A Review of bioinformatics tools for bio-prospecting from metagenomic sequence data

Prokka: rapid prokaryotic genome annotation

Seemann, T. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu153 · Cited by 18,512 · see citing works

The RAST Server: Rapid Annotations using Subsystems Technology

Aziz, R. K., Bartels, D., Best, A. A., DeJongh, M., Disz, T. L., Edwards, R. A., Formsma, K., Gerdes, S., Glass, E. M., Kubal, M., et al. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-75 · Cited by 11,777 · 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

Velvet: Algorithms for de novo short read assembly using de Bruijn graphsPreprint

Zerbino, D. R., & Birney, E. (2008) · Genome Research · doi:10.1101/gr.074492.107 · Cited by 9,387 · see citing works

Space/time trade-offs in hash coding with allowable errors

Bloom, B. (1970) · CACM · doi:10.1145/362686.362692 · Cited by 8,424 · see citing works

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 · see citing works

Versatile and open software for comparing large genomes

Kurtz, S., Phillippy, A., Delcher, A. L., Smoot, M., Shumway, M., Antonescu, C., & Salzberg, S. L. (2004) · Genome Biology · doi:10.1186/gb-2004-5-2-r12 · Cited by 5,254 · see citing works

KEGG for integration and interpretation of large-scale molecular data sets

Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr988 · Cited by 4,848 · see citing works

Environmental genome shotgun sequencing of the sargasso sea

Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D., Eisen, J. A., Wu, D., Paulsen, I., Nelson, K. E., Nelson, W., et al. (2004) · Science · doi:10.1126/science.1093857 · Cited by 4,376 · see citing works

Coming of age: ten years of next-generation sequencing technologies

Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.49 · Cited by 4,352 · see citing works

Prediction of complete gene structures in human genomic DNA

Burge, C., & Karlin, S. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.0951 · Cited by 4,211 · see citing works

Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data

Chin, C., Alexander, D., Marks, P., Klammer, A., Drake, J., Heiner, C., Clum, A., Copeland, A., Huddleston, J., Eichler, E., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2474 · Cited by 4,008 · see citing works

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395 · see citing works

Identifying bacterial genes and endosymbiont DNA with Glimmer

Delcher, A. L., Bratke, K. A., Powers, E. C., & Salzberg, S. L. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm009 · Cited by 3,141 · see citing works

IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth

Peng, Y., Leung, H. C. M., Yiu, S., & Chin, F. Y. L. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts174 · Cited by 2,997 · see citing works

Structure and function of the global ocean microbiome

Sunagawa, S., Coelho, L. P., Chaffron, S., Kultima, J. R., Labadie, K., Salazar, G., Djahanschiri, B., Zeller, G., Mende, D. R., Alberti, A., et al. (2015) · Science · doi:10.1126/science.1261359 · Cited by 2,945 · 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

A Combined Transmembrane Topology and Signal Peptide Prediction Method

Käll, L., Krogh, A., & Sonnhammer, E. L. L. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.03.016 · Cited by 2,474 · see citing works

Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products

Handelsman, J., Rondon, M. R., Brady, S. F., Clardy, J., & Goodman, R. M. (1998) · Chemistry & Biology · doi:10.1016/s1074-5521(98)90108-9 · Cited by 2,007 · see citing works

Ab initio gene identification in metagenomic sequences

Zhu, W., Lomsadze, A., & Borodovsky, M. Y. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq275 · Cited by 1,793 · see citing works

Ten years of next-generation sequencing technology

van Dijk, E., Auger, H., Jaszczyszyn, Y., & Thermes, C. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.07.001 · Cited by 1,632 · see citing works

An Eulerian path approach to DNA fragment assembly

Pevzner, P. A., Tang, H., & Waterman, M. S. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.171285098 · Cited by 1,352 · see citing works

Metagenomic discovery of biomass-degrading genes and genomes from cow rumen

Hess, M., Sczyrba, A., Egan, R., Kim, T.-W., Chokhawala, H., Schroth, G., Luo, S., Clark, D. S., Chen, F., Zhang, T., et al. (2011) · Science · doi:10.1126/science.1200387 · Cited by 1,319 · see citing works

The InterPro protein families database: the classification resource after 15 years

Mitchell, A., Chang, H.-Y., Daugherty, L. C., Fraser, M., Hunter, S., López, R., McAnulla, C., McMenamin, C., Nuka, G., Pesseat, S., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1243 · Cited by 1,276 · see citing works

Reorganizing the protein space at the Universal Protein Resource (UniProt)

The UniProt Consortium (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr981 · Cited by 1,223 · see citing works

The Reactome pathway Knowledgebase

Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 1,201 · see citing works

MetaGene: prokaryotic gene finding from environmental genome shotgun sequences

Noguchi, H., Park, J., & Takagi, T. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl723 · Cited by 1,163 · see citing works

A transposase strategy for creating libraries of circularly permuted proteins

Mehta, M., Liu, S., & Silberg, J. J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks060 · Cited by 1,042 · see citing works

InterPro in 2011: new developments in the family and domain prediction database

Hunter, S., Jones, P., Mitchell, A., Apweiler, R., Attwood, T. K., Bateman, A. G., Bernard, T., Binns, D., Peer Bork, B., Burge, S., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr948 · Cited by 1,036 · see citing works

Hybrid error correction and de novo assembly of single-molecule sequencing reads

Koren, S., Schatz, M. C., Walenz, B. P., Martin, J. V., Howard, J. T., Ganapathy, G., Wang, Z., Rasko, D. A., McCombie, W. R., Jarvis, E. D., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2280 · Cited by 993 · see citing works

Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.Preprint

Ter-Hovhannisyan, V., Lomsadze, A., Chernoff, Y., & Borodovsky, M. (2008) · Genome Research · doi:10.1101/gr.081612.108 · Cited by 919 · see citing works

FragGeneScan: predicting genes in short and error-prone reads

Rho, M., Tang, H., & Ye, Y. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq747 · Cited by 838 · see citing works

Ensembl 2012

Flicek, P., Amode, M. R., Barrell, D. G., Beal, K. M., Brent, S., Carvalho‐Silva, D., Clapham, P., Coates, G. M. P., Fairley, S., Fitzgerald, S. F., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr991 · Cited by 818 · see citing works

Next generation sequencing technology: Advances and applications

Buermans, H. P. J., & den Dunnen, J. T. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.06.015 · Cited by 761 · see citing works

MetaQUAST: evaluation of metagenome assemblies

Mikheenko, A., Saveliev, V., & Gurevich, A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv697 · Cited by 711 · see citing works

Gene: a gene-centered information resource at NCBI

Brown, G., Hem, V., Katz, K., Ovetsky, M., Wallin, C., Ermolaeva, O. D., Tolstoy, I., Tatusova, T. A., Pruitt, K. D., Maglott, D. R., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1055 · Cited by 706 · see citing works

WikiPathways: Pathway Editing for the People

Pico, A. R., Kelder, T., van Iersel, M. P., Hanspers, K., Conklin, B. R., & Evelo, C. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060184 · Cited by 701 · see citing works

MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads

Namiki, T., Hachiya, T., Tanaka, H. L., & Sakakibara, Y. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks678 · Cited by 677 · see citing works

MetaGeneAnnotator: Detecting Species-Specific Patterns of Ribosomal Binding Site for Precise Gene Prediction in Anonymous Prokaryotic and Phage Genomes

Noguchi, H., Taniguchi, T., & Itoh, T. (2008) · DNA Research · doi:10.1093/dnares/dsn027 · Cited by 655 · see citing works

Improved data analysis for the MinION nanopore sequencer

Jain, M., Fiddes, I. T., Miga, K. H., Olsen, H. E., Paten, B., & Akeson, M. A. (2015) · Nature Methods · doi:10.1038/nmeth.3290 · Cited by 639 · see citing works

Ray Meta: scalable de novo metagenome assembly and profiling

Boisvert, S., Raymond, F., Godzaridis, É., Laviolette, F., & Corbeil, J. (2012) · Genome biology · doi:10.1186/gb-2012-13-12-r122 · Cited by 593 · see citing works

Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models

Brady, A., & Salzberg, S. L. (2009) · Nature Methods · doi:10.1038/nmeth.1358 · Cited by 526 · see citing works

WikiPathways: building research communities on biological pathways

Kelder, T., van Iersel, M., Hanspers, K., Kutmon, M., Conklin, B. R., Evelo, C. T., & Pico, A. R. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1074 · Cited by 521 · see citing works

Sequence assembly demystified

Nagarajan, N., & Pop, M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3367 · Cited by 474 · see citing works

The rumen microbial metagenome associated with high methane production in cattle

Wallace, R. J., Rooke, J. A., McKain, N., Duthie, C.-A., Hyslop, J. J., Ross, D. W., Waterhouse, A., Watson, M., & Roehe, R. (2015) · BMC Genomics · doi:10.1186/s12864-015-2032-0 · Cited by 416 · see citing works

IMG/M: a data management and analysis system for metagenomes

Markowitz, V. M., Ivanova, N., Szeto, E., Palaniappan, K., Chu, K., Dalevi, D., Chen, I.-M. A., Grechkin, Y., Dubchak, I. L., Anderson, I., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm869 · Cited by 385 · see citing works

Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance

Roehe, R., Dewhurst, R. J., Duthie, C.-A., Rooke, J. A., McKain, N., Ross, D. W., Hyslop, J. J., Waterhouse, A., Freeman, T. C., Watson, M., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005846 · Cited by 359 · see citing works

Variational Bayesian Model Selection for Mixture Distributions

Corduneanu, A., & Bishop, C. (2001) · Cited by 345 · see citing works

MinION Analysis and Reference Consortium: Phase 1 data release and analysis

Ip, C. L. C., Loose, M., Tyson, J. R., de Cesare, M., Brown, B. L., Jain, M., Leggett, R. M., Eccles, D., Zalunin, V. V., Urban, J. M., et al. (2015) · F1000Research · doi:10.12688/f1000research.7201.1 · Cited by 317 · see citing works

Using the Metagenomics RAST Server (MG-RAST) for Analyzing Shotgun MetagenomesPreprint

Glass, E. M., Wilkening, J., Wilke, A., Antonopoulos, D. A., & Meyer, F. (2010) · Cold Spring Harbor Protocols · doi:10.1101/pdb.prot5368 · Cited by 304 · see citing works

IMG/M 4 version of the integrated metagenome comparative analysis system

Markowitz, V. M., Chen, I.-M. A., Chu, K., Szeto, E., Palaniappan, K., Pillay, M., Ratner, A., Huang, J., Pagani, I., Tringe, S. G., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt919 · Cited by 264 · see citing works

An integer linear programming approach for finding deregulated subgraphs in regulatory networks

Backes, C., Rurainski, A., Klau, G. W., Müller, O. J., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1227 · Cited by 259 · see citing works

Scaling metagenome sequence assembly with probabilistic de Bruijn graphs

Jason, P., Hintze, A., Canino-Koning, R., Howe, A., Tiedje, J. M., & Brown, C. T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121464109 · Cited by 250 · see citing works

The Binning of Metagenomic Contigs for Microbial Physiology of Mixed Cultures

Strous, M., Kraft, B., Bisdorf, R., & Tegetmeyer, H. E. (2012) · Frontiers in Microbiology · doi:10.3389/fmicb.2012.00410 · Cited by 226 · see citing works

MetAMOS: a modular and open source metagenomic assembly and analysis pipeline

Treangen, T. J., Koren, S., Sommer, D. D., Liu, B., Astrovskaya, I., Ondov, B., Darling, A. E., Phillippy, A. M., & Pop, M. (2013) · Genome biology · doi:10.1186/gb-2013-14-1-r2 · Cited by 225 · see citing works

Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering

Kelley, D. R., Liu, B., Delcher, A. L., Pop, M., & Salzberg, S. L. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1067 · Cited by 204 · see citing works

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform

Li, P., Lo, C., Anderson, J. J., Davenport, K. W., Bishop‐Lilly, K. A., Xu, Y., Ahmed, S. A., Feng, S., Mokashi, V. P., & Chain, P. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1027 · Cited by 195 · see citing works

Succinct de Bruijn Graphs

Bowe, A., Onodera, T., Sadakane, K., & Shibuya, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33122-0_18 · Cited by 171 · see citing works

Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning

Cleary, B., Brito, I., Huang, K. H., Gevers, D., Shea, T., Young, S., & Alm, E. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3329 · Cited by 151 · see citing works

EBI metagenomics—a new resource for the analysis and archiving of metagenomic data

Hunter, S., Corbett, M., Denise, H., Fraser, M., Gonzalez-Beltran, A. N., Hunter, C., JONES, P. M., Leinonen, R., McAnulla, C., Maguire, E., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt961 · Cited by 149 · see citing works

xBASE2: a comprehensive resource for comparative bacterial genomics

Chaudhuri, R. R., Loman, N. J., Snyder, L. A. S., Bailey, C. M., Stekel, D. J., & Pallen, M. J. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm928 · Cited by 135 · see citing works

Bambus 2: scaffolding metagenomes

Koren, S., Treangen, T. J., & Pop, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr520 · Cited by 135 · see citing works

Successful test launch for nanopore sequencing

Loman, N. J., & Watson, M. (2015) · Nature Methods · doi:10.1038/nmeth.3327 · Cited by 131 · see citing works

MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning

Afiahayati, Sato, K., & Sakakibara, Y. (2014) · DNA Research · doi:10.1093/dnares/dsu041 · Cited by 118 · see citing works

Clustering metagenomic sequences with interpolated Markov models

Kelley, D. R., & Salzberg, S. L. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-544 · Cited by 114 · see citing works

Unsupervised statistical clustering of environmental shotgun sequences

Kislyuk, A. O., Bhatnagar, S., Dushoff, J. G., & Weitz, J. S. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-316 · Cited by 108 · see citing works

Xander: employing a novel method for efficient gene-targeted metagenomic assembly

Wang, Q., Fish, J. A., Gilman, M., Sun, Y., Brown, C. T., Tiedje, J. M., & Cole, J. R. (2015) · Microbiome · doi:10.1186/s40168-015-0093-6 · Cited by 103 · see citing works

Using the Reactome Database

Stein, L. (2004) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0807s7 · Cited by 62 · see citing works

Using the Reactome Database

Haw, R., & Stein, L. D. (2012) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0807s38 · Cited by 46 · see citing works

Illuminating the future of DNA sequencing

Watson, M. (2014) · Genome biology · doi:10.1186/gb4165 · Cited by 43 · see citing works

Metagenomics, gene discovery and the ideal biocatalyst

Cowan, D. A., Arslanoglu, A., Burton, S. G., Baker, G. C., Cameron, R. A., Smith, J. J., & Meyer, Q. C. (2004) · Biochemical Society Transactions · doi:10.1042/bst0320298 · Cited by 41 · see citing works

Sequencing ultra-long DNA molecules with the Oxford Nanopore MinIONPreprint

Urban, J. M., Bliss, J. E., Lawrence, C. E., & Gerbi, S. A. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/019281 · Cited by 37 · see citing works

Normalized N50 assembly metric using gap-restricted co-linear chaining

Mäkinen, V., Salmela, L., & Ylinen, J. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-255 · Cited by 25 · see citing works

Meta4: a web application for sharing and annotating metagenomic gene predictions using web services

Richardson, E. J., Escalettes, F., Fotheringham, I., Wallace, R. J., & Watson, M. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00168 · Cited by 7 · see citing works

MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis

Uritskiy, G. V., DiRuggiero, J., & Taylor, J. (2018) · Microbiome · doi:10.1186/s40168-018-0541-1 · Cited by 2,642 · see citing works

Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery

Stewart, R. D., Auffret, M. D., Warr, A., Walker, A. W., Roehe, R., & Watson, M. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0202-3 · Cited by 593 · see citing works

Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

Stewart, R. D., Auffret, M. D., Warr, A., Wiser, A. H., Press, M. O., Langford, K. W., Liachko, I., Snelling, T. J., Dewhurst, R. J., Walker, A. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03317-6 · Cited by 559 · see citing works

Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future

Huws, S. A., Creevey, C. J., Oyama, L. B., Mizrahi, I., Denman, S. E., Popova, M., Muñoz-Tamayo, R., Forano, E., Waters, S. M., Hess, M., et al. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.02161 · Cited by 487 · see citing works

Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses

Pérez-Cobas, A. E., Gomez-Valero, L., & Buchrieser, C. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000409 · Cited by 314 · see citing works

Metagenomics-enabled microbial surveillance

Ko, K. K. K., Chng, K. R., & Nagarajan, N. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01089-w · Cited by 275 · see citing works

Recent insights into the tick microbiome gained through next-generation sequencing

Greay, T. L., Gofton, A. W., Paparini, A., Ryan, U. M., Oskam, C. L., & Irwin, P. (2018) · Parasites & Vectors · doi:10.1186/s13071-017-2550-5 · Cited by 220 · see citing works

Identification and evaluation of the microbiome in the female and male reproductive tracts

Koedooder, R., Mackens, S., Budding, A. E., Fares, D. A., Blockeel, C., Laven, J. S. E., & Schoenmakers, S. (2018) · Human Reproduction Update · doi:10.1093/humupd/dmy048 · Cited by 205 · see citing works

Gut Dysbiosis and Muscle Aging: Searching for Novel Targets against Sarcopenia

Picca, A., Fanelli, F., Calvani, R., Mulè, G., Pesce, V., Sisto, A., Pantanelli, C., Bernabei, R., Landi, F., & Marzetti, E. (2018) · Mediators of Inflammation · doi:10.1155/2018/7026198 · Cited by 181 · see citing works

Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions

Nam, N. N., Do, H. D. K., Trinh, K. T. L., & Lee, N. Y. (2023) · Foods · doi:10.3390/foods12112140 · Cited by 203 · see citing works

State-of-the-Art Biocatalysis

Pyser, J. B., Chakrabarty, S., Romero, E. O., & Narayan, A. R. H. (2021) · ACS Central Science · doi:10.1021/acscentsci.1c00273 · Cited by 170 · see citing works

Metagenomic Approaches for Understanding New Concepts in Microbial Science

Alves, L. D. F., Westmann, C. A., Lovate, G. L., de Siqueira, G. M. V., Borelli, T. C., & Guazzaroni, M. (2018) · International Journal of Genomics · doi:10.1155/2018/2312987 · Cited by 162 · see citing works

Rethomics: An R framework to analyse high-throughput behavioural data

Geissmann, Q., Rodríguez, L. G., Beckwith, E. J., & Gilestro, G. F. (2019) · PLoS ONE · doi:10.1371/journal.pone.0209331 · Cited by 135 · see citing works

Unifying the known and unknown microbial coding sequence space

Vanni, C., Schechter, M. S., Acinas, S. G., Barberán, A., Buttigieg, P. L., Casamayor, E. O., Delmont, T. O., Duarte, C. M., Eren, A. M., Finn, R. D., et al. (2022) · eLife · doi:10.7554/elife.67667 · Cited by 134 · see citing works

Omics-Driven Biotechnology for Industrial Applications

Amer, B., & Baidoo, E. E. K. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.613307 · Cited by 148 · see citing works

Single mosquito metatranscriptomics identifies vectors, emerging pathogens and reservoirs in one assay

Batson, J., Dudas, G., Haas-Stapleton, E. J., Kistler, A. L., Li, L. M., Logan, P., Ratnasiri, K., & Retallack, H. (2021) · eLife · doi:10.7554/elife.68353 · Cited by 139 · see citing works

Soil plastisphere: Exploration methods, influencing factors, and ecological insights

Wang, C., Wang, L., Ok, Y. S., Tsang, D. C. W., & Hou, D. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2022.128503 · Cited by 134 · see citing works

Testing the advantages and disadvantages of short- and long- read eukaryotic metagenomics using simulated reads

Pearman, W. S., Freed, N. E., & Silander, O. K. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3528-4 · Cited by 114 · see citing works

Metagenomic applications in exploration and development of novel enzymes from nature: a review

Prayogo, F. A., Budiharjo, A., Kusumaningrum, H. P., Wijanarka, W., Suprihadi, A., & Nurhayati, N. (2020) · Journal of Genetic Engineering and Biotechnology · doi:10.1186/s43141-020-00043-9 · Cited by 114 · see citing works

The future of NGS (Next Generation Sequencing) analysis in testing food authenticity

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Unraveling uncultivable pesticide degraders via stable isotope probing (SIP)

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Helminth Microbiomes – A Hidden Treasure Trove?

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Bioinformatics for Marine Products: An Overview of Resources, Bottlenecks, and Perspectives

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Evidence for the Widespread Occurrence of Bacteria Implicated in Acute Oak Decline from Incidental Genetic Sampling

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Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions

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Metagenomic Studies in Inflammatory Skin Diseases

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Highly Robust de Novo Full-Length Protein Sequencing

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Metagenomic Applications to Herbivore Gut Microbiomes: A Comprehensive Review of Microbial Diversity and Host Interactions

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Cold-Active Lipases and Esterases: A Review on Recombinant Overexpression and Other Essential Issues

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Can the microbiota predict response to systemic cancer therapy, surgical outcomes, and survival? The answer is in the gut

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Metaproteomic Discovery and Characterization of a Novel Lipolytic Enzyme From an Indian Hot Spring

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Archaea, the tree of life, and cellular evolution in eukaryotes

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The researcher's guide to selecting biomarkers in mental health studies

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Exploring the gut-brain-microbiome axis in Alzheimer's disease: Integrating metagenomics, metabolomics, and artificial intelligence for next-generation biomarker discovery

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Carbon Dioxide Sequestration by Microbial Carbonic Anhydrases From Submarine Hydrothermal Systems

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Metagenomic next-generation sequencing of radial ultrasound bronchoscopy-guided “cocktail” specimens as an efficient method for the diagnosis of focal pulmonary infections: a randomised study

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GAAP: A GUI-based Genome Assembly and Annotation Package

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Decision Theory-Based COI-SNP Tagging Approach for 126 Scombriformes Species Tagging

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PROSPECTS FOR DIATOMS IDENTIFICATION USING METAGENOMICS: A REVIEW

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Role of Genomics in Smart Era and Its Application in COVID‐19

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Metagenomics: a powerful lens viewing the microbial world

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Oral Microbiome in Oral Cancer Research from Sampling to Analysis: Strategies, Challenges, and Recommendations

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Role of Plant GrowthPromoting Microbes in Plant Growth and Development

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High-Throughput Sequencing Technologies in Metagenomics: Advanced Approaches for Algal Research

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Sequencing and Annotated of Goat's Milk using Bioinformatics Tool: ClustalX

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Advances in Metagenomics: Bioinformatics Tools and Techniques

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