• This record comes from PubMed

MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy

. 2024 Feb ; 24 (2) : e13904. [epub] 20231123

Language English Country England, Great Britain Media print-electronic

Document type Journal Article

Grant support
460129525 Deutsche Forschungsgemeinschaft
2019/03396-9 Fundação de Amparo à Pesquisa do Estado de São Paulo
2022/03534-5 Fundação de Amparo à Pesquisa do Estado de São Paulo
German Network for Bioinformatics Infrastructure
VH-NG-1248 Helmholtz-Gemeinschaft
Lundin Energy Norway

Several computational frameworks and workflows that recover genomes from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is difficult for scientists with little bioinformatics experience to evaluate quality, annotate genes, dereplicate, assign taxonomy and calculate relative abundance and coverage of genomes belonging to different domains. MuDoGeR is a user-friendly tool tailored for those familiar with Unix command-line environment that makes it easy to recover genomes of prokaryotes, eukaryotes and viruses from metagenomes, either alone or in combination. We tested MuDoGeR using 24 individual-isolated genomes and 574 metagenomes, demonstrating the applicability for a few samples and high throughput. While MuDoGeR can recover eukaryotic viral sequences, its characterization is predominantly skewed towards bacterial and archaeal viruses, reflecting the field's current state. However, acting as a dynamic wrapper, the MuDoGeR is designed to constantly incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR. Additionally, MuDoGeR is also available as a Singularity container.

See more in PubMed

Albertsen, M., Hugenholtz, P., Skarshewski, A., Nielsen, K. L., Tyson, G. W., & Nielsen, P. H. (2013). Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nature Biotechnology, 31(6), 533-538. https://doi.org/10.1038/nbt.2579

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). Binning metagenomic contigs by coverage and composition. Nature Methods, 11(11), 1144-1146. https://doi.org/10.1038/nmeth.3103

Besemer, J., Lomsadze, A., & Borodovsky, M. (2001). GeneMarkS: A self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Research, 29(12), 2607-2618.

Bin Jang, H., Bolduc, B., Zablocki, O., Kuhn, J. H., Roux, S., Adriaenssens, E. M., Brister, J. R., Kropinski, A. M., Krupovic, M., Lavigne, R., Turner, D., & Sullivan, M. B. (2019). Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nature Biotechnology, 37(6), 632-639. https://doi.org/10.1038/s41587-019-0100-8

Blankenberg, D., Kuster, G. V., Coraor, N., Ananda, G., Lazarus, R., Mangan, M., Nekrutenko, A., & Taylor, J. (2010). Galaxy: A web-based genome analysis tool for experimentalists. Current Protocols in Molecular Biology, 89(1), 19.10.1-19.10.21. https://doi.org/10.1002/0471142727.mb1910s89

Bornemann, T. L. V., Esser, S. P., Stach, T. L., Burg, T., & Probst, A. J. (2020). uBin - A manual refining tool for metagenomic bins designed for educational purposes. bioRxiv, 2020.07.15.204776. https://doi.org/10.1101/2020.07.15.204776

Breitwieser, F. P., Lu, J., & Salzberg, S. L. (2018). A review of methods and databases for metagenomic classification and assembly. Briefings in Bioinformatics, 20(4), 1125-1139. https://doi.org/10.1093/bib/bbx120

Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights, D., Koenig, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., … Knight, R. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7(5), 335-336. https://doi.org/10.1038/nmeth.f.303

Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P., & Parks, D. H. (2020). GTDB-Tk: A toolkit to classify genomes with the genome taxonomy database. Bioinformatics, 36(6), 1925-1927. https://doi.org/10.1093/bioinformatics/btz848

Churcheward, B., Millet, M., Bihouée, A., Fertin, G., & Chaffron, S. (2022). MAGNETO: An automated workflow for genome-resolved metagenomics. mSystems, 7(4), e00432-22. https://doi.org/10.1128/msystems.00432-22

Corrêa, F. B., Saraiva, J. P., Stadler, P. F., & da Rocha, U. N. (2020). TerrestrialMetagenomeDB: A public repository of curated and standardized metadata for terrestrial metagenomes. Nucleic Acids Research, 48(D1), D626-D632. https://doi.org/10.1093/nar/gkz994

da Rocha, U. N., Kasmanas, J. C., Toscan, R., Sanches, D. S., Magnusdottir, S., & Saraiva, J. P. (2023). Simulation of 69 microbial communities indicates sequencing depth and false positives are major drivers of bias in prokaryotic metagenome-assembled genome recovery. bioRxiv, p. 2023.05.02.539054. https://doi.org/10.1101/2023.05.02.539054

Delmont, T. O., & Eren, A. M. (2016). Identifying contamination with advanced visualization and analysis practices: Metagenomic approaches for eukaryotic genome assemblies. PeerJ, 4, e1839. https://doi.org/10.7717/peerj.1839

Dias, O., Saraiva, J., Faria, C., Ramirez, M., Pinto, F., & Rocha, I. (2019). iDS372, a phenotypically reconciled model for the metabolism of Streptococcus pneumoniae strain R6. Frontiers in Microbiology, 10, 1283. https://doi.org/10.3389/fmicb.2019.01283

Eren, A. M., Esen, Ö. C., Quince, C., Vineis, J. H., Morrison, H. G., Sogin, M. L., & Delmont, T. O. (2015). Anvi'o: An advanced analysis and visualization platform for ‘omics data. PeerJ, 3, e1319. https://doi.org/10.7717/peerj.1319

Evans, P. N., Parks, D. H., Chadwick, G. L., Robbins, S. J., Orphan, V. J., Golding, S. D., & Tyson, G. W. (2015). Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science, 350(6259), 434-438. https://doi.org/10.1126/science.aac7745

Galiez, C., Siebert, M., Enault, F., Vincent, J., & Söding, J. (2017). WIsH: Who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics, 33(19), 3113-3114. https://doi.org/10.1093/bioinformatics/btx383

Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., & Köster, J. (2018). Bioconda: Sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 476. https://doi.org/10.1038/s41592-018-0046-7

Guo, J., Bolduc, B., Zayed, A. A., Varsani, A., Dominguez-Huerta, G., Delmont, T. O., Pratama, A. A., Gazitúa, M. C., Vik, D., Sullivan, M. B., & Roux, S. (2021). VirSorter2: A multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome, 9(1), 37. https://doi.org/10.1186/s40168-020-00990-y

Holt, C., & Yandell, M. (2011). MAKER2: An annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinformatics, 12(1), 491. https://doi.org/10.1186/1471-2105-12-491

Kallies, R., Hölzer, M., Brizola Toscan, R., Nunes da Rocha, U., Anders, J., Marz, M., & Chatzinotas, A. (2019). Evaluation of sequencing library preparation protocols for viral metagenomic analysis from pristine aquifer groundwaters. Viruses, 11(6), 484. https://doi.org/10.3390/v11060484

Kang, D. D., Froula, J., Egan, R., & Wang, Z. (2015). MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ, 3, e1165. https://doi.org/10.7717/peerj.1165

Kasmanas, J. C., Bartholomäus, A., Corrêa, F. B., Tal, T., Jehmlich, N., Herberth, G., von Bergen, M., Stadler, P. F., Carvalho, A. C. P. L. F., & Nunes da Rocha, U. (2021). HumanMetagenomeDB: A public repository of curated and standardized metadata for human metagenomes. Nucleic Acids Research, 49(D1), D743-D750. https://doi.org/10.1093/nar/gkaa1031

Keller-Costa, T., Lago-Lestón, A., Saraiva, J. P., Toscan, R., Silva, S. G., Gonçalves, J., Cox, C. J., Kyrpides, N., Nunes da Rocha, U., & Costa, R. (2021). Metagenomic insights into the taxonomy, function, and dysbiosis of prokaryotic communities in octocorals. Microbiome, 9(1), 72. https://doi.org/10.1186/s40168-021-01031-y

Kieft, K., Zhou, Z., & Anantharaman, K. (2020). VIBRANT: Automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome, 8(1), 90. https://doi.org/10.1186/s40168-020-00867-0

Kieser, S., Brown, J., Zdobnov, E. M., Trajkovski, M., & McCue, L. A. (2020). ATLAS: A Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinformatics, 21(1), 257. https://doi.org/10.1186/s12859-020-03585-4

Koonin, E. V., Krupovic, M., & Dolja, V. V. (2023). The global virome: How much diversity and how many independent origins? Environmental Microbiology, 25(1), 40-44. https://doi.org/10.1111/1462-2920.16207

Koren, S., Treangen, T. J., & Pop, M. (2011). Bambus 2: Scaffolding metagenomes. Bioinformatics, 27(21), 2964-2971. https://doi.org/10.1093/bioinformatics/btr520

Krakau, S., Straub, D., Gourlé, H., Gabernet, G., & Nahnsen, S. (2022). Nf-core/mag: A best-practice pipeline for metagenome hybrid assembly and binning. NAR Genomics and Bioinformatics, 4(1), lqac007. https://doi.org/10.1093/nargab/lqac007

Kurtzer, G. M., Sochat, V., & Bauer, M. W. (2017). Singularity: Scientific containers for mobility of compute. PLoS One, 12(5), e0177459. https://doi.org/10.1371/journal.pone.0177459

Leliaert, F., Smith, D. R., Moreau, H., Herron, M. D., Verbruggen, H., Delwiche, C. F., & De Clerck, O. (2012). Phylogeny and molecular evolution of the green algae. Critical Reviews in Plant Sciences, 31(1), 1-46. https://doi.org/10.1080/07352689.2011.615705

Li, D., Luo, R., Liu, C.-M., Leung, C.-M., Ting, H.-F., Sadakane, K., Yamashita, H., & Lam, T.-W. (2016). MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods (San Diego, Calif.), 102, 3-11. https://doi.org/10.1016/j.ymeth.2016.02.020

Liu, B., Sträuber, H., Saraiva, J., Harms, H., Silva, S. G., Kasmanas, J. C., Kleinsteuber, S., & Nunes da Rocha, U. (2022). Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture. Microbiome, 10(1), 48. https://doi.org/10.1186/s40168-021-01219-2

López-Mondéjar, R., Tláskal, V., Větrovský, T., Štursová, M., Toscan, R., Nunes da Rocha, U., & Baldrian, P. (2020). Metagenomics and stable isotope probing reveal the complementary contribution of fungal and bacterial communities in the recycling of dead biomass in forest soil. Soil Biology and Biochemistry, 148, 107875. https://doi.org/10.1016/j.soilbio.2020.107875

Lu, J., Rincon, N., Wood, D. E., Breitwieser, F. P., Pockrandt, C., Langmead, B., Salzberg, S. L., & Steinegger, M. (2022). Metagenome analysis using the kraken software suite. Nature Protocols, 17(12), 2839. https://doi.org/10.1038/s41596-022-00738-y

Mangul, S., Mosqueiro, T., Abdill, R. J., Duong, D., Mitchell, K., Sarwal, V., Hill, B., Brito, J., Littman, R. J., Statz, B., Lam, A. K.-M., Dayama, G., Grieneisen, L., Martin, L. S., Flint, J., Eskin, E., & Blekhman, R. (2019). Challenges and recommendations to improve the installability and archival stability of omics computational tools. PLoS Biology, 17(6), e3000333. https://doi.org/10.1371/journal.pbio.3000333

McMurdie, P. J., & Holmes, S. (2013). Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One, 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217

Melkonian, C., Fillinger, L., Atashgahi, S., da Rocha, U. N., Kuiper, E., Olivier, B., Braster, M., Gottstein, W., Helmus, R., Parsons, J. R., Smidt, H., van der Waals, M., Gerritse, J., Brandt, B. W., Röling, W. F. M., Molenaar, D., & van Spanning, R. J. M. (2021). High biodiversity in a benzene-degrading nitrate-reducing culture is sustained by a few primary consumers. Communications Biology, 4(1), 530. https://doi.org/10.1038/s42003-021-01948-y

Meyer, F., Lesker, T.-R., Koslicki, D., Fritz, A., Gurevich, A., Darling, A. E., Sczyrba, A., Bremges, A., & McHardy, A. C. (2021). Tutorial: Assessing metagenomics software with the CAMI benchmarking toolkit. Nature Protocols, 16(4), 1801. https://doi.org/10.1038/s41596-020-00480-3

Namiki, T., Hachiya, T., Tanaka, H., & Sakakibara, Y. (2012). MetaVelvet: An extension of velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Research, 40(20), e155. https://doi.org/10.1093/nar/gks678

Nayfach, S., Camargo, A. P., Schulz, F., Eloe-Fadrosh, E., Roux, S., & Kyrpides, N. C. (2021). CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nature Biotechnology, 39(5), 578-585. https://doi.org/10.1038/s41587-020-00774-7

Noor, E., Eden, E., Milo, R., & Alon, U. (2010). Central carbon metabolism as a minimal biochemical walk between precursors for biomass and energy. Molecular Cell, 39(5), 809-820. https://doi.org/10.1016/j.molcel.2010.08.031

Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017). metaSPAdes: A new versatile metagenomic assembler. Genome Research, 27(5), 824-834. https://doi.org/10.1101/gr.213959.116

Oliveira Monteiro, L. M., Saraiva, J. P., Brizola Toscan, R., Stadler, P. F., Silva-Rocha, R., & Nunes da Rocha, U. (2022). PredicTF: Prediction of bacterial transcription factors in complex microbial communities using deep learning. Environmental Microbiomes, 17(1), 7. https://doi.org/10.1186/s40793-021-00394-x

Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., & Tyson, G. W. (2015). CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research, 25(7), 1043-1055. https://doi.org/10.1101/gr.186072.114

Parks, D. H., Rinke, C., Chuvochina, M., Chaumeil, P.-A., Woodcroft, B. J., Evans, P. N., Hugenholtz, P., & Tyson, G. W. (2017). Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nature Microbiology, 2(11), 1533-1542. https://doi.org/10.1038/s41564-017-0012-7

Pawlowski, J., Audic, S., Adl, S., Bass, D., Belbahri, L., Berney, C., Bowser, S. S., Cepicka, I., Decelle, J., Dunthorn, M., Fiore-Donno, A. M., Gile, G. H., Holzmann, M., Jahn, R., Jirků, M., Keeling, P. J., Kostka, M., Kudryavtsev, A., Lara, E., … de Vargas, C. (2012). CBOL protist working group: Barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLoS Biology, 10(11), e1001419. https://doi.org/10.1371/journal.pbio.1001419

Pearman, W. S., Freed, N. E., & Silander, O. K. (2020). Testing the advantages and disadvantages of short- and long- read eukaryotic metagenomics using simulated reads. BMC Bioinformatics, 21(1), 220. https://doi.org/10.1186/s12859-020-3528-4

Peng, Y., Leung, H. C. M., Yiu, S. M., & Chin, F. Y. L. (2012). IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics, 28(11), 1420-1428. https://doi.org/10.1093/bioinformatics/bts174

Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A., & Sun, F. (2017). VirFinder: A novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome, 5(1), 69. https://doi.org/10.1186/s40168-017-0283-5

Roux, S., Adriaenssens, E. M., Dutilh, B. E., Koonin, E. V., Kropinski, A. M., Krupovic, M., Kuhn, J. H., Lavigne, R., Brister, J. R., Varsani, A., Amid, C., Aziz, R. K., Bordenstein, S. R., Bork, P., Breitbart, M., Cochrane, G. R., Daly, R. A., Desnues, C., Duhaime, M. B., … Eloe-Fadrosh, E. A. (2019). Minimum information about an uncultivated virus genome (MIUViG). Nature Biotechnology, 37(1), 29-37. https://doi.org/10.1038/nbt.4306

Saary, P., Mitchell, A. L., & Finn, R. D. (2020). Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC. Genome Biology, 21(1), 244. https://doi.org/10.1186/s13059-020-02155-4

Santos-Medellin, C., Zinke, L. A., ter Horst, A. M., Gelardi, D. L., Parikh, S. J., & Emerson, J. B. (2021). Viromes outperform total metagenomes in revealing the spatiotemporal patterns of agricultural soil viral communities. The ISME Journal, 15(7), 1956-1970. https://doi.org/10.1038/s41396-021-00897-y

Saraiva, J. P., Bartholomäus, A., Kallies, R., Gomes, M., Bicalho, M., Coelho Kasmanas, J., Vogt, C., Chatzinotas, A., Stadler, P., Dias, O., & Nunes da Rocha, U. (2021). OrtSuite: From genomes to prediction of microbial interactions within targeted ecosystem processes. Life Science Alliance, 4(12), e202101167. https://doi.org/10.26508/lsa.202101167

Saraiva, J. P., Bartholomäus, A., Toscan, R. B., Baldrian, P., & Nunes da Rocha, U. (2023). Recovery of 197 eukaryotic bins reveals major challenges for eukaryote genome reconstruction from terrestrial metagenomes. Molecular Ecology Resources, 23(5), 1066-1076. https://doi.org/10.1111/1755-0998.13776

Saraiva, J. P., Worrich, A., Karakoç, C., Kallies, R., Chatzinotas, A., Centler, F., & Nunes da Rocha, U. (2021). Mining synergistic microbial interactions: A roadmap on how to integrate multi-omics data. Microorganisms, 9(4), 840. https://doi.org/10.3390/microorganisms9040840

Saraiva, J. P. L. F., Zubiria-Barrera, C., Klassert, T. E., Lautenbach, M. J., Blaess, M., Claus, R. A., Slevogt, H., & König, R. (2017). Combination of classifiers identifies fungal-specific activation of lysosome genes in human monocytes. Frontiers in Microbiology, 8, 2366. https://doi.org/10.3389/fmicb.2017.02366

Seemann, T. (2014). Prokka: Rapid prokaryotic genome annotation. Bioinformatics (Oxford, England), 30(14), 2068-2069. https://doi.org/10.1093/bioinformatics/btu153

Sharon, I., Morowitz, M. J., Thomas, B. C., Costello, E. K., Relman, D. A., & Banfield, J. F. (2013). Time series community genomics analysis reveals rapid shifts in bacterial species, strains, and phage during infant gut colonization. Genome Research, 23(1), 111-120. https://doi.org/10.1101/gr.142315.112

Sieber, C. M. K., Probst, A. J., Sharrar, A., Thomas, B. C., Hess, M., Tringe, S. G., & Banfield, J. F. (2018). Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nature Microbiology, 3(7), 836-843. https://doi.org/10.1038/s41564-018-0171-1

Taur, Y., Coyte, K., Schluter, J., Robilotti, E., Figueroa, C., Gjonbalaj, M., Littmann, E. R., Ling, L., Miller, L., Gyaltshen, Y., Fontana, E., Morjaria, S., Gyurkocza, B., Perales, M.-A., Castro-Malaspina, H., Tamari, R., Ponce, D., Koehne, G., Barker, J., … Xavier, J. B. (2018). Reconstitution of the gut microbiota of antibiotic-treated patients by autologous fecal microbiota transplant. Science Translational Medicine, 10(460), eaap9489. https://doi.org/10.1126/scitranslmed.aap9489

Tisza, M. J., & Buck, C. B. (2021). A catalog of tens of thousands of viruses from human metagenomes reveals hidden associations with chronic diseases. Proceedings of the National Academy of Sciences of the United States of America, 118(23), e2023202118. https://doi.org/10.1073/pnas.2023202118

Tláskal, V., Brabcová, V., Větrovský, T., Jomura, M., López-Mondéjar, R., Monteiro, L. M. O., Saraiva, J. P., Human, Z. R., Cajthaml, T., da Rocha, U. N., & Baldrian, P. (2021). Complementary roles of Wood-inhabiting fungi and bacteria facilitate deadwood decomposition. mSystems, 6(1), e01078-20. https://doi.org/10.1128/mSystems.01078-20

Tyson, G. W., Chapman, J., Hugenholtz, P., Allen, E. E., Ram, R. J., Richardson, P. M., Solovyev, V. V., Rubin, E. M., Rokhsar, D. S., & Banfield, J. F. (2004). Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature, 428(6978), 37-43. https://doi.org/10.1038/nature02340

Uritskiy, G. V., DiRuggiero, J., & Taylor, J. (2018). MetaWRAP-A flexible pipeline for genome-resolved metagenomic data analysis. Microbiome, 6(1), 158. https://doi.org/10.1186/s40168-018-0541-1

Waterhouse, R. M., Seppey, M., Simão, F. A., Manni, M., Ioannidis, P., Klioutchnikov, G., Kriventseva, E. V., & Zdobnov, E. M. (2017). BUSCO applications from quality assessments to gene prediction and phylogenomics. Molecular Biology and Evolution, 35, 543-548. https://doi.org/10.1093/molbev/msx319

West, P. T., Probst, A. J., Grigoriev, I. V., Thomas, B. C., & Banfield, J. F. (2018). Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Research, 28(4), 569-580. https://doi.org/10.1101/gr.228429.117

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18

Wu, Y.-W., Simmons, B. A., & Singer, S. W. (2016). MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics (Oxford, England), 32(4), 605-607. https://doi.org/10.1093/bioinformatics/btv638

Yandell, M., & Ence, D. (2012). A beginner's guide to eukaryotic genome annotation. Nature Reviews Genetics, 13(5), 329-342. https://doi.org/10.1038/nrg3174

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...