The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.
Colorectal Cancer and the Human Gut Microbiome: Reproducibility with Whole-Genome Shotgun Sequencing
Open accessAbstract
Accumulating evidence indicates that the gut microbiota affects colorectal cancer development, but previous studies have varied in population, technical methods, and associations with cancer. Understanding these variations is needed for comparisons and for potential pooling across studies. Therefore, we performed whole-genome shotgun sequencing on fecal samples from 52 pre-treatment colorectal cancer cases and 52 matched controls from Washington, DC. We compared findings from a previously published 16S rRNA study to the metagenomics-derived taxonomy within the same population. In addition, metagenome-predicted genes, modules, and pathways in the Washington, DC cases and controls were compared to cases and controls recruited in France whose specimens were processed using the same platform. Associations between the presence of fecal Fusobacteria, Fusobacterium, and Porphyromonas with colorectal cancer detected by 16S rRNA were reproduced by metagenomics, whereas higher relative abundance of Clostridia in cancer cases based on 16S rRNA was merely borderline based on metagenomics. This demonstrated that within the same sample set, most, but not all taxonomic associations were seen with both methods. Considering significant cancer associations with the relative abundance of genes, modules, and pathways in a recently published French metagenomics dataset, statistically significant associations in the Washington, DC population were detected for four out of 10 genes, three out of nine modules, and seven out of 17 pathways. In total, colorectal cancer status in the Washington, DC study was associated with 39% of the metagenome-predicted genes, modules, and pathways identified in the French study. More within and between population comparisons are needed to identify sources of variation and disease associations that can be reproduced despite these variations. Future studies should have larger sample sizes or pool data across studies to have sufficient power to detect associations that are reproducible and significant after correction for multiple testing.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In contrast to 16S rRNA sequencing, whole-genome shotgun metagenomic sequencing provides more comprehensive taxonomic and functional characterization, encompassing broader genomic content and enabling higher-resolution bacterial community profiling.”
From Microbiome meets immunotherapy: unlocking the hidden predictors of immune checkpoint inhibitors (2025) · cites this paper as [147]
Experimental — accuracy not yet measuredIn the citing paper
“Besides Fusobacterium, other oral bacteria and markers of periodontal disease are enriched in colorectal tumors and feces of patients, including the genera of Porphyromonas and Peptostreptococcus, and Parvimonas micra.15, 35, 37, 38, 53”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [37]
In the citing paper
“studies of fecal microbiomes that used either 16s rRNA or shotgun metagenomic sequencing found that F nucleatum to be increased in fecal samples from patients with CRC or adenoma compared with controls (Table 1).15, 18, 35–39”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [37]
In the citing paper
“several studies have shown that compared with controls, patients with CRC had lower abundances of Bifidobacterium and Lactobacillus spp,16, 18 and other SCFA-producing bacteria, such as the genera Clostridium and Roseburia, the family Lachnospiraceae, and Faecalibacterium prausnitzii.15, 35, 37, 45, 53, 129”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [37]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Structure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624
KEGG for linking genomes to life and the environment
Kanehisa, M. I., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm882 · Cited by 6,828
A metagenome-wide association study of gut microbiota in type 2 diabetes
Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al. (2012) · Nature · doi:10.1038/nature11450 · Cited by 6,762
Richness of human gut microbiome correlates with metabolic markers
Le Chatelier, E., Nielsen, T., Qin, J., Prifti, E., Hildebrand, F., Falony, G., Almeida, M., Arumugam, M., Batto, J.-M., Kennedy, S., et al. (2013) · Nature · doi:10.1038/nature12506 · Cited by 4,662
Self-citationGut metagenome in European women with normal, impaired and diabetic glucose control
Karlsson, F. H., Tremaroli, V., Nookaew, I., Bergström, G., Behre, C. J., Fagerberg, B., Nielsen, J., & Bäckhed, F. (2013) · Nature · doi:10.1038/nature12198 · Cited by 3,115
The gut microbiota, bacterial metabolites and colorectal cancer
Louis, P., Hold, G. L., & Flint, H. J. (2014) · Nature Reviews Microbiology · doi:10.1038/nrmicro3344 · Cited by 2,925
Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota
MetaHIT consortium, Forslund, K., Hildebrand, F., Nielsen, T., Falony, G., Le Chatelier, E., Sunagawa, S., Prifti, E., Vieira-Silva, S., Gudmundsdottir, V., et al. (2015) · Nature · doi:10.1038/nature15766 · Cited by 2,116
Self-citationGut microbiome development along the colorectal adenoma-carcinoma sequence
Feng, Q., Liang, S., Jia, H., Stadlmayr, A., Tang, L., Lan, Z., Zhang, D., Xia, H., Xu, X., Jie, Z., et al. (2015) · Nature Communications · doi:10.1038/ncomms7528 · Cited by 1,506
Potential of fecal microbiota for early-stage detection of colorectal cancer
Zeller, G., Tap, J., Voigt, A. Y., Sunagawa, S., Kultima, J. R., Costea, P. I., Amiot, A., Böhm, J., Brunetti, F., Habermann, N., et al. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145645 · Cited by 1,375
Self-citation
Flagged first, then by citations
1 of the 32 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- J Oksanen, 2007, The vegan package, Community ecology package, 631
Cited by
Sorted by citations
Items 201–300 of 370
Gao, F., Shi, L., Hines, T. J., Hebbar, S., Neufeld, K. L., & Smith, D. S. (2017) · Molecular Biology of the Cell · doi:10.1091/mbc.e16-07-0555 · Cited by 9
Is the oral pathogen, Porphyromona gingivalis, associated to colorectal cancer?: a systematic review
Navarro-Sánchez, A., Nieto-Vitoria, M. Á., López-López, J. A., Martínez-Crespo, J. J., & Navarro‐Mateu, F. (2025) · BMC Cancer · doi:10.1186/s12885-025-13770-4 · Cited by 8
A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
Eshkiki, H., Tanhaei, F., Caraffini, F., & Mora, B. (2025) · Applied Sciences · doi:10.3390/app152412934 · Cited by 9
Are we any closer to screening for colorectal cancer using microbial markers?A critical review
Starý, L., Mezerová, K., Skalický, P., Zbořil, P., & Raclavský, V. (2017) · Biomedical Papers · doi:10.5507/bp.2017.051 · Cited by 7
Bhushan, B., Eslavath, M. R., Yadav, A. P., Srivastava, A. K., Reddy, M. P. K., Norboo, T., Kumar, B., Singh, S., & Ganju, L. (2021) · Journal of Proteins and Proteomics · doi:10.1007/s42485-021-00077-8 · Cited by 7
mbImpute: an accurate and robust imputation method for microbiome dataPreprint
Jiang, R., Li, W. V., & Li, J. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.07.982314 · Cited by 7
Kværner, A. S., Birkeland, E. E., Bucher-Johannessen, C., Vinberg, E., Nordby, J. I., Kangas, H., Bemanian, V., Ellonen, P., Botteri, E., Natvig, E., et al. (2020) · medRxiv · doi:10.1101/2020.12.22.20248658 · Cited by 7
Wirbel, J., Essex, M., Forslund, S. K., & Zeller, G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.09.491139 · Cited by 7
Self-citationFrom lab to life: technological innovations in transforming cancer metastasis detection and therapy
Basu, S., Sasikumar, S., Sur, S., Vaidya, V., Kheur, S. M., Gupta, S., Ranjan, A., Paul, M. K., Nawani, N. N., Bhat, A., et al. (2025) · Discover Oncology · doi:10.1007/s12672-025-02910-8 · Cited by 7
Manning, S. W., Hackney, E., Dunneram, Y., Hull, M. A., Mitra, S., Stewart, C. J., Louca, P., Meader, N., Sharp, L., & Rees, C. J. (2025) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.70252 · Cited by 6
Wei, Z., Chen, G., & Tang, Z.-Z. (2025) · Genome biology · doi:10.1186/s13059-025-03721-4 · Cited by 5
Mohammadzadeh, R., Mahnert, A., Zurabishvili, T., Wink, L., Kumpitsch, C., Habisch, H., Sprengel, J., Filek, K., Mertelj, P., Pernitsch, D., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69711-7 · Cited by 10
Liu, H., Song, C., Wang, J., Chen, Z., Zhang, X., Zhou, H., Yao, L., Chen, D., Gu, W., Huang, R.-K., et al. (2023) · The International Journal of Biological Markers · doi:10.1177/03936155231210881 · Cited by 6
gülhan, Ü. G., Nıkerel, E., Çakır, T., Sevilgen, F. E., & Durmuş, S. (2024) · Molecular Omics · doi:10.1039/d4mo00016a · Cited by 6
Leithy, A. A. E., Youssef, A. S. E.-D., Nassar, A. A.-M., Aziz, R. K., Khaled, N. M., Mahrous, M. T., Farahat, G. N., Mohamed, A. H., & Bakr, Y. M. (2025) · Gut Pathogens · doi:10.1186/s13099-025-00681-9 · Cited by 6
Zhao, L., Chen, W., Ge, Y., Lv, X., Wang, Y., Yu, H., Liu, Y., Wu, D., Jiao, N., Wu, Y., et al. (2021) · Microbiology Spectrum · doi:10.1128/spectrum.00872-21 · Cited by 6
El‐Sokkary, M. M. A. (2022) · Pan African Medical Journal · doi:10.11604/pamj.2022.43.119.30037 · Cited by 6
Metagenomic analysis reveals gut plasmids as diagnosis markers for colorectal cancer
Cai, Z., Li, P., Zhu, W., Wei, J., Lü, J., Song, X., Li, K., Li, S., & Li, M. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1130446 · Cited by 7
Mikrobielle Biomarker zur Krebsfrüherkennung
Voigt, A. Y., Zeller, G., & Bork, P. (2017) · DMW - Deutsche Medizinische Wochenschrift · doi:10.1055/s-0042-110193 · Cited by 6
Self-citationA Comprehensive Analysis of the Global Human Gut Archaeome from a Thousand Genome CataloguePreprint
Chibani, C. M., Mahnert, A., Borrel, G., Almeida, A., Werner, A., Brugère, J., Gribaldo, S., Finn, R. D., Schmitz, R. A., & Moissl‐Eichinger, C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3742253 · Cited by 6
Quantifying Metagenomic Strain Associations from Microbiomes with AnpanPreprint
Ghazi, A. R., Thompson, K. N., Bhosle, A., Mei, Z., Yan, Y., Wang, F., Wang, K., Franzosa, E. A., & Huttenhower, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.06.631550 · Cited by 7
Pai, Y.-C., Huang, C., Lin, L.-Y., Li, Y.-H., & Yu, L. C. (2025) · Journal of physiological investigation. · doi:10.4103/ejpi.ejpi-d-25-00045 · Cited by 6
Measuring gut microbiome as a colorectal cancer screening tool: potential and challenges
Severino, A., Marchitto, S. A., Bisegna, P., Porcari, S., Rondinella, D., Schepis, T., Barbaro, F., Pecere, S., Maida, M. F., Spada, C., et al. (2025) · Expert Review of Gastroenterology & Hepatology · doi:10.1080/17474124.2025.2592078 · Cited by 6
Lobel, L., Fonseca‐Pereira, D., Nakatsu, G., Michaud, M., Cao, Y. G., Bae, S., Krabak, C., Oren, Y., Clay, S. L., Tekle, G. E., et al. (2026) · Immunity · doi:10.1016/j.immuni.2026.05.001 · Cited by 5
Advancements in Understanding and Preventing Obesity-Related Colon Cancer
Shieh, C., Thompson, H. J., McLaughlin, E. M., Chiang, C., & Hussan, H. (2024) · The Cancer Journal · doi:10.1097/ppo.0000000000000744 · Cited by 5
DeepGum: Deep feature transfer for gut microbiome analysis using bottleneck models
Çiftcioğlu, U. G. E., & Nalbantoğlu, Ö. U. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.105984 · Cited by 6
The profiles of dysbiotic microbial communities
Brun, P. (2019) · AIMS Microbiology · doi:10.3934/microbiol.2019.1.87 · Cited by 6
Escalona, M. A. R., Poloni, J. D. F., Krause, M. J., & Dorn, M. (2023) · Molecular Omics · doi:10.1039/d3mo00021d · Cited by 5
Microbiome and Diseases: Colorectal Cancer
Iftekhar, A., Sperlich, A., Janssen, K., & Sigal, M. J. (2018) · Springer eBooks · doi:10.1007/978-3-319-90545-7_15 · Cited by 5
Khan, S. M., & Kelly, L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/726901 · Cited by 5
Wu, Y., Ji, G. L., Han, D., Zhang, Y., Zhu, X., Li, H., Li, M., Gao, Y., Xie, R., Xu, M., et al. (2025) · npj Biofilms and Microbiomes · doi:10.1038/s41522-025-00826-3 · Cited by 5
The landscape of tissue-resident microbiota across normal, polyp, and colorectal cancer tissues
Xiang, H., Shen, B., Lao, W., Bai, B., Chen, M., Zhou, L., Yu, G., Zhang, W., Chen, H., Wang, Y., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69705-5 · Cited by 5
Shen, J. F., Jin, G., Zhang, Z., Zhang, J., Sun, Y., Xie, X., Ma, T., Zhu, Y., Du, Y., Niu, Y., et al. (2022) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-022-11846-w · Cited by 4
Potential role of healthy microbiome in metabolic syndrome and immune competence
Majumder, R., Debnath, A., & Das, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85730-7.00061-8 · Cited by 4
Chowdhury, R. R., Dhar, J., Robinson, S. M., Lahiri, A., Basak, K., Paul, S., & Banerjee, R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107629 · Cited by 4
Visual analysis of colorectal cancer and gut microbiota: A bibliometric analysis from 2002 to 2022
Long, D., Mao, C., Zhang, Z., Zou, J., & Zhu, Y. (2023) · Medicine · doi:10.1097/md.0000000000035727 · Cited by 4
Molecular Mechanisms of Gut Microbiota-Associated Colorectal Carcinogenesis
Shao, Y., & Zeng, X. (2020) · Infectious Microbes & Diseases · doi:10.1097/im9.0000000000000030 · Cited by 4
Oral Microbes are a Signature of Disease in the GutPreprint
Khan, S. M., & Kelly, L. (2023) · Research Square · doi:10.21203/rs.3.rs-1631596/v2 · Cited by 7
RFW captures species-level metagenomic functions by integrating genome annotation information
Mi, K., Xu, R., & Liu, X. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100932 · Cited by 3
Nogal, A., Wang, K., Thompson, K. N., Kim, H., Bhosle, A., Piccinno, G., Maharjan, S., Upreti, C. R., Nguyen, L. H., Segata, N., et al. (2026) · Cell Host & Microbe · doi:10.1016/j.chom.2026.05.001 · Cited by 3
Instance-based transfer learning enables cross-cohort early detection of colorectal cancer
Sun, Y., Wu, S., Wu, Z., Zhu, W., Gao, H., Xing, J., Zhao, J., Fan, X., & Su, X. (2026) · npj Biofilms and Microbiomes · doi:10.1038/s41522-026-01001-y · Cited by 4
Infectious Organisms Associated With Colorectal Cancer
Park, M. Y., & Eslick, G. D. (2019) · Elsevier eBooks · doi:10.1016/b978-0-323-54843-4.00009-x · Cited by 3
The Clinical Versatility of Next-Generation Sequencing in Colorectal Cancer
Hancock, K. (2020) · American Journal of Biomedical Science & Research · doi:10.34297/ajbsr.2020.07.001220 · Cited by 3
What can we expect from emerging technologies in microbiome era?
Smith, S. I., & Ajayi, A. (2021) · Minerva Biotechnology and Biomolecular Research · doi:10.23736/s2724-542x.20.02744-5 · Cited by 3
Community-led risk analysis of direct-to-consumer whole-genome sequencing
Samlali, K., Thornbury, M., & Venter, A. (2022) · Biochemistry and Cell Biology · doi:10.1139/bcb-2021-0506 · Cited by 4
Streptococcus gallolyticus infection: A neglected marker for colorectal cancer?
Ismail, C. M. K. H., Aidid, E. M., Hamzah, H. A., Shalihin, M. S. E., & Nor, A. M. (2023) · Arab Journal of Gastroenterology · doi:10.1016/j.ajg.2023.02.002 · Cited by 3
Li, J., Gui, Q., Yang, Y., Chen, L., Huang, S., Zhang, X., 蒋婧瑾, Peng, C., Wu, Y., Chen, L., et al. (2025) · Journal of Zhejiang University SCIENCE B · doi:10.1631/jzus.b2400341 · Cited by 4
Casimiro‐Soriguer, C. S., Loucera, C., Peña-Chilet, M., & Dopazo, J. (2020) · doi:10.21203/rs.2.21634/v1 · Cited by 3
Russ, C. A., Zertalis, N. A., & Nanton, V. (2021) · medRxiv · doi:10.1101/2021.06.05.21258404 · Cited by 3
Hinton, A. L., & Mucha, P. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.08.471758 · Cited by 3
Oral Microbes are a Signature of Disease in the GutPreprint
Kelly, L., & Khan, S. M. (2022) · Research Square · doi:10.21203/rs.3.rs-1631596/v1 · Cited by 3
Mohammadzadeh, R., Mahnert, A., Zurabishvili, T., Wink, L., Kumpitsch, C., Habisch, H., Sprengel, J., Filek, K., Mertelj, P., Pernitsch, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.12.659283 · Cited by 3
Li, Q., Liu, F., Zhong, J., Fang, X., Zhang, X., Xiong, H., Li, G., & Chen, H. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1656016 · Cited by 3
Zhang, Z. D., Lu, T., Dong, B., Liu, J. Q., Zhang, Y., Li, S., Liu, H., Xin, L., Guan, T., Guo, H., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08403-8 · Cited by 2
Scano, C., Choudhury, A., Rojo, M., Hawkins, J., Crowhurst, L., Zaharas, G., Lavado, R., & Greathouse, K. L. (2026) · Microbiology · doi:10.1099/mic.0.001729 · Cited by 1
Significance of the normal microflora of the body
Gupta, V., Tripathy, B. C., Gupta, N., & Prakash, J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-96128-8.00008-0 · Cited by 3
A Deep Learning Approach to Predict Health Status Using Microbiome Profiling
Wickramaratne, D., Wijesinghe, C. R., & Weerasinghe, A. R. (2022) · International Conference on Advances in ICT for Emerging Regions · doi:10.1109/icter58063.2022.10024102 · Cited by 2
VTwins: inferring causative microbial features from metagenomic data of limited samples
Meng, Q., Zhou, Q., Shi, S., Xiao, J., Ma, Q., Yu, J., Chen, J., & Kang, Y. (2023) · 中国科学通报:英文版 · doi:10.1016/j.scib.2023.10.024 · Cited by 2
Çiftcioğlu, U. G. E., & Nalbantoğlu, Ö. U. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106935 · Cited by 2
Decoding the link between microbial secondary metabolites and colorectal cancer
Wang, S., & Wu, M. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108372 · Cited by 2
Jyoti, J., & Hütt, M. ‐. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf095 · Cited by 1
Bloqueadores ácidos competitivos de potasio (PCABs): estado del arte
Díaz, J. A. U., Regino, W. O., Cheng, H. G. C., Fernández, H. M., Piscoya, A., Ordoñez, J. S. F., & Parra, L. O. (2025) · Revista de Gastroenterología del Perú · doi:10.47892/rgp.2025.451.1873 · Cited by 2
Colorectal cancer: risk factors and potential of dietary probiotics in its prevention
Arora, R., Sharma, S., & Kumar, B. (2022) · Proceedings of the Indian National Science Academy · doi:10.1007/s43538-022-00083-4 · Cited by 2
A Pipeline for Constructing Reference Genomes for Large Cohort-Specific Metagenome Compression
Wang, L., Ding, R., He, S., Wang, Q., & Zhou, Y. (2023) · Microorganisms · doi:10.3390/microorganisms11102560 · Cited by 2
Chandel, N., Maile, A., Shrivastava, S., Verma, A. K., & Thakur, V. (2024) · Gut Microbiome · doi:10.1017/gmb.2024.6 · Cited by 3
Biegert, G. W. G., Karpinets, T. V., Wu, X., Alam, M. B. E., Sims, T. T., Yoshida-Court, K., Lynn, E. J., Yue, J., Medrano, A. Y. D., Petrosino, J. F., et al. (2020) · doi:10.21203/rs.3.rs-30541/v1 · Cited by 2
Almeida, A. S., Tran, T. T. T., Ghosh, T. S., Ribière, C., Foley, C., Azevedo, L. A., Pellanda, P., Frei, W., Hueston, C. M., Kumar, R. S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.24.432654 · Cited by 2
Wolf, P. G., Cowley, E. S., Breister, A. M., Matatov, S., Lucio, L., Polak, P., Ridlon, J. M., Gaskins, H. R., & Anantharaman, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.01.450790 · Cited by 2
Letourneur, D., Bergsten, E., Raynaud, N., Mondy, J., Nigro, G., Tsoumtsa, L. L., Vaysse, A., Descorps‐Declère, S., Yoshida, A. E., Stefani, C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.02.680056 · Cited by 2
Genos-m: a foundation model for human-associated microbial genomesPreprint
Fang, C., Yang, F., Hou, H., Ren, H., Zhong, H., Xu, H., Zhang, J., Su, J., Cai, J., Yuan, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.21.726868 · Cited by 3
Biegert, G., Karpinets, T., Wu, X., Alam, M. B. E., Sims, T. T., Yoshida-Court, K., Lynn, E. J., Yue, J., Medrano, A. D., Petrosino, J., et al. (2020) · doi:10.1101/2020.05.05.080002 · Cited by 1
Feature Selection Using Local Interpretable Model-Agnostic Explanations on Metagenomic Data
Thanh-Hai, N., Tran, T. B., Tran, A. C., & Thai-Nghe, N. (2020) · Communications in computer and information science · doi:10.1007/978-981-33-4370-2_24 · Cited by 1
Ortañez, J., & Degnan, P. H. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1241582 · Cited by 1
Organoids as an in vitro model to study human tumors and bacteria
Liu, N., Liu, S., Xu, X., Nong, X., & Chen, H. I. (2024) · Journal of Surgical Oncology · doi:10.1002/jso.27626 · Cited by 1
Russ, C., Zertalis, N. A., & Nanton, V. (2024) · EMJ Gastroenterology · doi:10.33590/emjgastroenterol/ohyp9967 · Cited by 1
Schroer, H. W., Beghini, F., Garay, J. A. R., Christakis, N. A., & Bosch, D. E. (2026) · mSystems · doi:10.1128/msystems.00305-26 · Cited by 1
Ensemble test for microbiome data
Bu, D., Yan, J., Yang, W., Zhang, X., & Li, Q. (2026) · Microbiome · doi:10.1186/s40168-026-02367-z · Cited by 1
Gómez‐Moreno, R., Martínez-Ramírez, R., Roche-Lima, A., Carrasquillo-Carrión, K., Pérez‐Santiago, J., & Baerga‐Ortiz, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00631 · Cited by 1
Seo, I., Bae, S. U., Kim, S., Jeong, W. K., & Baek, S. K. (2019) · Journal of Bacteriology and Virology · doi:10.4167/jbv.2019.49.4.162 · Cited by 1
Zhao, L., Shi, Y., Lau, H. C.-H., Liu, W., Luo, G., Wang, G., Liu, C., Pan, Y., Zhou, Q., Ding, Y., et al. (2022) · Gastroenterology · doi:10.1016/s0016-5085(22)60240-3 · Cited by 1
StableMate: a statistical method to select stable predictors in omics data
Deng, Y., Mao, J., Choi, J., & Cao, K. L. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae130 · Cited by 1
Islam, M. Z., Tran, M. A., Xu, T., Tierney, B. T., Patel, C. J., & Kostic, A. D. (2021) · Research Square · doi:10.21203/rs.3.rs-1195626/v1 · Cited by 1
Biegert, G. W. G., Karpinets, T. V., Wu, X., Alam, M. B. E., Sims, T. T., Yoshida-Court, K., Lynn, E. J., Yue, J., Medrano, A. Y. D., Petrosino, J. F., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-78162/v1 · Cited by 1
Chen, F., Li, S., Guo, R., Song, F., Zhang, Y., Wang, X., Huo, X., Lv, Q., Ullah, H., Wang, G., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.17.500372 · Cited by 1
Deep Learning Transforms Phage-Host Interaction Discovery from Metagenomic DataPreprint
Yang, Y., Wang, T., Huang, D., Wang, X., Weiss, S. T., Korzenik, J. R., & Liu, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.26.656232 · Cited by 2
Barak, N., Bhattacharya, H., Asnicar, F., Sung, J., Segata, N., & Yassour, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.12.658885 · Cited by 1
Leuzzi, D., Rampelli, S., Savojardo, C., Scicchitano, D., D’Amico, F., Frisan, T., Martelli, P. L., Candela, M., & Turroni, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.17.706414 · Cited by 1
Wei, Z., Hong, Q., Chen, G., Hartert, T. V., Rosas‐Salazar, C., Das, S. R., Shilts, M. H., Levin, A. M., & Tang, Z.-Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.09.717497
Mikrobielle Biomarker zur Krebsfrüherkennung
Voigt, A. Y., Zeller, G., & Bork, P. (2018) · TumorDiagnostik & Therapie · doi:10.1055/s-0043-124940
Self-citationMicrobial community profiling of human gastrointestinal cancers
Thomas, A. M. (2019) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.95.2019.tde-07022019-134344
Compromised intestinal barrier underlies gut microbiota dysbiosis of intestinal diseasesPreprint
Jiang, P., Lai, S., Wu, S., Yin, X., Zhang, J., Zhao, X., Xu, S.-Q., & Chen, W. (2019) · medRxiv · doi:10.1101/19011833
Predominance of Extreme Environmental Conditions at High Altitude in Modulating Human Gut MicrobiotaPreprint
Bhushan, B., Eslavath, M. R., Yadav, A. P., Srivastava, A. K., Reddy, M. P., Norboo, T., Kumar, B., Ganju, L., & Singh, S. (2020) · doi:10.22541/au.159646791.16636826
Body site-specific and disease-specific virulome in the human microbiomePreprint
Liu, F., Dong, W., Guo, Y., Xiong, Q., Lü, N., Song, X., Xue, Y., Cao, D., Fan, X., Yuan, F., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.13.403006
VTwins: Identifying Robust Associations from High-dimensional Data of Limited SamplesPreprint
Meng, Q., Zhou, Q., Ma, Q., Yu, J. Y., Chen, J., & Kang, Y. (2021) · Research Square · doi:10.21203/rs.3.rs-116301/v1
Correcting for Background Noise Improves Phenotype Prediction from Human Gut Microbiome DataPreprint
Briscoe, L., Balliu, B., Sankararaman, S., Halperin, E., & Garud, N. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.19.436199
Thanh-Hai, N., Tran, T. B., Phan, N. Y. K., Điện, T. T., & Thai-Nghe, N. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-76620-7_6
Wolf, P. G., Cowley, E. S., Breister, A. M., Matatov, S., Lucio, L., Polak, P., Ridlon, J. M., Gaskins, H. R., & Anantharaman, K. (2021) · Research Square · doi:10.21203/rs.3.rs-809984/v1
THE INDO-PAKISTAN WAR, 1971, AND THE UNITED NATIONS
Faruki, K. A. (2016)
(2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823485-3.00040-3
Microbiota in utero? When and Where Microbial Establishment Starts?
Jiménez, E. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819265-8.00073-5
Metadata
- Authors
- Vogtmann, E., Hua, X., Zeller, G., Sunagawa, S., Voigt, A. Y., Hercog, R., Goedert, J. J., Shi, J., Bork, P., & Sinha, R.
- Affiliations
- National Cancer Institute (US)
- European Molecular Biology Laboratory (DE)
- German Cancer Research Center (DE)
- Corresponding author
- Vogtmann, Emily
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-05-12
- Volume
- 11
- Issue
- 5
- Pages
- e0155362
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4865240
- OpenAlex
- W2377027462
- Cited by
- 366
- References
- 32
- Citations by year
- 2026: 32 · 2025: 42 · 2024: 46 · 2023: 34 · 2022: 44 · 2021: 44 · 2020: 53 · 2019: 25 · 2018: 20 · 2017: 23 · 2016: 3
- FWCI
- 12.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/publicdomain/zero/1.0/ (vor)
- Locations
- PLoS ONE (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Online Publication Service of Würzburg University (Würzburg University) (submittedVersion, public-domain)
- Funders
- National Cancer Institute (Intramural Research Program)
- European Research Council (268985)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Health Sciences › Medicine › Infectious Diseases › Clostridium difficile and Clostridium perfringens research
- Keywords
- colorectal cancer, gut microbiota, metagenomics, 16S rRNA, whole genome shotgun sequencing, reproducibility, Fusobacterium, Porphyromonas, Clostridia, fecal microbiota, cross-population comparison
- MeSH terms
- Gastrointestinal Microbiome, District of Columbia, Female, Humans, Male, Middle Aged, RNA, Ribosomal, 16S, Colorectal Neoplasms, Reproducibility of Results, Genome, Human, Case-Control Studies, Sequence Analysis, DNA
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review