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A Review of Bioinformatics Tools for Bio-Prospecting from Metagenomic Sequence Data
Open accessAbstract
The microbiome can be defined as the community of microorganisms that live in a particular environment. Metagenomics is the practice of sequencing DNA from the genomes of all organisms present in a particular sample, and has become a common method for the study of microbiome population structure and function. Increasingly, researchers are finding novel genes encoded within metagenomes, many of which may be of interest to the biotechnology and pharmaceutical industries. However, such "bioprospecting" requires a suite of sophisticated bioinformatics tools to make sense of the data. This review summarizes the most commonly used bioinformatics tools for the assembly and annotation of metagenomic sequence data with the aim of discovering novel genes.
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“The rumen also remains a source of valuable bioactives for the biotechnology industry, and metagenomics is a key tool for such bioprospecting (Oyama et al., 2017; Roumpeka et al., 2017).”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Roumpeka et al., 2017
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Prokka: rapid prokaryotic genome annotation
Seemann, T. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu153 · Cited by 18,937 · cited 1×
“Finally,Prokka (Seemann, 2014) is a pipeline for annotating bacterial genomes and has an option for highly fragmented metagenomic assemblies.”
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 11,893 · cited 4×
“After removing repetitive sequences and low-quality regions, MG-RAST maps sequence data to three non-redundant databases and creates a phylogenetic profile of the metagenomic sample.”
MethodInfluentialLi, D., Liu, C.-M., Luo, R., Sadakane, K., & Lam, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv033 · Cited by 10,133 · cited 1×
“Megahit (Li et al., 2015) uses a very similar approach to IDBA-UD, but takes advantage of succinct de Bruijn graphs (Bowe et al., 2012) and GPUs, which lowers the memory requirements and increases speed, respectively.”
MethodVelvet: 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 9,587 · cited 9×
“MetaVelvet authors reported longer N50 sizes, higher cover rates of genomes (compared to other metagenome and single genome assemblers) and high numbers of predicted proteins (by MetaGene gene finding software, Noguchi et al., 2006).”
MethodInfluentialSpace/time trade-offs in hash coding with allowable errors
Bloom, B. (1970) · CACM · doi:10.1145/362686.362692 · Cited by 8,424 · cited 1×
“Bloom filters are probabilistic data structures which test the membership of an element in a dataset, allowing false positives but no false negatives (Bloom, 1970).”
BackgroundKEGG 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 · cited 1×
“Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database connecting genomic, biochemical and phenotypical information from multiple individual databases (Kanehisa et al., 2008).”
BackgroundThe 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 5,605
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 · cited 1×
“The assembled data are validated by aligning them against reference genomes [MUMmer software (Kurtz et al., 2004)].”
MethodKEGG 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 · cited 1×
“In Reactome also references other databases such as UniProt, Ensembl, KEGG and many others (Consortium, 2012; Flicek et al., 2012; Kanehisa et al., 2012; Brown et al., 2015).”
BackgroundEnvironmental 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 · cited 1×
“Venter et al. (2004) discovered over 1.2 million unknown genes using metagenomic sequencing of the Sargasso Sea.”
BackgroundInfluentialComing 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 · cited 1×
“(10–15 kb with some reads > 50,000 bp; Goodwin et al., 2016).”
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 · cited 1×
“GenScan (Burge and Karlin, 1997) is another popular method used for single eukaryotic genomes, and contains models for exons, introns and intergenic sequences.”
MethodNonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data
Chin, C.-S., Alexander, D. H., Marks, P., Klammer, A. A., Drake, J., Heiner, C., Clum, A., Copeland, A., Huddleston, J., Eichler, E. E., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2474 · Cited by 4,029 · cited 1×
“%) but this can be corrected to very high accuracy (Koren et al., 2012; Chin et al., 2013).”
BackgroundCaspi, 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
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 3,157 · cited 5×
“MetaWatt is an open source algorithm that can be implemented in any platform which supports BLAST and Glimmer.”
MethodInfluentialPeng, Y., Leung, H. C. M., Yiu, S., & Chin, F. Y. L. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts174 · Cited by 2,998 · cited 5×
“In IDBA-UD (Peng et al., 2012), contigs are constructed through progressive cycles of assembly using gradually increasing k -mer values.”
MethodInfluentialStructure 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 2,978 · cited 1×
“Similarly, the global ocean sampling survey (Sunagawa et al., 2015) described 40 million non-redundant sequences from over 35000 species, only 0.44% of which overlapped with known reference genomes, highlighting the huge “unexplored genomic potential in our oceans.””
BackgroundInfluentialBinning 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 2,815 · cited 5×
“Thus, CONCOCT has been suggested for extracting biologically important information and could possibly contribute to recovery after infection.”
BackgroundMethodInfluentialA 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 · cited 1×
“InterProScan uses models of proteins and domains from the InterPro database, and the Phobius analysis algorithm (Krogh et al., 2004) is available as an additional feature.”
MethodHandelsman, 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 · cited 1×
“Handelsman et al. (1998) first coined the term as the functional analysis of a collection of microbial DNA extracted from soil samples.”
BackgroundAb initio gene identification in metagenomic sequences
Zhu, W., Lomsadze, A., & Borodovsky, M. Y. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq275 · Cited by 1,818 · cited 1×
“GeneMark is an abinitio gene prediction software suite that has modes for both metagenomes (MetaGeneMark, Zhu et al., 2010) and Eukaryotes (GeneMark-ES, Ter-hovhannisyan et al., 2008), though we are unaware of studies that have combined these two.”
MethodTen 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 1,755 · cited 2×
“The Illumina output is compatible with the most applications for further study (van Dijk et al., 2014).”
MethodAn 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 · cited 1×
“Many more recent de novo assemblers use the de Bruijn graph approach (Pevzner et al., 2001) which constructs a graph by reading the consecutive kmers (sequences of k bases long) within each read.”
BackgroundMetagenomic 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 · cited 2×
“Many microbiomes are incredibly complex – for example, Hess et al. (2011) estimated that a single cow rumen contains approximately 1000 OTUs – and therefore any sequencing technology applied to microbiome samples needs to be sufficiently deep and comprehensive to capture representative sequences from…”
BackgroundInfluentialThe 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
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 · cited 1×
“In Reactome also references other databases such as UniProt, Ensembl, KEGG and many others (Consortium, 2012; Flicek et al., 2012; Kanehisa et al., 2012; Brown et al., 2015).”
BackgroundMetaGene: 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 1,156
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
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 · cited 7×
“InterProScan uses models of proteins and domains from the InterPro database, and the Phobius analysis algorithm (Krogh et al., 2004) is available as an additional feature.”
BackgroundMethodInfluentialGene prediction in novel fungal genomes using an ab initio algorithm with unsupervised trainingPreprint
Ter-Hovhannisyan, V., Lomsadze, A., Chernoff, Y. O., & Borodovsky, M. Y. (2008) · Genome Research · doi:10.1101/gr.081612.108 · Cited by 1,029
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 1,025 · cited 1×
“%) but this can be corrected to very high accuracy (Koren et al., 2012; Chin et al., 2013).”
BackgroundFragGeneScan: 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 865 · cited 1×
“FragGeneScan (Rho et al., 2010) is designed to predict genes (often fragmented) directly from short reads themselves, without the need of assembly; however, the software can also run on assembled sequenced.”
BackgroundFlicek, 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 842 · cited 1×
“In Reactome also references other databases such as UniProt, Ensembl, KEGG and many others (Consortium, 2012; Flicek et al., 2012; Kanehisa et al., 2012; Brown et al., 2015).”
BackgroundNext 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 812
MetaQUAST: evaluation of metagenome assemblies
Mikheenko, A., Saveliev, V., & Gurevich, A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv697 · Cited by 711 · cited 1×
“MetaQUAST (Mikheenko et al., 2016) is a tool specifically designed for the quality assessment of metagenomics assemblies.”
MethodGene: 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 · cited 1×
“In Reactome also references other databases such as UniProt, Ensembl, KEGG and many others (Consortium, 2012; Flicek et al., 2012; Kanehisa et al., 2012; Brown et al., 2015).”
BackgroundWikiPathways: 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 · cited 1×
“It is part of the MediaWiki software and relies on creation, curation and editing of various biochemical pathways by any user with a WikiPathways account (Pico et al., 2008).”
MethodNamiki, T., Hachiya, T., Tanaka, H. L., & Sakakibara, Y. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks678 · Cited by 669
Noguchi, H., Taniguchi, T., & Itoh, T. (2008) · DNA Research · doi:10.1093/dnares/dsn027 · Cited by 655 · cited 3×
“Metagenomic assembly with IDBA-UD, in real and simulated data, showed also N50 values, high contig length and large number of predicted genes (by MetaGeneAnnotator, Noguchi et al., 2008).”
BackgroundMethodImproved 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 · cited 1×
“At launch, the MinION read length was approximately 6–8 kb (Jain et al., 2015; Loman and Watson, 2015); however, Urban et al. (2015) published a lab protocol which could improve the MinION reads length producing many reads even longer than 100 kb.”
MethodRay 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 590
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 · cited 4×
“Phymm uses IMMs trained on known genomes to classify the data; whereas SCIMM is a totally un-supervised tool, also based on IMMs.”
MethodInfluentialWikiPathways: 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
Nagarajan, N., & Pop, M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3367 · Cited by 474 · cited 1×
“Assembly of a single genome is a relatively complex procedure as repetitive elements, within genomes, make the assignment of reads to chromosomes non-trivial [reviewed in Nagarajan and Pop (2013)].”
BackgroundThe 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 · cited 2×
“Wallace et al. (2015) also sequenced ruminal digesta samples using Illumina sequencing, assembling the data de novo .”
BackgroundMethodSelf-citationIMG/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 392 · cited 1×
“A binning step aims to categorize metagenomic data into phylogenies while characteristics such as phenotype, habitat, living conditions and diseases can also be attributed to the data (Markowitz et al., 2008).”
BackgroundRoehe, 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 · cited 2×
“There are many links to external databases such as NCBI Entrez Gene, OMIM and UniProt .”
InfluentialVariational Bayesian Model Selection for Mixture Distributions
Corduneanu, A., & Bishop, C. (2001) · Cited by 345
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 312 · cited 1×
“Like PacBio, ONT technologies also have high systematic error rates (Ip et al., 2015).”
BackgroundUsing 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
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 266 · cited 1×
“A binning step aims to categorize metagenomic data into phylogenies while characteristics such as phenotype, habitat, living conditions and diseases can also be attributed to the data (Markowitz et al., 2008).”
BackgroundAn 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 263
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 · cited 1×
“Based on de Bruijn graphs assembly, Pell et al. (2012) described a probabilistic method for storing de Bruijn assembling graphs using less memory.”
MethodMetAMOS: 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 232 · cited 1×
“MetAMOS (Treangen et al., 2013) is an example of a modular framework which combines existing tools into a metagenomic analysis pipeline.”
MethodThe 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 229 · cited 4×
“In MetaWatt (Strous et al., 2012), four steps are carried out.”
BackgroundMethodInfluentialGene 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 207 · cited 1×
“Glimmer-MG (Kelley et al., 2012) is an extension of the popular bacterial gene-prediction software Glimmer (Delcher et al., 2007).”
MethodLi, 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 · cited 1×
“The EDGE platform (Li et al., 2017) also contains a number of relevant software tools including QC, assembly, annotation, taxonomic classification and phylogenetic analysis.”
MethodBowe, 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 · cited 1×
“Megahit (Li et al., 2015) uses a very similar approach to IDBA-UD, but takes advantage of succinct de Bruijn graphs (Bowe et al., 2012) and GPUs, which lowers the memory requirements and increases speed, respectively.”
MethodDetection 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 150
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 · cited 1×
“EBI MetaGenomics (Hunter et al., 2014) is a system dedicated to metagenomic analysis based at the EBI.”
BackgroundBambus 2: scaffolding metagenomes
Koren, S., Treangen, T. J., & Pop, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr520 · Cited by 140 · cited 1×
“Secondly, scaffolds are created using paired-end and mate-pair data using Bambus 2 (Koren et al., 2011).”
MethodxBASE2: 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 · cited 1×
“Testing Prokka against RAST (Aziz et al., 2008) and xBase2 (Chaudhuri et al., 2008) in annotating E. coli data, Prokka showed overall the best performance.”
MethodSuccessful test launch for nanopore sequencing
Loman, N. J., & Watson, M. (2015) · Nature Methods · doi:10.1038/nmeth.3327 · Cited by 131 · cited 1×
“At launch, the MinION read length was approximately 6–8 kb (Jain et al., 2015; Loman and Watson, 2015); however, Urban et al. (2015) published a lab protocol which could improve the MinION reads length producing many reads even longer than 100 kb.”
MethodAfiahayati, Sato, K., & Sakakibara, Y. (2014) · DNA Research · doi:10.1093/dnares/dsu041 · Cited by 118 · cited 1×
“An extension of MetaVelvet in assembling metagenomics data is MetaVelvet-SL (Sato and Sakakibara, 2015) which focuses on identifying and classifying chimeric nodes in the assembly network.”
MethodClustering metagenomic sequences with interpolated Markov models
Kelley, D. R., & Salzberg, S. L. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-544 · Cited by 116 · cited 4×
“PHYSCIMM can thoroughly describe the microbial composition of a sample when the species are represented in public databases.”
MethodInfluentialUnsupervised 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 109 · cited 1×
“The method is reported to perform well in low complexity metagenomic communities (Kislyuk et al., 2009).”
MethodXander: 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
Stein, L. (2004) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0807s7 · Cited by 62 · cited 1×
“The reactions are organized hierarchically, with single reactions in the lowest level, while interconnected pathways are organized in higher levels (Haw and Stein, 2012).”
BackgroundHaw, R., & Stein, L. D. (2012) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0807s38 · Cited by 46
Illuminating the future of DNA sequencing
Watson, M. (2014) · Genome biology · doi:10.1186/gb4165 · Cited by 43 · cited 1×
“Advances in metagenomics have themselves been driven by advances in second-and third-generation sequencing technologies, which are now capable of producing hundreds of gigabases of DNA sequenced data at a very low cost (Watson, 2014).”
BackgroundMetagenomics, 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 · cited 1×
“The microbiome can potentially provide a wide range of novel enzymes and biocatalysts with major applications in the marketplace, for example the biotechnology, biofuels and pharmaceutical industry (Cowan et al., 2004).”
BackgroundSequencing 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 · cited 1×
“At launch, the MinION read length was approximately 6–8 kb (Jain et al., 2015; Loman and Watson, 2015); however, Urban et al. (2015) published a lab protocol which could improve the MinION reads length producing many reads even longer than 100 kb.”
MethodNormalized 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
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 · cited 1×
“Meta4 (Richardson et al., 2013) is a simple web application that allows users to query, search and browse the millions of gene and protein predictions that often result from metagenomic assembly and annotation.”
Background
Cited by
Sorted by citations
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
“Software for WMG data analysis have grown in number and complexity, improving our ability to process, analyze, and interpret such data [8 – 12].”
MethodStewart, 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 611
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
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
“The rumen also remains a source of valuable bioactives for the biotechnology industry, and metagenomics is a key tool for such bioprospecting (Oyama et al., 2017; Roumpeka et al., 2017).”
BackgroundSelf-citationPérez-Cobas, A. E., Gomez-Valero, L., & Buchrieser, C. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000409 · Cited by 314
“Metagenomics refers to the application of sequencing techniques to analyse the totality of the genomic material present in a sample [1].”
Background
Metadata
- Authors
- Roumpeka, D. D., Wallace, R. J., Escalettes, F., Fotheringham, I., & Watson, M.
- Affiliations
- Roslin Institute (GB)
- University of Aberdeen (GB)
- Ingenza (United Kingdom) (GB)
- Corresponding author
- Roumpeka, Despoina D.
- ORCID
- Watson, Mick
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2017-03-06
- Volume
- 8
- Pages
- 23-23
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC5337752
- OpenAlex
- W2595593998
- Cited by
- 199
- References
- 74
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 23 · 2023: 14 · 2022: 24 · 2021: 34 · 2020: 19 · 2019: 25 · 2018: 22 · 2017: 5
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- Biotechnology and Biological Sciences Research Council (BBS/E/D/20310000, BB/J004243/1, BB/L016389/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Microbial Natural Products and Biosynthesis
- Keywords
- metagenomics, bioinformatics software, bioprospecting, microbiome, metagenome assembly, metagenome annotation, gene discovery, microbial community structure
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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