobacterium nucleatum and bacterial alpha diversity in the colorectal tumor-associated microbiota.Cancer causes & control : CCC · 2026Article
- Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective.The Journal of infectious diseases · 2026Review
- Mitigation and detection of putative microbial contaminant reads from long-read metagenomic datasets.Microbial genomics · 2026Article
- Article
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 2 countries.
Funding
Abstract
Sequencing-based approaches for the analysis of microbial communities are susceptible to contamination, which could mask biological signals or generate artifactual ones. Methods for in silico decontamination using controls are routinely used, but do not make optimal use of information shared across samples and cannot handle taxa that only partially originate in contamination or leakage of biological material into controls. Here we present Source tracking for Contamination Removal in microBiomes (SCRuB), a probabilistic in silico decontamination method that incorporates shared information across multiple samples and controls to precisely identify and remove contamination. We validate the accuracy of SCRuB in multiple data-driven simulations and experiments, including induced contamination, and demonstrate that it outperforms state-of-the-art methods by an average of 15-20 times. We showcase the robustness of SCRuB across multiple ecosystems, data types and sequencing depths. Demonstrating its applicability to microbiome research, SCRuB facilitates improved predictions of host phenotypes, most notably the prediction of treatment response in melanoma patients using decontaminated tumor microbiome data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.