A multi-modal deep language model for contaminant removal from metagenome-assembled genomes

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

Learning Transferable Visual Models From Natural Language Supervision

Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,474 · see citing works

Prodigal: prokaryotic gene recognition and translation initiation site identification

Hyatt, D., Chen, G.-L., LoCascio, P. F., Land, M., Larimer, F. W., & Hauser, L. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-119 · Cited by 12,426 · see citing works

MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph

Li, D., Liu, C.-M., Luo, R., Sadakane, K., & Lam, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv033 · Cited by 9,781 · see citing works

Focal Loss for Dense Object Detection

Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858826 · Cited by 9,556 · see citing works

metaSPAdes: a new versatile metagenomic assembler

Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017) · Genome Research · doi:10.1101/gr.213959.116 · Cited by 4,496 · see citing works

MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies

Kang, D., Li, F., Kirton, E., Thomas, A., Egan, R. S., An, H., & Wang, Z. (2019) · PeerJ · doi:10.7717/peerj.7359 · Cited by 3,784 · see citing works

Applied Microbiology and Biotechnology

(2013) · Applied Microbiology and Biotechnology · doi:10.1007/253.1432-0614 · Cited by 3,514 · see citing works

dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication

Olm, M. R., Brown, C. T., Brooks, B., & Banfield, J. F. (2017) · The ISME Journal · doi:10.1038/ismej.2017.126 · Cited by 3,031 · see citing works

Binning metagenomic contigs by coverage and composition

Alneberg, J., Bjarnason, B. S., de Bruijn, I., Schirmer, M., Quick, J., Ijaz, U. Z., Lahti, L., Loman, N. J., Andersson, A. F., & Quince, C. (2014) · Nature Methods · doi:10.1038/nmeth.3103 · Cited by 2,707 · see citing works

Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea

The Genome Standards Consortium, Bowers, R. M., Kyrpides, N. C., Stepanauskas, R., Harmon-Smith, M., Doud, D., Reddy, T. B. K., Schulz, F., Jarett, J., Rivers, A. R., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3893 · Cited by 2,624 · see citing works

Constrained K-means Clustering with Background Knowledge

Wagstaff, K. L., Cardie, C., Rogers, S., & Schrödl, S. (2001) · Cited by 2,469 · see citing works

Insights into the phylogeny and coding potential of microbial dark matter

Rinke, C., Schwientek, P., Sczyrba, A., Ivanova, N. N., Anderson, I. J., Cheng, J.-F., Darling, A., Malfatti, S., Swan, B. K., Gies, E. A., et al. (2013) · Nature · doi:10.1038/nature12352 · Cited by 2,394 · see citing works

Proceedings of IEEE International Conference on Computer Vision

(1995) · doi:10.1109/iccv.1995 · Cited by 2,339 · see citing works

GTDB-Tk v2: memory friendly classification with the genome taxonomy database

Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P., & Parks, D. H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac672 · Cited by 2,106 · see citing works

Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life

Parks, D. H., Rinke, C., Chuvochina, M., Chaumeil, P.-A., Woodcroft, B. J., Evans, P. N., Hugenholtz, P., & Tyson, G. W. (2017) · Nature Microbiology · doi:10.1038/s41564-017-0012-7 · Cited by 1,882 · see citing works

CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning

Chklovski, A., Parks, D. H., Woodcroft, B. J., & Tyson, G. W. (2023) · Nature Methods · doi:10.1038/s41592-023-01940-w · Cited by 1,705 · see citing works

A new genomic blueprint of the human gut microbiota

Almeida, A., Mitchell, A. L., Boland, M., Forster, S. C., Gloor, G. B., Tarkowska, A., Lawley, T. D., & Finn, R. D. (2019) · Nature · doi:10.1038/s41586-019-0965-1 · Cited by 1,314 · see citing works

Focal Loss for Dense Object Detection

Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017) · arXiv · doi:10.48550/arxiv.1708.02002 · Cited by 1,227 · see citing works

Visual attention network

Guo, M.-H., Lu, C.-Z., Liu, Z.-N., Cheng, M., & Hu, S. (2023) · Computational Visual Media · doi:10.1007/s41095-023-0364-2 · Cited by 1,163 · see citing works

metaFlye: scalable long-read metagenome assembly using repeat graphs

Kolmogorov, M., Bickhart, D. M., Behsaz, B., Gurevich, A. A., Rayko, M., Shin, S. B., Kuhn, K. L., Yuan, J., Polevikov, E., Smith, T. P. L., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-00971-x · Cited by 1,087 · see citing works

Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software

Sczyrba, A., Hofmann, P., Belmann, P., Koslicki, D., Janssen, S., Dröge, J., Gregor, I., Majda, S., Fiedler, J., Dahms, E., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4458 · Cited by 873 · see citing works

A genomic catalog of Earth’s microbiomes

Nayfach, S., Roux, S., Seshadri, R., Udwary, D., Varghese, N. J., Schulz, F., Wu, D., Páez-Espino, D., Chen, I.-M. A., Huntemann, M., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0718-6 · Cited by 847 · see citing works

New insights from uncultivated genomes of the global human gut microbiome

Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S., & Kyrpides, N. C. (2019) · Nature · doi:10.1038/s41586-019-1058-x · Cited by 737 · see citing works

Discovery and characterization of gut microbiota decarboxylases that can produce the neurotransmitter tryptamine

Williams, B. B., Van Benschoten, A. H., Cimermancic, P., Donia, M. S., Zimmermann, M., Taketani, M., Ishihara, A., Kashyap, P. C., Fraser, J. S., & Fischbach, M. A. (2014) · Cell Host & Microbe · doi:10.1016/j.chom.2014.09.001 · Cited by 732 · see citing works

MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm

Wu, Y., Tang, Y.-H., Tringe, S. G., Simmons, B. A., & Singer, S. W. (2014) · Microbiome · doi:10.1186/2049-2618-2-26 · Cited by 708 · see citing works

Improved metagenome binning and assembly using deep variational autoencoders

Nissen, J. N., Johansen, J., Allesøe, R. L., Sønderby, C. K., Armenteros, J. J. A., Grønbech, C. H., Jensen, L. J., Nielsen, H. B., Petersen, T. N., Winther, O., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00777-4 · Cited by 634 · see citing works

Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome

Mars, R. A. T., Yang, Y., Ward, T., Houtti, M., Priya, S., Lekatz, H. R., Tang, X., Sun, Z., Kalari, K. R., Korem, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.007 · Cited by 456 · see citing works

Omni-Dimensional Dynamic ConvolutionPreprint

Li, C., Zhou, A., & Yao, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.07947 · Cited by 373 · see citing works

GUNC: detection of chimerism and contamination in prokaryotic genomes

Orakov, A. N., Fullam, A., Coelho, L. P., Khedkar, S., Szklarczyk, D., Mende, D., Schmidt, T., & Bork, P. (2020) · Genome Biology · doi:10.1186/s13059-021-02393-0 · Cited by 288 · see citing works

Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomic datasets

Rodriguez‐R, L. M., & Konstantinidis, K. T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt584 · Cited by 257 · see citing works

Comparing and evaluating metagenome assembly tools from a microbiologist's perspective - not only size matters!

Vollmers, J., Wiegand, S., & Kaster, A.-K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169662 · Cited by 240 · see citing works

SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing

Pan, S., Zhao, X., & Coelho, L. P. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad209 · Cited by 215 · see citing works

A Clostridia-rich microbiota enhances bile acid excretion in diarrhea-predominant irritable bowel syndrome

Zhao, L., Yang, W., Chen, Y., Huang, F., Lu, L., Lin, C., Huang, T., Ning, Z., Zhai, L., Zhong, L. L. D., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci130976 · Cited by 202 · see citing works

A genomic catalogue of soil microbiomes boosts mining of biodiversity and genetic resources

Ma, B., Lu, C., Wang, Y., Yu, J., Zhao, K., Xue, R., Ren, H., Lv, X., Pan, R., Zhang, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43000-z · Cited by 176 · see citing works

UniFormer: Unified Transformer for Efficient Spatiotemporal Representation LearningPreprint

Li, K., Wang, Y., Gao, P., Song, G., Liu, Y., Li, H., & Qiao, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.04676 · Cited by 138 · see citing works

Unravelling the Identity, Metabolic Potential and Global Biogeography of the Atmospheric Methane‐Oxidizing Upland Soil Cluster α

Pratscher, J., Vollmers, J., Wiegand, S., Dumont, M. G., & Kaster, A. (2018) · Environmental Microbiology · doi:10.1111/1462-2920.14036 · Cited by 120 · see citing works

DeepNet: Scaling Transformers to 1,000 Layers

Wang, H., Ma, S., Dong, L., Huang, S., Zhang, D., & Wei, F. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3386927 · Cited by 113 · see citing works

Comprehensive dataset of shotgun metagenomes from oxygen stratified freshwater lakes and ponds

Buck, M., Garcia, S. L., Fernández, L., Martin, G., Martínez-Rodríguez, G. A., Saarenheimo, J., Zopfi, J., Bertilsson, S., & Peura, S. (2021) · Scientific Data · doi:10.1038/s41597-021-00910-1 · Cited by 96 · see citing works

Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery

Bernard, G., Pathmanathan, J., Lannes, R., Lopez, P., & Bapteste, É. (2018) · Genome Biology and Evolution · doi:10.1093/gbe/evy031 · Cited by 92 · see citing works

Altered metabolism of bile acids correlates with clinical parameters and the gut microbiota in patients with diarrhea-predominant irritable bowel syndrome

Wei, W., Wang, H., Zhang, Y., Zhang, Y., Niu, B.-Y., & Yao, S. (2020) · World Journal of Gastroenterology · doi:10.3748/wjg.v26.i45.7153 · Cited by 82 · see citing works

Insights into structural variations and genome rearrangements in prokaryotic genomes

Periwal, V., & Scaria, V. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu600 · Cited by 71 · see citing works

High-resolution metagenomic reconstruction of the freshwater spring bloom

Kavagutti, V., Bulzu, P., Chiriac, M., Salcher, M. M., Mukherjee, I., Shabarova, T., Grujčić, V., Mehrshad, M., Kasalický, V., Andrei, A., et al. (2023) · Microbiome · doi:10.1186/s40168-022-01451-4 · Cited by 68 · see citing works

Microbial single-cell omics: the crux of the matter

Kaster, A., & Sobol, M. S. (2020) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-020-10844-0 · Cited by 65 · see citing works

A comparison of single-coverage and multi-coverage metagenomic binning reveals extensive hidden contamination

Mattock, J., & Watson, M. (2023) · Nature Methods · doi:10.1038/s41592-023-01934-8 · Cited by 59 · see citing works

How clear is our current view on microbial dark matter? (Re-)assessing public MAG & SAG datasets with MDMcleaner

Vollmers, J., Wiegand, S., Lenk, F., & Kaster, A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac294 · Cited by 56 · see citing works

Metagenome-assembled genomes of phytoplankton microbiomes from the Arctic and Atlantic Oceans

Duncan, A., Barry, K., Daum, C. G., Eloe‐Fadrosh, E. A., Roux, S., Schmidt, K., Tringe, S. G., Valentin, K.-U., Varghese, N. J., Salamov, A. A., et al. (2022) · Microbiome · doi:10.1186/s40168-022-01254-7 · Cited by 51 · see citing works

Potato root-associated microbiomes adapt to combined water and nutrient limitation and have a plant genotype-specific role for plant stress mitigation

Faist, H., Trognitz, F., Antonielli, L., Symanczik, S., White, P. J., & Sessitsch, A. (2023) · Environmental Microbiome · doi:10.1186/s40793-023-00469-x · Cited by 48 · see citing works

Phylogenetic Reconstruction Based on Synteny Block and Gene Adjacencies

Drillon, G., Champeimont, R., Oteri, F., Fischer, G., & Carbone, A. (2020) · Molecular Biology and Evolution · doi:10.1093/molbev/msaa114 · Cited by 40 · see citing works

Targeted Cell Sorting Combined With Single Cell Genomics Captures Low Abundant Microbial Dark Matter With Higher Sensitivity Than Metagenomics

Dam, H. T., Vollmers, J., Sobol, M. S., Cabezas, A., & Kaster, A. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.01377 · Cited by 36 · see citing works

metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies

Lai, S., Pan, S., Sun, C., Coelho, L. P., Chen, W., & Zhao, X. (2022) · Genome biology · doi:10.1186/s13059-022-02810-y · Cited by 33 · see citing works

MetaVelvet-DL: a MetaVelvet deep learning extension for de novo metagenome assembly

Liang, K., & Sakakibara, Y. (2021) · BMC Bioinformatics · doi:10.1186/s12859-020-03737-6 · Cited by 30 · see citing works

City-scale monitoring of antibiotic resistance genes by digital PCR and metagenomics

Maestre‐Carballa, L., Navarro‐López, V., & Martínez‐García, M. (2024) · Environmental Microbiome · doi:10.1186/s40793-024-00557-6 · Cited by 30 · see citing works

Completion of draft bacterial genomes by long-read sequencing of synthetic genomic pools

Derakhshani, H., Bernier, S. P., Marko, V. A., & Surette, M. G. (2020) · BMC Genomics · doi:10.1186/s12864-020-06910-6 · Cited by 22 · see citing works

Metagenomes, metatranscriptomes and microbiomes of naturally decomposing deadwood

Tláskal, V., Brabcová, V., Větrovský, T., López‐Mondéjar, R., Monteiro, L. M. O., Saraiva, J., da Rocha, U. N., & Baldrián, P. (2021) · Scientific Data · doi:10.1038/s41597-021-00987-8 · Cited by 15 · see citing works

Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes

bohao, Z. O. U. (2023) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.8343505 · Cited by 1 · see citing works

Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes

bohao, Z. O. U. (2023) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.8343497 · Cited by 1 · see citing works

A deep multi-modal deep language model for contaminant removal from metagenome-assembled genomes

ZOU, B. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.11917894 · Cited by 1 · see citing works

AI-empowered human microbiome research

Tian, Z., & Zhao, F. (2025) · Gut · doi:10.1136/gutjnl-2025-335946 · Cited by 51 · see citing works

Early inoculation and bacterial community assembly in plants: A review

Wang, X., Li, Y., Rensing, C., & Zhang, X. (2025) · Microbiological Research · doi:10.1016/j.micres.2025.128141 · Cited by 17 · see citing works

A unified time-frequency foundation model for sleep decoding

Huang, W., Wang, Y., Cheng, H., Xu, W., Li, T., Wu, X., Xu, H., Liao, P., Cui, Z., Zou, Q., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-67970-4 · Cited by 11 · see citing works

From microbiomes to predictive ecosystems: challenges and opportunities in artificial intelligence-based approaches

Yu, W., Qu, H., Wang, S., Shī, M., Lu, Y., Xia, S., Stanton, C., Zhang, C., Wang, Y., Zhu, T., et al. (2026) · The Lancet Microbe · doi:10.1016/j.lanmic.2026.101428 · Cited by 6 · see citing works

Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning

Zhang, J., Wang, X., Wang, D., Zheng, Z., Wang, H., & La, M. (2026) · mSystems · doi:10.1128/msystems.00800-25 · Cited by 5 · see citing works

MetaflowX: a scalable and resource-efficient workflow for multi-strategy metagenomic analysis

Xia, Y., Liang, L., Wang, X., Chen, Z., Liu, J., Yang, Y., Xie, H., Ding, Z., Huang, X., Long, S., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf954 · Cited by 3 · see citing works

CompleteBin: A transformer-based framework unlocks microbial dark matter through improved short contig binningPreprint

bohao, Z. O. U., Zhang, Z., Wang, X., Tao, R., Gu, N., Kristiansen, K., Li, S., Zhang, L., & Zhang, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.20.649691 · Cited by 2 · see citing works

Small amounts of misassembly can have disproportionate effects on pangenome-based metagenomic analyses

Majernik, S. N., Beaver, L. P., & Bradley, P. H. (2025) · mSphere · doi:10.1128/msphere.00857-24 · Cited by 1 · see citing works

Mitigation and detection of putative microbial contaminant reads from long-read metagenomic datasets

Ayala-Montaño, S., Afolayan, A. O., Kociurzynski, R., Löber, U., & Reuter, S. (2026) · Microbial Genomics · doi:10.1099/mgen.0.001609

Metagenomic analysis of the tomato microbiome in Colombia reveals bacterial diversity, potential pathogens, and resistance determinants

Enciso, Y., Echeverry-Gallego, R. A., Franco, D. C., Sánchez, J., Moncayo‐Lasso, A., Martínez‐Pachón, D., & Vanegas, J. (2026) · Food Research International · doi:10.1016/j.foodres.2026.118632

End-to-end evaluation of pipelines for metagenome-assembled genomes reveals hidden performance gapsPreprint

Coleman, I., Ma, J., Qian, G., Jiang, Y., Kav, A. B., & Korem, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.06.712906

Decoding the cancer microbiome: multi-omics, AI, and translational opportunities

Sun, Y., Cheng, O., Ma, A., Shabnaz, S., Huang, X., Zhao, J., Tan, A. C., & Ma, Q. (2026) · Genome biology · doi:10.1186/s13059-026-04284-8

Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes

Zhang, L., bohao, Z. O. U., Wang, J., Ding, Y., Zhang, Z., Huang, Y., Fang, X., Cheung, K. C., & See, S. (2023) · Greater South Information System · doi:10.60692/nj6wb-gr059

DeepCheck: multitask learning aids in assessing microbial genome quality

Guo, W., Wu, N., Zhao, K., Yang, S., Wang, L., & Liu, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae539 · Cited by 6 · see citing works

Pre-binning correction of chimeric contigs reduces reference-discordant gene annotations in near-complete metagenome-assembled genomesPreprint

Ding, Y., Xiao, J., bohao, Z. O. U., Yang, C., Wu, Z., Lyu, A., & Zhang, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.15.759294

Fast Identification and Removal of Sequence Contamination from Genomic and Metagenomic Datasets

Schmieder, R., & Edwards, R. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017288 · Cited by 740 · see citing works

Deep learning in metagenomics: from metagenomic contigs sorting to phage-bacterial association prediction

Tang, T. (. (2023) · University of Southern California Digital Library · doi:10.25549/usctheses-ouc113759510