Comparing and evaluating metagenome assembly tools from a microbiologist's perspective - not only size matters!
Open accessAbstract
With the constant improvement in cost-efficiency and quality of Next Generation Sequencing technologies, shotgun-sequencing approaches -such as metagenomics- have nowadays become the methods of choice for studying and classifying microorganisms from various habitats. The production of data has dramatically increased over the past years and processing and analysis steps are becoming more and more of a bottleneck. Limiting factors are partly the availability of computational resources, but mainly the bioinformatics expertise in establishing and applying appropriate processing and analysis pipelines. Fortunately, a large diversity of specialized software tools is nowadays available. Nevertheless, choosing the most appropriate methods for answering specific biological questions can be rather challenging, especially for non-bioinformaticians. In order to provide a comprehensive overview and guide for the microbiological scientific community, we assessed the most common and freely available metagenome assembly tools with respect to their output statistics, their sensitivity for low abundant community members and variability in resulting community profiles as well as their ease-of-use. In contrast to the highly anticipated "Critical Assessment of Metagenomic Interpretation" (CAMI) challenge, which uses general mock community-based assembler comparison we here tested assemblers on real Illumina metagenome sequencing data from natural communities of varying complexity sampled from forest soil and algal biofilms. Our observations clearly demonstrate that different assembly tools can prove optimal, depending on the sample type, available computational resources and, most importantly, the specific research goal. In addition, we present detailed descriptions of the underlying principles and pitfalls of publically available assembly tools from a microbiologist's perspective, and provide guidance regarding the user-friendliness, sensitivity and reliability of the resulting phylogenetic profiles.
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metaSPAdes is MGnify's primary assembly algorithm because the ensuing assemblies are typically of better quality than those produced using other assemblers.
“Assembly is a computationally expensive process, frequently requiring terabytes of memory to accomplish when using metaSPAdes (20), which is our primary assembly algorithm, as the ensuing assemblies are typically of better quality than those produced using other assemblers (21).”
From MGnify: the microbiome analysis resource in 2020 (2019) · cites this paper as [21]
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Microbiome definition re-visited: old concepts and new challenges
Berg, G., Rybakova, D., Fischer, D., Cernava, T., Vergès, M.-C. C., Charles, T., Chen, X., Cocolin, L., Eversole, K., Corral, G. H., et al. (2020) · Microbiome · doi:10.1186/s40168-020-00875-0 · Cited by 2,275 · see citing works
Best practices for analysing microbiomes
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., et al. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0029-9 · Cited by 2,030 · see citing works
Allio, R., Schomaker-Bastos, A., Romiguier, J., Prosdocimi, F., Nabholz, B., & Delsuc, F. (2020) · Molecular Ecology Resources · doi:10.1111/1755-0998.13160 · Cited by 1,921 · see citing works
PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures
Krawczyk, P. S., Lipiński, L., & Dziembowski, A. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1321 · Cited by 617 · see citing works
Current challenges and best-practice protocols for microbiome analysis
Bharti, R., & Grimm, D. G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbz155 · Cited by 592 · see citing works
MGnify: the microbiome analysis resource in 2020
Mitchell, A. L., Almeida, A., Beracochea, M., Boland, M., Burgin, J., Cochrane, G., Crusoe, M. R., Kale, V., Potter, S. C., Richardson, L. J., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1035 · Cited by 562 · see citing works
A review of methods and databases for metagenomic classification and assembly
Breitwieser, F. P., Lu, J., & Salzberg, S. L. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx120 · Cited by 560 · see citing works
Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
Steinegger, M., Mirdita, M., & Söding, J. (2019) · Nature Methods · doi:10.1038/s41592-019-0437-4 · Cited by 445 · see citing works
Roux, S., Emerson, J., Eloe‐Fadrosh, E. A., & Sullivan, M. B. (2017) · PeerJ · doi:10.7717/peerj.3817 · Cited by 307 · see citing works
Pérez-Cobas, A. E., Gomez-Valero, L., & Buchrieser, C. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000409 · Cited by 314 · see citing works
Biology and Taxonomy of crAss-like Bacteriophages, the Most Abundant Virus in the Human Gut
Guerin, E., Shkoporov, A. N., Stockdale, S. R., Clooney, A. G., Ryan, F. J., Sutton, T., Draper, L. A., González-Tortuero, E., Ross, R. P., & Hill, C. (2018) · Cell Host & Microbe · doi:10.1016/j.chom.2018.10.002 · Cited by 297 · see citing works
Identifying biases and their potential solutions in human microbiome studies
Nearing, J. T., Comeau, A. M., & Langille, M. G. I. (2021) · Microbiome · doi:10.1186/s40168-021-01059-0 · Cited by 240 · see citing works
Yang, C., Chowdhury, D., Zhang, Z., Cheung, W. K., Lü, A., Bian, Z., & Zhang, L. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.11.028 · Cited by 226 · see citing works
Mitchell, A., Scheremetjew, M., Denise, H., Potter, S., Tarkowska, A., Qureshi, M., Salazar, G. A., Pesseat, S., Boland, M. A., Hunter, F. M. I., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx967 · Cited by 208 · see citing works
Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities
Chiara, M., D’Erchia, A. M., Gissi, C., Manzari, C., Parisi, A., Resta, N., Zambelli, F., Picardi, E., Pavesi, G., Horner, D. S., et al. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa297 · Cited by 202 · see citing works
Gut Bacteriophage: Current Understanding and Challenges
Sutton, T., & Hill, C. (2019) · Frontiers in Endocrinology · doi:10.3389/fendo.2019.00784 · Cited by 202 · see citing works
Dai, D., Brown, C., Bürgmann, H., Larsson, D. G. J., Nambi, I. M., Zhang, T., Flach, C., Pruden, A. J., & Vikesland, P. J. (2022) · Microbiome · doi:10.1186/s40168-021-01216-5 · Cited by 193 · see citing works
Choice of assembly software has a critical impact on virome characterisation
Sutton, T., Clooney, A. G., Ryan, F. J., Ross, R. P., & Hill, C. (2019) · Microbiome · doi:10.1186/s40168-019-0626-5 · Cited by 153 · see citing works
Elucidation of complexity and prediction of interactions in microbial communities
Zúñiga, C., Zaramela, L. S., & Zengler, K. (2017) · Microbial Biotechnology · doi:10.1111/1751-7915.12855 · Cited by 152 · see citing works
Host-associated microbiomes drive structure and function of marine ecosystems
Wilkins, L. G. E., Leray, M., O’Dea, A., Yuen, B., Peixoto, R. S., Pereira, T. J., Bik, H. M., Coil, D. A., Duffy, J. E., Herre, E. A., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000533 · Cited by 141 · see citing works
Metagenomic Data Assembly – The Way of Decoding Unknown Microorganisms
Lapidus, A. L., & Korobeynikov, A. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.613791 · Cited by 134 · see citing works
Assembling metagenomes, one community at a time
van der Walt, A. J., Goethem, M. W. V., Ramond, J., Makhalanyane, T. P., Reva, O. N., & Cowan, D. A. (2017) · BMC Genomics · doi:10.1186/s12864-017-3918-9 · Cited by 122 · see citing works
Computational methods for chromosome-scale haplotype reconstruction
Garg, S. (2021) · Genome Biology · doi:10.1186/s13059-021-02328-9 · Cited by 116 · see citing works
The Carbon Footprint of Bioinformatics
Grealey, J., Lannelongue, L., Saw, W., Marten, J., Méric, G., Carmona, S. R., & Inouye, M. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac034 · Cited by 115 · see citing works
Beilsmith, K. R., Thoen, M. P. M., Brachi, B., Gloss, A. D., Khan, M. H., & Bergelson, J. M. (2018) · The Plant Journal · doi:10.1111/tpj.14170 · Cited by 98 · see citing works
Assembly methods for nanopore-based metagenomic sequencing: a comparative study
Latorre‐Pérez, A., Villalba‐Bermell, P., Pascual, J., Porcar, M., & Vilanova, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-70491-3 · Cited by 99 · see citing works
Brown, C., Keenum, I. M., Dai, D., Zhang, L., Vikesland, P. J., & Pruden, A. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-83081-8 · Cited by 100 · see citing works
Noyes, N. R., Weinroth, M. D., Parker, J. K., Dean, C. J., Lakin, S. M., Raymond, R., Rovira, P., Doster, E., Abdo, Z., Martin, J. N., et al. (2017) · Microbiome · doi:10.1186/s40168-017-0361-8 · Cited by 97 · see citing works
GraphBin: refined binning of metagenomic contigs using assembly graphs
Mallawaarachchi, V., Wickramarachchi, A., & Lin, Y. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa180 · Cited by 93 · see citing works
Optimizing and evaluating the reconstruction of Metagenome-assembled microbial genomes
Papudeshi, B. N., Haggerty, J. M., Doane, M. P., Morris, M. M., Walsh, K., Beattie, D. T., Pande, D., Zaeri, P., Silva, A. E. T., Thompson, F. L., et al. (2017) · BMC Genomics · doi:10.1186/s12864-017-4294-1 · Cited by 83 · see citing works
Current and Promising Approaches to Identify Horizontal Gene Transfer Events in Metagenomes
Douglas, G. M., & Langille, M. G. I. (2019) · Genome Biology and Evolution · doi:10.1093/gbe/evz184 · Cited by 79 · see citing works
Comparative metagenomics of hydrocarbon and methane seeps of the Gulf of Mexico
Vigneron, A., Alsop, E. B., Cruaud, P., Philibert, G., King, B., Baksmaty, L., Lavallée, D., Lomans, B. P., Kyrpides, N. C., Head, I. M., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-16375-5 · Cited by 77 · see citing works
Mahapatra, G. P., Raman, S., Nayak, S., Gouda, S., Das, G., & Patra, J. K. (2019) · Current Microbiology · doi:10.1007/s00284-019-01698-5 · Cited by 76 · see citing works
Terabase-scale metagenome coassembly with MetaHipMer
Hofmeyr, S. A., Egan, R. S., Georganas, E., Copeland, A., Riley, R. W., Clum, A., Eloe‐Fadrosh, E. A., Roux, S., Goltsman, E., Buluç, A., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-67416-5 · Cited by 73 · see citing works
Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data
Kashaf, S. S., Almeida, A., Segre, J. A., & Finn, R. D. (2021) · Nature Protocols · doi:10.1038/s41596-021-00508-2 · Cited by 72 · see citing works
Measuring metagenome diversity and similarity with Hill numbers
Ma, Z. S., & Li, L. (2018) · Molecular Ecology Resources · doi:10.1111/1755-0998.12923 · Cited by 70 · see citing works
Vollmers, J., Wiegand, S., Lenk, F., & Kaster, A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac294 · Cited by 56 · see citing works
MetaCompare 2.0: differential ranking of ecological and human health resistome risks
Rumi, M. A., Oh, M., Davis, B. C., Brown, C., Juvekar, A. S., Vikesland, P. J., Pruden, A. J., & Zhang, L. (2024) · FEMS Microbiology Ecology · doi:10.1093/femsec/fiae155 · Cited by 65 · 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
Visualizing metagenomic and metatranscriptomic data: A comprehensive review
Aplakidou, E., Vergoulidis, N., Chasapi, M. N., Venetsianou, N. K., Kokoli, M., Panagiotopoulou, E., Iliopoulos, I., Karatzas, E., Pafilis, E., Georgakopoulos‐Soares, I., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.060 · Cited by 62 · see citing works
Blaustein, R. A., McFarland, A. G., Maamar, S. B., Lopez, A. E., Castro-Wallace, S. L., & Hartmann, E. M. (2019) · mSystems · doi:10.1128/msystems.00281-18 · Cited by 62 · see citing works
Uncovering the genomic potential of the Amazon River microbiome to degrade rainforest organic matter
Júnior, C. D. S., Sarmento, H., de Miranda, F. P., Henrique‐Silva, F., & Logares, R. (2020) · Microbiome · doi:10.1186/s40168-020-00930-w · Cited by 58 · see citing works
What Is in Umbilicaria pustulata? A Metagenomic Approach to Reconstruct the Holo-Genome of a Lichen
Tzovaras, B. G., Segers, F. H. I. D., Bicker, A., Grande, F. D., Otte, J., Anvar, S. Y., Hankeln, T., Schmitt, I., & Ebersberger, I. (2020) · Genome Biology and Evolution · doi:10.1093/gbe/evaa049 · Cited by 57 · see citing works
Taubert, M., Grob, C., Crombie, A. T., Howat, A. M., Burns, O. J., Weber, M., Lott, C., Kaster, A., Vollmers, J., Jehmlich, N., et al. (2019) · Environmental Microbiology · doi:10.1111/1462-2920.14728 · Cited by 56 · see citing works
Metagenomic assemblies tend to break around antibiotic resistance genes
Abramova, A., Karkman, A., & Bengtsson‐Palme, J. (2024) · BMC Genomics · doi:10.1186/s12864-024-10876-0 · Cited by 56 · see citing works
Slizovskiy, I. B., Mukherjee, K., Dean, C., Boucher, C. A., & Noyes, N. R. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.01376 · Cited by 53 · see citing works
Evaluating de Novo Assembly and Binning Strategies for Time Series Drinking Water Metagenomes
Vosloo, S., Huo, L.-X., Anderson, C. L., Dai, Z.-H., Sevillano, M., & Pinto, A. (2021) · bioRxiv · doi:10.1128/spectrum.01434-21 · Cited by 43 · see citing works
A comparison of short-read, HiFi long-read, and hybrid strategies for genome-resolved metagenomics
Eisenhofer, R., Nesme, J., Santos‐Bay, L., Koziol, A., Sørensen, S. J., Alberdi, A., & Aizpurua, O. (2024) · Microbiology Spectrum · doi:10.1128/spectrum.03590-23 · Cited by 51 · see citing works
Asante, J., & Sekyere, J. O. (2019) · Environmental Microbiology Reports · doi:10.1111/1758-2229.12735 · Cited by 49 · see citing works
Hempel, C. A., Wright, N., Harvie, J., Hleap, J. S., Adamowicz, S. J., & Steinke, D. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac689 · Cited by 48 · see citing works
Jameson, E., Stephenson, J., Jones, H. J., Millard, A. D., Kaster, A., Purdy, K. J., Airs, R. L., Murrell, J. C., & Chen, Y. (2018) · The ISME Journal · doi:10.1038/s41396-018-0269-8 · Cited by 47 · see citing works
Gut microbiota in ALS: possible role in pathogenesis?
McCombe, P. A., Henderson, R. D., Lee, A., Lee, J. D., Woodruff, T. M., Restuadi, R., McRae, A., Wray, N. R., Ngo, S., & Steyn, F. J. (2019) · Expert Review of Neurotherapeutics · doi:10.1080/14737175.2019.1623026 · Cited by 46 · see citing works
Leyva, L. L., Brereton, N. J. B., & Koski, K. G. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.11.057 · Cited by 45 · see citing works
metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model
Wu, S., Feng, T., Tang, W., Qi, C., Gao, J., He, X., Wang, J., Zhou, H., & Fang, Z. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae085 · Cited by 43 · see citing works
Metagenomic approaches and opportunities in arid soil research
Ejaz, M. R., Badr, K., Hassan, Z. U., Al-Thani, R. F., & Jaoua, S. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176173 · Cited by 44 · see citing works
The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modelingPreprint
Cornman, A., West-Roberts, J. A., Camargo, A. P., Roux, S., Beracochea, M., Mirdita, M., Ovchinnikov, S., & Hwang, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.14.607850 · Cited by 45 · see citing works
Behera, B., Dehury, B., Rout, A. K., Patra, B., Mantri, N., Chakraborty, H. J., Sarkar, D., Kaushik, N., Bansal, V., Singh, I., et al. (2021) · Gene Reports · doi:10.1016/j.genrep.2021.101372 · Cited by 34 · see citing works
Contrasting Pathways for Anaerobic Methane Oxidation in Gulf of Mexico Cold Seep Sediments
Vigneron, A., Alsop, E. B., Cruaud, P., Philibert, G., King, B., Baksmaty, L., Lavallée, D., Lomans, B. P., Eloe‐Fadrosh, E. A., Kyrpides, N. C., et al. (2019) · mSystems · doi:10.1128/msystems.00091-18 · Cited by 42 · see citing works
Localized effect of treated wastewater effluent on the resistome of an urban watershed
Thornton, C. N., Tanner, W. D., VanDerslice, J. A., & Brazelton, W. J. (2020) · GigaScience · doi:10.1093/gigascience/giaa125 · Cited by 42 · see citing works
Bacterial genomic epidemiology with mixed samples
Mäklin, T., Kallonen, T., Alanko, J., Samuelsen, Ø., Hegstad, K., Mäkinen, V., Corander, J., Heinz, E., & Honkela, A. (2021) · Microbial Genomics · doi:10.1099/mgen.0.000691 · Cited by 42 · see citing works
Islam, R., Raju, R. S., Tasnim, N., Shihab, I. H., Bhuiyan, M. A., Araf, Y., & Islam, T. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab102 · Cited by 41 · see citing works
Fernández‐Orth, D., Miró, E., Brown-Jaque, M., Rodríguez‐Rubio, L., Espinal, P., Rodríguez-Navarro, J., González‐López, J. J., Muniesa, M., & Navarro, F. (2018) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dky540 · Cited by 39 · see citing works
Genome-resolved metagenomics using environmental and clinical samples
Kayani, M. U. R., Huang, W., Feng, R., & Chen, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab030 · Cited by 40 · see citing works
Computational methods and challenges in analyzing intratumoral microbiome data
Wang, Q., Liu, Z., Ma, A., Li, Z., Liu, B., & Ma, Q. (2023) · Trends in Microbiology · doi:10.1016/j.tim.2023.01.011 · Cited by 37 · see citing works
Methanethiol and Dimethylsulfide Cycling in Stiffkey Saltmarsh
Carrión, O., Pratscher, J., Kumari, R., Rostant, W. G., Haque, M. F. U., Murrell, J. C., & Todd, J. D. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01040 · Cited by 38 · see citing works
A method for achieving complete microbial genomes and improving bins from metagenomics data
Lui, L., Nielsen, T., & Arkin, A. (2020) · bioRxiv · doi:10.1371/journal.pcbi.1008972 · Cited by 37 · see citing works
Deshpande, A. S., & Fahrenfeld, N. L. (2022) · Water Research · doi:10.1016/j.watres.2022.118363 · Cited by 37 · see citing works
ASaiM: a Galaxy-based framework to analyze microbiota data
Batut, B., Gravouil, K., Defois, C., Hiltemann, S., Brugère, J., Peyretaillade, É., & Peyret, P. (2018) · GigaScience · doi:10.1093/gigascience/giy057 · Cited by 36 · see citing works
Practical evaluation of 11 de novo assemblers in metagenome assembly
Forouzan, E., Shariati, P., Maleki, M. S. M., Karkhane, A. A., & Yakhchali, B. (2018) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2018.06.007 · Cited by 36 · see citing works
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
Okazaki, Y., Fujinaga, S., Salcher, M. M., Callieri, C., Tanaka, A., Kohzu, A., Oyagi, H., Tamaki, H., & Nakano, S. (2021) · Microbiome · doi:10.1186/s40168-020-00974-y · Cited by 34 · see citing works
Gene Prediction in Metagenomic Fragments with Deep Learning
Zhang, S., Jin, X., & Zhang, T. (2017) · BioMed Research International · doi:10.1155/2017/4740354 · Cited by 33 · see citing works
Applying Genome-Resolved Metagenomics to Deconvolute the Halophilic Microbiome
Uritskiy, G., & DiRuggiero, J. (2019) · Genes · doi:10.3390/genes10030220 · Cited by 33 · see citing works
Kohl, K. D. (2017) · Integrative and Comparative Biology · doi:10.1093/icb/icx013 · Cited by 31 · see citing works
Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance
Imchen, M., Moopantakath, J., Kumavath, R., Barh, D., Tiwari, S., Ghosh, P., & Azevedo, V. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.563975 · Cited by 33 · see citing works
Novel ssDNA Viruses Detected in the Virome of Bleached, Habitat-Forming Kelp Ecklonia radiata
Beattie, D. T., Lachnit, T., Dinsdale, E. A., Thomas, T., & Steinberg, P. D. (2018) · Frontiers in Marine Science · doi:10.3389/fmars.2017.00441 · Cited by 32 · see citing works
Slizovskiy, I. B., Oliva, M., Settle, J., Zyskina, L. V., Prosperi, M., Boucher, C. A., & Noyes, N. R. (2022) · Microbiome · doi:10.1186/s40168-022-01368-y · Cited by 31 · see citing works
Ecosystem-specific microbiota and microbiome databases in the era of big data
Lobanov, V. P., Gobet, A., & Joyce, A. (2022) · Environmental Microbiome · doi:10.1186/s40793-022-00433-1 · Cited by 31 · see citing works
High-throughput DNA sequencing technologies for water and wastewater analysis
Chan, A. W. Y., Naphtali, J., & Schellhorn, H. E. (2019) · Science Progress · doi:10.1177/0036850419881855 · Cited by 30 · see citing works
Accurate Binning of Metagenomic Contigs Using Composition, Coverage, and Assembly Graphs
Mallawaarachchi, V., & Lin, Y. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2022.0262 · Cited by 30 · see citing works
A Review of Scalable Bioinformatics Pipelines
Fjukstad, B., & Bongo, L. A. (2017) · Data Science and Engineering · doi:10.1007/s41019-017-0047-z · Cited by 28 · see citing works
Machine learning meets genome assembly
Padovani, K., Setúbal, J. C., de Carvalho, A. C. P. L. F., Oliveira, G., Château, A., & Alves, R. C. D. O. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby072 · Cited by 28 · see citing works
Extreme Scale De Novo Metagenome Assembly
Georganas, E., Egan, R. S., Hofmeyr, S. A., Goltsman, E., Arndt, B., Tritt, A., Buluç, A., Oliker, L., & Yelick, K. (2018) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2018.00013 · Cited by 27 · see citing works
Rathour, R., Gupta, J., Kumar, M., Hiloidhari, M., Mehrotra, A. K., & Thakur, I. S. (2017) · Genome Announcements · doi:10.1128/genomea.01029-17 · Cited by 27 · see citing works
Menon, R. K., Gomez, A. M., Brandt, B. W., Leung, Y. Y., Gopinath, D., Watt, R. M., Wim Crielaard, A., Nelson, K. E., & Botelho, M. G. (2019) · Scientific Reports · doi:10.1038/s41598-019-55056-3 · Cited by 27 · see citing works
Goussarov, G., Mysara, M., Vandamme, P. A. R., & Houdt, R. V. (2022) · MicrobiologyOpen · doi:10.1002/mbo3.1298 · Cited by 26 · see citing works
Doster, E., Rovira, P., Noyes, N. R., Burgess, B. A., Yang, X., Weinroth, M. D., Linke, L., Magnuson, R. J., Boucher, C. A., Belk, K. E., et al. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02499 · Cited by 26 · see citing works
CRISPRclassify: Repeat-Based Classification of CRISPR Loci
Nethery, M. A., Korvink, M., Makarova, K. S., Wolf, Y. I., Koonin, E. V., & Barrangou, R. (2021) · The CRISPR Journal · doi:10.1089/crispr.2021.0021 · Cited by 25 · see citing works
FragGeneScanRs: faster gene prediction for short reads
der Jeugt, F. V., Dawyndt, P., & Mesuere, B. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04736-5 · Cited by 25 · see citing works
Koutsandreas, T., Ladoukakis, E., Pilalis, E. D., Zarafeta, D., Kolisis, F. N., Σκρέτας, Γ., & Chatziioannou, A. A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00469 · Cited by 25 · see citing works
Evaluating Metagenome Assembly on a Simple Defined Community with Many Strain VariantsPreprint
Awad, S., Irber, L. C., & Brown, C. T. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/155358 · Cited by 25 · see citing works
Haryono, M., Law, Y., Arumugam, K., Liew, L. C. W., Nguyen, T. Q. N., Drautz‐Moses, D. I., Schuster, S. C., Wuertz, S., & Williams, R. B. H. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.869135 · Cited by 24 · see citing works
Iron-Fueled Life in the Continental Subsurface: Deep Mine Microbial Observatory, South Dakota, USA
Casar, C. P., Momper, L., Kruger, B. R., & Osburn, M. R. (2021) · Applied and Environmental Microbiology · doi:10.1128/aem.00832-21 · Cited by 23 · see citing works
MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics
Churcheward, B., Millet, M., Bihouée, A., Guillaume Fertin, A., & Chaffron, S. (2022) · mSystems · doi:10.1128/msystems.00432-22 · Cited by 22 · see citing works
Identifying Eukaryotes and Factors Influencing Their Biogeography in Drinking Water Metagenomes
Gabrielli, M., Dai, Z., Delafont, V., Timmers, P. H. A., van der Wielen, P. W. J. J., Antonelli, M., & Pinto, A. J. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.2c09010 · Cited by 22 · see citing works
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Methods and Developments in Graphical Pangenomics
Outten, J., & Warren, A. S. (2021) · Journal of the Indian Institute of Science · doi:10.1007/s41745-021-00255-z · Cited by 19 · see citing works
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- Authors
- Vollmers, J., Wiegand, S., & Kaster, A.-K.
- Affiliations
- Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures (DE)
- Corresponding author
- Kaster, Anne-Kristin
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-01-18
- Volume
- 12
- Issue
- 1
- Pages
- e0169662
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5242441
- OpenAlex
- W2572263783
- Cited by
- 240
- References
- 96
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 28 · 2023: 15 · 2022: 35 · 2021: 37 · 2020: 27 · 2019: 32 · 2018: 25 · 2017: 22 · 2015: 1
- FWCI
- 11.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Johns Hopkins University
- University of East Anglia
- Max-Planck-Institut für Terrestrische Mikrobiologie
- Leibniz-Gemeinschaft
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Keywords
- metagenome assembly, assembler comparison, shotgun sequencing, low-abundance taxa, phylogenetic profiles, algal biofilms, CAMI challenge, mock communities, ease of use
- MeSH terms
- Phylogeny, Software, Soil Microbiology, Macrocystis, Biodiversity, Metagenome, Metagenomics, High-Throughput Nucleotide Sequencing, Forests
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