The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
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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Cites
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.”
BackgroundInfluential
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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
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