A broken promise: microbiome differential abundance methods do not control the false discovery rate

Hawinkel, Stijn, Mattiello, Federico, Bijnens, Luc, Thas, Olivier·2019·Briefings in Bioinformatics
DOI 10.1093/bib/bbx104

Cited by (200)

Items 101–200 of 200

  1. Identification of microbial features in multivariate regression under false discovery rate control
    Arun Srinivasan, Lingzhou Xue, Xiang Zhan · 2022 · Computational Statistics & Data Analysis
  2. benchdamic: benchmarking of differential abundance methods for microbiome data
    Matteo Calgaro, Chiara Romualdi, Davide Risso, Nicola Vitulo · 2022 · Bioinformatics
  3. Comparative metagenomic analysis of the oral microbiome in COVID-19 patients and healthy individuals
    Piyush Bhanu, Sakshi Buchke, Nisha Hemandhar-Kumar, Piyush Varsha, S. K. Ravi Kiran, G. Vikneswaran, Arjun Alva, G. S. Basavaraj, Jitendra Kumar · 2025 · Scientific Reports
  4. Stool Studies Don't Pass the Sniff Test: A Systematic Review of Human Gut Microbiome Research Suggests Widespread Misuse of Machine Learning
    Thomas P. Quinn · 2021 · arXiv (Cornell University)
  5. Associations between microbial communities and key chemical constituents in U.S. domestic moist snuff
    Robert E. Tyx, Angel J. Rivera, Glen A. Satten, Lisa Mari Keong, Peter Kuklenyik, Grace Lee, Tameka S. Lawler, J. Brett Kimbrell, Stephen B. Stanfill, Liza Valentín-Blasini et al. · 2022 · PLoS ONE
  6. Accommodating multiple potential normalizations in microbiome associations studies
    Hoseung Song, Wodan Ling, Ni Zhao, Anna Plantinga, Courtney Ann Broedlow, Nichole R. Klatt, Tiffany Hensley‐McBain, Michael C. Wu · 2023 · BMC Bioinformatics
  7. Explicit Scale Simulation for analysis of RNA-sequencing with ALDEx2
    Gregory Brian Gloor, Michelle Pistner Nixon, Justin D. Silverman · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
  8. Regional and Longitudinal Dynamics of Cyanobacterial Blooms/Cyanobiome and Cyanotoxin Production in the Great Lakes Area
    Faizan Saleem, Jennifer L. Jiang, Enze Li, Kevin Tran, Adam Boere, Mahbuba Rahman, Athanasios Paschos, Judy A. Westrick, Arthur Zastepa, Thomas A. Edge et al. · 2024 · Toxins
  9. Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2
    Gregory Brian Gloor, Michelle Pistner Nixon, Justin D. Silverman · 2025 · NAR Genomics and Bioinformatics
  10. Impact of data compositionality on the detection of microbiota responses
    Brandon Hickman, Katri Korpela · 2025 · Gut Microbes
  11. A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias
    Taavi Päll, Hannes Luidalepp, Tanel Tenson, Ülo Maiväli · 2023 · PLoS Biology
  12. mbImpute: an accurate and robust imputation method for microbiome data
    Ruochen Jiang, Wei Vivian Li, Jingyi Jessica Li · 2020 · bioRxiv (Cold Spring Harbor Laboratory)
  13. A realistic benchmark for the identification of differentially abundant taxa in (confounded) human microbiome studies
    Jakob Wirbel, Morgan Essex, Sofia Kirke Forslund, Georg Zeller · 2022 · bioRxiv (Cold Spring Harbor Laboratory)
  14. Oral Microbiome in Oral Cancer Research from Sampling to Analysis: Strategies, Challenges, and Recommendations
    K. Y. P. Liu, Andrew Huang, C. Pepin, Ya Shen, P. Tsang, Catherine F. Poh · 2025 · Cancers
  15. Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies
    Leyla Kodalci, Olivier Thas · 2023 · PLoS ONE
  16. From wolves to humans: oral microbiome resistance to transfer across mammalian hosts
    Nicholas A Podar, Alyssa A. Carrell, Kira A. Cassidy, Dawn M. Klingeman, Zamin Koo Yang, Erin E. Stahler, Douglas W. Smith, Daniel R. Stahler, Mircea Podar · 2024 · mBio
  17. Das Mikrobiom – die unplanbare Größe zukünftiger Therapien
    Achim Georg Beule · 2018 · Laryngo-Rhino-Otologie
  18. Some comments on certain statistical aspects of the study of the microbiome
    José A. Ferreira, Susana Fuentes · 2019 · Briefings in Bioinformatics
  19. Testing hypotheses about the microbiome using the linear decomposition model (LDM)
    Yi-Juan Hu, Glen A. Satten · 2017 · bioRxiv (Cold Spring Harbor Laboratory)
  20. The gut microbiota in spondyloarthritis: an update
    V. R. Rodriguez, Morgan Essex, D. Poddubnyy · 2025 · Current Opinion in Rheumatology
  21. Taxonomy and Virulence Factors in the Root Canal Microbiome: Metagenomic Insights by Lesion Size and Clinical Factors in Primary Endodontic Infections
    Bayley Clarke, Ronald Ordinola‐Zapata, William Noblett, Michael Gould, Christopher M. Staley · 2026 · Journal of Endodontics
  22. Composition and functionality of bacterioplankton communities in marine coastal zones adjacent to finfish aquaculture
    R.R.P. Da Silva, Camille A. White, John Phillip Bowman, D. Jeff Ross · 2022 · Marine Pollution Bulletin
  23. Postlarval Shrimp-Associated Microbiota and Underlying Ecological Processes over AHPND Progression
    Zhongjiang Zhou, Jiaqi Lü, Pingping Zhan, Jinbo Xiong · 2025 · Microorganisms
  24. Identity and timing of protist inoculation affect plant performance largely irrespective of changes in the rhizosphere microbial community
    Nathalie Amacker, Zhilei Gao, Alexandre Jousset, Stefan Geisen, George A. Kowalchuk · 2025 · Applied and Environmental Microbiology
  25. A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets
    Andrew D. Fernandes, Michael Vu, Lisa-Monique Edward, Jean M. Macklaim, Gregory Brian Gloor · 2018 · arXiv (Cornell University)
  26. The Role of Environmental and Climatic Factors in Accelerating Antibiotic Resistance in the Mediterranean Region
    Nikolaos P. Tzavellas, Natalia Atzemoglou, Petros Bozidis, Konstantina Gartzonika · 2026 · Acta Microbiologica Hellenica
  27. Assessment of absolute abundance in mother-infant gut microbiome using marine-sourced bacterial DNA spike-in and comparison with conventional quantification methods
    Shuo Wang, David Healy, Dhrati V. Patangia, Shona Uniacke-Lowe, Eleni Kamilari, Iwona M. Kozak, Bo Kyung Yang, Eugene Michael Dempsey, Catherine Stanton, R. Paul Ross · 2025 · Microbiome Research Reports
  28. Compositional Data Analysis using Kernels in mass cytometry data
    Pratyaydipta Rudra, Ryan Michael Baxter, Elena W.Y. Hsieh, Debashis Ghosh · 2022 · Bioinformatics Advances
  29. Molecular and associated approaches for studying soil biota and their functioning
    Ulaş Karaöz, Joanne Emerson, Eoin Brodie · 2024 · Elsevier eBooks
  30. ProgPerm: Progressive permutation for a dynamic representation of the robustness of microbiome discoveries
    Liangliang Zhang, Yushu Shi, Kim-Anh Do, Christine B. Peterson, Robert R. Jenq · 2021 · BMC Bioinformatics
  31. Correlation analysis of the oral mucosal microbiome and diabetes mellitus using microbial DNA in elderly male subjects
    Jong-Hoon Lee, Young Lee, Seong‐Ho Choi, Eunsin Bae, Dong‐Woon Lee · 2022 · Oral Biology Research
  32. Temporal shifts of the microbiome associated with antibiotic treatment of purpuric drug eruptions related to epidermal growth factor receptor inhibitors
    Paul‐Chen Hsieh, Chi‐Sheng Chang, Kai‐Lung Chen, Yung‐Tsu Cho, Chia‐Yu Chu, Kuan‐Yu Chen · 2022 · Journal of the European Academy of Dermatology and Venereology
  33. llperm: a permutation of regressor residuals test for microbiome data
    Markus Viljanen, Hendriek C. Boshuizen · 2022 · BMC Bioinformatics
  34. The Influence of the Degree of Forest Management on Methylmercury and the Composition of Microbial Communities in the Sediments of Boreal Drainage Ditches
    Krišs Bitenieks, Arta Bārdule, Karin Eklöf, Mikk Espenberg, Dainis Edgars Rungis, Zane Kļaviņa, Ivars Kļaviņš, Haiyan Hu, Zane Lībieté · 2022 · Microorganisms
  35. Compositional variable selection in quantile regression for microbiome data with false discovery rate control
    Runze Li, Jin Mu, Songshan Yang, Cong Ye, Xiang Zhan · 2024 · Statistical Analysis and Data Mining The ASA Data Science Journal
  36. Composition-on-composition regression analysis for multi-omics integration of metagenomic data
    Nicholas Rios, Yuke Shi, Jun Chen, Xiang Zhan, Lingzhou Xue, Qi-Zhai Li · 2025 · Bioinform.
  37. metaGEENOME: an integrated framework for differential abundance analysis of microbiome data in cross-sectional and longitudinal studies
    Ahmed Abdelkader, Nur A. Ferdous, Mohamed S. El-Hadidi, Tomasz Burzykowski, Mohamed Mysara · 2025 · BMC Bioinformatics
  38. Comparative Study of Synthetic Bulk RNA-Seq Generators
    Felitsiya Shakola, Dean Palejev, Ivan V. Ivanov · 2022 · Lecture notes in computer science
  39. Editorial: Statistical and Computational Methods for Microbiome Multi-Omics Data
    Himel Mallick, Vanni Bucci, Lingling An · 2020 · Frontiers in Genetics
  40. Compositional Differential Abundance Testing: Defining and Finding a New Type of Health-Microbiome Associations
    Siyuan Ma, Curtis Huttenhower, Lucas Janson · 2024 · bioRxiv (Cold Spring Harbor Laboratory)
  41. Repurposing without Reinventing the Wheel - Ensemble Models for Differential Analysis
    Suvo Chatterjee, Erina Paul, Ziyu Liu, Jialin Gao, Piyali Basak, Arinjita Bhattacharyya, Chitrak Banerjee, Himel Mallick · 2025 · bioRxiv (Cold Spring Harbor Laboratory)
  42. A review of common methods used in the analysis of human microbiome sequencing data
    Luke Hannan, Mark Patrick Nicol, Maia Lesosky · 2024 · F1000Research
  43. TCVS: tree-guided compositional variable selection analysis of microbiome data
    Yicong Mao, Zhiwen Jiang, Tianying Wang, Yi‐Juan Hu, Xiang Zhan · 2025 · Bioinformatics
  44. The relationship between the gut microbiota and neuropsychological development and behaviour during childhood and adolescence: a systematic review of epidemiological studies
    Pol Jiménez-Arenas, Muriel Ferrer, Marina Ruiz-Rivera, Jordi Júlvez, Mariona Bustamante, Mireia Gascón · 2026 · Brain Behavior & Immunity - Health
  45. Compositional Analysis of Microbiome Data
    Yinglin Xia, Jun Sun · 2023 · Springer eBooks
  46. A semiparametric quantile regression rank score test for zero-inflated data
    Zirui Wang, Wodan Ling, Tianying Wang · 2025 · Biometrics
  47. A resampling-based empirical Bayes method for precise false discovery rate estimation under dependence
    Stijn Hawinkel, Luc Bijnens, Olivier Thas · 2022 · Research Square
  48. Group Heteroscedasticity - A Silent Saboteur of Power and False Discovery in RNA-Seq Differential Expression
    Suvo Chatterjee, Arindam Fadikar, Vrushab Hanumesh, Siddhant Sunil Meshram, Roger S. Zoh, Siyuan Ma, Ganesan Arunkumar, Himel Mallick · 2024 · bioRxiv (Cold Spring Harbor Laboratory)
  49. Not every gene is special: one simple rule to control the false discovery rate when analysing high-throughput sequencing data
    Scott J Dos Santos, Andreea C. Murariu, Justin D. Silverman, Gregory Brian Gloor · 2025 · bioRxiv (Cold Spring Harbor Laboratory)
  50. Microbial community profiling of human gastrointestinal cancers
    Andrew Maltez Thomas · 2019 · Digital Library of Theses and Dissertations (Universidade de São Paulo)
  51. ProgPermute: Progressive permutation for a dynamic representation of the robustness of microbiome discoveries
    Liangliang Zhang, Yushu Shi, Kim‐Anh Do, Christine B. Peterson, Robert R. Jenq · 2020 · arXiv (Cornell University)
  52. Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?
    Maryia Khomich, Ingrid Måge, Ingunn Berget, Ida Rud · 2020
  53. Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?
    Ingrid Måge, Ingunn Berget, Ida Rud, Maryia Khomich · 2020
  54. Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?
    Maryia Khomich, Ingrid Måge, Ingunn Berget · 2020
  55. Harnessing the potential of predatory protists to support a beneficial soil microbiome
    Nathalie Amacker · 2022
  56. Effects of finfish farms on pelagic protist communities in a semi-closed stratified embayment
    R.R.P. Da Silva, Camille A. White, John Phillip Bowman, D.J. Ross · 2022 · bioRxiv (Cold Spring Harbor Laboratory)
  57. Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data
    Kevin Mildau, Dennis E. te Beest, BAS ENGEL, Gerrit Gort, Jolanda M. Lambert, Sophie H. N. Willemsen‐Swinkels, Fred A. van Eeuwijk · 2023 · NAR Genomics and Bioinformatics
  58. Learning to increase the power of conditional randomization tests
    Shalev Shaer, Yaniv Romano · 2023 · Machine Learning
  59. Decoding the Microbiome-Metabolome Nexus: A Systematic Benchmark of Integrative Strategies
    Loïc Mangnier, Antoine Bodein, Margaux Mariaz, Marie‐Pier Scott‐Boyer, Alban Mathieu, Neerja Vashist, Matthew S. Bramble, Arnaud Droit · 2024 · bioRxiv (Cold Spring Harbor Laboratory)
  60. Additional file 2 of Clipper: p-value-free FDR control on high-throughput data from two conditions
    Xinzhou Ge, Yiling Elaine Chen, Dongyuan Song, MeiLu McDermott, Kyla Woyshner, Antigoni Manousopoulou, Ning Wang, Wei Li, Leo David Wang, Jingyi Jessica Li · 2021 · Figshare
  61. Testing independence: count data case
    Dana Bucalo, Bojana Milošević · 2024 · Journal of Statistical Computation and Simulation
  62. Network based differential abundance analysis: bridging community interactions and host microbiome dynamics
    Zakir Hossine, Isaac N. Towers, Benjamin D. Kaehler · 2025 · Network Modeling Analysis in Health Informatics and Bioinformatics
  63. Inferring absolute counts from proportions by constraining multivariate normal distributions
    Jeffrey R. Hage, Devin C. Koestler, Brock C. Christensen · 2026 · npj Systems Biology and Applications
  64. DeepIMB: Imputation of non-biological zero counts in microbiome data
    Hanbyul Song, Md. Mozaffar Hosain, Taesung Park · 2025 · Genes & Genomics
  65. Additional file 2 of A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions
    Yang, Lu, Chen, Jun · 2022 · Figshare
  66. Additional file 8 of mbImpute: an accurate and robust imputation method for microbiome data
    Ruochen Jiang, Li, Wei Vivian, Jingyi Jessica Li · 2021 · Figshare
  67. Additional file 3 of A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies
    Jakob Wirbel, Morgan Essex, Forslund, Sofia Kirke, Zeller, Georg · 2024 · Figshare
  68. Additional file 4 of A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies
    Jakob Wirbel, Morgan Essex, Forslund, Sofia Kirke, Zeller, Georg · 2024 · Figshare
  69. Additional file 2 of A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies
    Jakob Wirbel, Morgan Essex, Forslund, Sofia Kirke, Zeller, Georg · 2024 · Figshare
  70. Additional file 1 of Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox
    Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Zeller, Georg · 2021 · Figshare
  71. Additional file 1 of LinDA: linear models for differential abundance analysis of microbiome compositional data
    Zhou, Huijuan, Kejun He, Chen, Jun, Zhang, Xianyang · 2022 · Figshare
  72. Additional file 1 of mbImpute: an accurate and robust imputation method for microbiome data
    Ruochen Jiang, Li, Wei Vivian, Li, Jingyi Jessica · 2021 · Figshare
  73. Additional file 1 of Metatranscriptomic analyses reveal ruminal pH regulates fiber degradation and fermentation by shifting the microbial community and gene expression of carbohydrate-active enzymes
    Li, Meng M., White, Robin R., Guan, Le Luo, Laura B. Harthan, M.D. Hanigan · 2021 · Figshare
  74. Additional file 1 of Relative abundance data can misrepresent heritability of the microbiome
    Marjolein Bruijning, Julien F. Ayroles, Lucas P. Henry, Britt Louise Koskella, Kyle Meyer, Charlotte Jessica E. Metcalf · 2023 · Figshare
  75. Additional file 3 of LinDA: linear models for differential abundance analysis of microbiome compositional data
    Zhou, Huijuan, Kejun He, Chen, Jun, Zhang, Xianyang · 2022 · Figshare
  76. Additional file 1 of A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies
    Jakob Wirbel, Morgan Essex, Forslund, Sofia Kirke, Zeller, Georg · 2024 · Figshare
  77. Additional file 10 of Comparison study of differential abundance testing methods using two large Parkinson disease gut microbiome datasets derived from 16S amplicon sequencing
    Wallen, Zachary D. · 2021 · Figshare
  78. Additional file 1 of Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data
    Wei Guo Bai, Dong, Mei, Longhai Li, Cindy Xin Feng, Xu, Wei · 2021 · Figshare
  79. Additional file 2 of LinDA: linear models for differential abundance analysis of microbiome compositional data
    Zhou, Huijuan, Kejun He, Chen, Jun, Zhang, Xianyang · 2022 · Figshare
  80. Additional file 2 of Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox
    Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Zeller, Georg · 2021 · Figshare
  81. Additional file 1 of Clipper: p-value-free FDR control on high-throughput data from two conditions
    Xinzhou Ge, Chen, Yiling Elaine, Song, Dongyuan, MeiLu McDermott, Kyla Woyshner, Antigoni Manousopoulou, Ning Wang, Li, Wei, Wang, Leo D., Jingyi Jessica Li · 2021 · Figshare
  82. Additional file 1 of ProgPerm: Progressive permutation for a dynamic representation of the robustness of microbiome discoveries
    Zhang, Liangliang, Yushu Shi, Do, Kim-Anh, Christine B. Peterson, Jenq, Robert R. · 2021 · Figshare
  83. Additional file 11 of Comparison study of differential abundance testing methods using two large Parkinson disease gut microbiome datasets derived from 16S amplicon sequencing
    Wallen, Zachary D. · 2021 · Figshare
  84. Additional file 1 of Accommodating multiple potential normalizations in microbiome associations studies
    Hoseung Song, Ling, Wodan, Zhao, Ni, Anna Plantinga, Courtney Ann Broedlow, Nichole R. Klatt, Tiffany Hensley‐McBain, Wu, Michael C. · 2023 · Figshare
  85. Additional file 1 of Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ)
    Ling, Wodan, Zhao, Ni, Anna Plantinga, Lenore J. Launer, Anthony A. Fodor, Meyer, Katie A., Wu, Michael C. · 2021 · Figshare
  86. Additional file 1 of PhyloMed: a phylogeny-based test of mediation effect in microbiome
    Qilin Hong, Chen, Guanhua, Tang, Zheng-Zheng · 2023 · Figshare
  87. Additional file 3 of Clipper: p-value-free FDR control on high-throughput data from two conditions
    Xinzhou Ge, Chen, Yiling Elaine, Song, Dongyuan, MeiLu McDermott, Kyla Woyshner, Antigoni Manousopoulou, Ning Wang, Li, Wei, Wang, Leo D., Jingyi Jessica Li · 2021 · Figshare
  88. Additional file 3 of Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox
    Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Zeller, Georg · 2021 · Figshare
  89. Additional file 2 of PhyloMed: a phylogeny-based test of mediation effect in microbiome
    Qilin Hong, Chen, Guanhua, Tang, Zheng-Zheng · 2023 · Figshare
  90. Single-cell differential abundance detection: A new angle on dissecting tumor heterogeneity
    Ying-Xue Xiao, Jing Sun, Lingling Xie, Yue Zou, Tong Li, Youjin Hao, Bo Li · 2026 · World Journal of Clinical Oncology
  91. Robust Distribution‐Free Tests for the Linear Model
    Torey Hilbert, Steven N. MacEachern, Yuan Zhang · 2026 · Statistics in Medicine
  92. Identification of key players in the gut microbiota based on a unified reference for a standardized quantitative metagenomics and metabolic analysis framework
    Marianne Borderes · 2021 · theses.fr (ABES)
  93. LOCOM2: Robust Differential Abundance Analysis for Microbiome Data
    Mengyu He, Glen A. Satten, Yi‐Juan Hu · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
  94. Uncovering cellular composition changes: Cutting-edge differential abundance methods for single-cell RNA sequencing
    Yingxue Xiao, J. Sun, You-Jin Hao, Ling-Ling Xie, Hongrui Duo, Yue Zou, Yinghong Li, Dan Qin, Qingxia Yang, Bo Li · 2026 · Trends in Analytical Chemistry (TrAC)
  95. Bias-mitigated microbiome inference refines coronary artery disease signature
    Luke Honeybrook · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
  96. Stool microbiota variations along the adenoma-colorectal carcinoma sequence — robustness of disease-associated microbial features
    A. Carvalho, S. Gschwendtner, Michael Schloter, A. M. Gil, Catarina R. Marques · 2026 · Frontiers in Microbiology
  97. In ovo dose-response characterization of Lactiplantibacillus plantarum HY7715 reveals coordinated physiological and microbiome responses in vivo (Gallus gallus)
    Chloe J. McGovern, Peter R. Gracey, Hannah Glesener, Lee E. Voth‐Gaeddert, Elad Tako · 2026 · Food Research International
  98. Not every gene is special: Modelling scale controls the false discovery rate when analysing high-throughput sequencing data
    Scott J Dos Santos, Andreea C Murariu, Justin D. Silverman, Gregory Brian Gloor · 2026 · PLoS Computational Biology
  99. Methods to Address Compositional Data Challenges in Clinical Studies for the Safety Assessment of Human Microbiome Perturbations
    Aline Métris, Rui Guan, Antonis Ampatzoglou, Barry Murphy · 2026 · Journal of Food Protection
  100. TSLC: differential abundance analysis method for identifying biologically relevant known microbes
    Robert Beblavy, Cynthia L. Monaco, Cassandra E. Newkirk, Jin Xiao, Michael B. Sohn · 2026 · Bioinformatics Advances