Bhartiya Krishi Anusandhan Patrika, volume 38 issue 3 (september 2023) : 210-217

Metagenomics and Its Tools and Softwares

Jyotika Bhati1,*, Ratna Prabha2, Dwijesh Chandra Mishra1
1Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012, India.
2Agricultural Knowledge Management Unit, ICAR-Indian Agricultural Research Institute, New Delhi-110 012, India.
  • Submitted22-03-2023|

  • Accepted21-08-2023|

  • First Online 29-09-2023|

  • doi 10.18805/BKAP637

Cite article:- Bhati Jyotika, Prabha Ratna, Mishra Chandra Dwijesh (2023). Metagenomics and Its Tools and Softwares . Bhartiya Krishi Anusandhan Patrika. 38(3): 210-217. doi: 10.18805/BKAP637.

Background: Throughout all of nature, microorganisms can be found thriving in a variety of environmental circumstances. Most marine life is either unculturable or challenging to culture using conventional techniques.

Methods: Through the analysis of genomic data obtained directly from an environmental sample, metagenomics enables the study of all microorganisms, regardless of whether they can be cultured or not. This allows for the identification of the species present as well as the extraction of knowledge about the functionality of microbial communities in their natural habitat. 

Result: This article summarizes the state-of-the-art metagenomic approaches used and their associated softwares and tools. In order to comprehend and utilise unculturable microorganisms, metagenomics provides access to the enormous diversity of the microbial world and has significantly advanced both academic communities and industrial settings.


  1. Afiahayati, Sato, K., Sakakibara, Y. (2014). MetaVelvet-SL: An extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA Research. 22(1): 69-77.

  2. Ahmed, V., Verma, M.K., Gupta, S., Mandhan, V. and Chauhan, N.S. (2018). Metagenomic profiling of soil microbes to mine salt stress tolerance genes. Frontiers in Microbiology. 9: 159. https://doi.org/10.3389/fmicb. 2018.00159.

  3. Alla, M., Vladislav, S., Alexey, G. (2016). MetaQUAST: Evaluation of metagenome assemblies. Bioinformatics. 32(7): 1088-1090.

  4. Alneberg, J., Bjarnason, B.S., De Bruijn, I., Schirmer, M., Quick, J., et al. (2014). Binning metagenomic contigs by coverage and composition. Nature Methods. 11: 1144-1146.

  5. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J. (1990). Basic local alignment search tool. Journal of Molecular Biology. 215(3): 403-410.

  6. Alves, L.D.F., Westmann, C.A., Lovate, G.L., Marcelino, G., De Siqueira, V., et al. (2018). Metagenomic Approaches for understanding new concepts in microbial science. International Journal of Genomics. 2018: 1-15.

  7. Bekele, W., Zegeye, A., Simachew, A., Assefa, G. (2021). Functional metagenomics from the Rumen environment- A review. Advances in Biosciences and Biotechnology. 12: 125-141.

  8. Caspi, R., Billington, R., Keseler, I.M., Kothari, A., Krummenacker, M., et al. (2020). The MetaCyc database of metabolic pathways and enzymes - a 2019 update. Nucleic Acids Research. 48(D1): D445-D453.

  9. Dinghua, L., Chi-Man, L., Ruibang, L., Kunihiko, S., Tak-Wah, L. (2015). MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 31(10): 1674-1676.

  10. Duarte, A.S.R., Stärk, K.D., Munk, P., Leekitcharoenphon, P., Bossers, A. et al. (2020). Addressing learning needs on the use of metagenomics in antimicrobial resistance surveillance. Frontiers in Public Health. 8: 38.

  11. Felsenstein, J. (1989). PHYLIP - Phylogeny Inference Package (Version 3.2). Cladistics. 5: 164-166.

  12. Ginolhac, A., Jarrin, C., Gillet, B., Robe, P., Pujic, P., et al. (2004). Phylogenetic analysis of polyketide synthase I domains from soil metagenomic libraries allows selection of promising clones. Applied Environmental Microbiology. 70: 5522-5527.

  13. Handelsman, J., Rondon, M.R., Brady, S.F., Clardy, J., Goodman, R.M. (1998). Molecular biological access to the chemistry of unknown soil microbes: A new frontier for natural products. Chemical Biology. 5(10): R245-R249.

  14. Healy, F.G., Ray, R.M., Aldrich, H.C., Wilkie, A.C., Ingram, L.O. et al. (1995). Direct isolation of functional genes encoding cellulases from the microbial consortia in a thermophilic, anaerobic digester maintained on lignocellulose. Applied Microbiology and Biotechnology. 43: 667-674.

  15. Hideki, N., Takeaki, T., Takehiko, I. (2008). Meta gene annotator: Detecting species-specific patterns of ribosomal binding site for precise gene prediction in anonymous prokaryotic and phage genomes. DNA Research. 15(6): 387-396.

  16. Hoff, K.J., Lingner, T., Meinicke, P., Tech, M. (2009). Orphelia: Predicting genes in metagenomic sequencing reads. Nucleic Acids Research. 37(Web Server issue): W101-W105.

  17. Hunter, S., Corbett, M., Denise, H., Fraser, M., Gonzalez-Beltran, A., et al. (2014). EBI metagenomics - A new resource for the analysis and archiving of metagenomic data. Nucleic Acids Research. 42(Database issue): D600-D606.

  18. Huson, D.H. and Weber, N. (2013). Microbial community analysis using MEGAN. Methods Enzymology. 531: 465-485.

  19. Jimenez, D.J. andreote, F.D., Chaves, D., Montan, J.S., Osorio- Forero, C., et al. (2012). Structural and functional insights from the metagenome of an acidic hot spring microbial planktonic community in the Colombian Andes. PLoS One. 7: 1-15.

  20. Kang, D.D., Froula, J., Egan, R., Wang, Z. (2015). MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. Peer Journal. 3: e1165.

  21. Kasper, C., Ribeiro, D., Almeida, A.M.D., Larzul, C., Liaubet, L., et al. (2020). Omics application in animal science- A special emphasis on stress response and damaging behaviour in pigs. Genes. 11: 920.

  22. Keegan, K.P., Glass, E.M., Meyer, F. (2016). MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function. Methods Molecular Biology. 1399: 207-233.

  23. Khandelwal, I., Sharma, A., Agrawal, P.K., Shrivastava, R. (2017). Bioinformatics Database Resources. In: Library and Information Services for Bioinformatics Education and Research. IGI Global, Hershey. pp 45-90.

  24. Maria-Eugenia, G., Ana, B., Peter, N.G., Manuel, F. (2009). Metagenomics as a new technological tool to gain scientific knowledge. World Journal of Microbiology and Biotechnology. 25: 945-954.

  25. Markowitz, V.M., Chen, I.M., Chu, K., Szeto, E., Palaniappan, K., et al. (2014). IMG/M 4 version of the integrated metagenome comparative analysis system. Nucleic Acids Research. 42(Database issue): D568-D573.

  26. Mehmood, M.A., Sehar, U., Ahmad, N. (2014). Use of bioinformatics tools in different spheres of life sciences. Data Mining, Genomices and Proteomics. 5: 1-13.

  27. Namiki, T., Hachiya, T., Tanaka, H., Sakakibara, Y. (2012). Meta Velvet: An extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Research. 40(20): e155.

  28. Nazir, A. (2016). Review on metagenomics and its applications. Imperial Journal of Interdisciplinary Research. 2(3): 277-286.

  29. Neelakanta, G. and Sultana, H. (2013). The use of metagenomic approaches to analyze changes in microbial communities. Microbiol. Insights. 6: 37-48.

  30. Niu, S., Yang, J., Mcdermaid, A., Zhao, J., Kang, Y. (2018). Bioinformatics tools for quantitative and functional metagenome and metatranscriptome data analysis in microbes. Briefings in Bioinformatics. 19: 1415- 1429.

  31. Nnadozie, C.F. and Odume, O.N. (2019). Freshwater environments as reservoirs of antibiotic resistant bacteria and their role in the dissemination of antibiotic resistance genes. Environmental Pollution. 254: 113067.

  32. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., et al. (1999). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research. 27(1): 29-34.

  33. Pace, N.R., Stahl, D.A., Lane, D.J., Olsen, G.J. (1986). Analyzing natural microbial populations by Ribosomal RNA sequences. Advances in Microbial Ecology. 9: 1-5.

  34. Peng, Y., Leung, H.C., Yiu, S.M., Chin, F.Y. (2012). IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 28(11): 1420-1428.

  35. Prayogo, F.A., Budiharjo, A., Kusumaningrum, H.P., Wijanarka, W., Suprihadi, A., et al. (2020). Metagenomic applications in exploration and development of novel enzymes from nature: A review. Journal of Genetic Engineering and Biotechnology. 18: 39.

  36. Pruesse, E., Quast, C., Knittel, K., Fuchs, B.M., Ludwig, W., et al. (2007). SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Research. 35(21): 7188-7196.

  37. Quevillon, E., Silventoinen, V., Pillai, S., Harte, N., Mulder, N., et al. (2005). InterProScan: Protein domains identifier. Nucleic Acids Research. 33(suppl_2): W116-W120.

  38. Raza, K. (2012). Application of data mining in bioinformatics. Indian Journal of Computer Science and Engineering. 1: 114-118.

  39. Rho, M., Tang, H., Ye, Y. (2010). FragGeneScan: Predicting genes in short and error-prone reads. Nucleic Acids Research. 38(20): e191.

  40. Riesenfeld, C.S., Goodman, R.M., Handelsman, J. (2004). Uncultured soil bacteria are a reservoir of new antibiotic resistance genes. Environmental Microbiology. 6: 981-989.

  41. Roumpeka, D.D., Wallace, R.J., Escalettes, F., Fotheringham, I., Watson, M. (2017). A review of bioinformatics tools for bio-prospecting from metagenomic sequence data. Frontiers in Genetics. 8: 1-10.

  42. Schmidt, T.M., DeLong, E.F., Pace, N.R. (1991). Analysis of a marine picoplankton community by 16S rRNA gene cloning and sequencing. Journal of Bacteriology. 173(14): 4371-4378.

  43. Strous, M., Kraft, B., Bisdorf, R., Tegetmeyer, H.E. (2012). The binning of metagenomic contigs for microbial physiology of mixed cultures. Frontiers in Microbiology. 3: 1-11.

  44. Suttner, B., Johnston, E.R., Orellana, L.H., Rodriguez-R, L.M., Hatt, J.K. et al. (2020). Metagenomics as a public health risk assessment tool in a study of natural creek sediments influenced by agricultural and livestock runoff: Potential and limitations. Applied Environmental Microbiology. 86(6): e02525-19.

  45. Tamames, J. and Puente-Sánchez, F. (2019). SqueezeMeta, A Highly Portable, Fully Automatic Metagenomic Analysis Pipeline. Frontiers in Microbiology. 9: 3349.

  46. Tamura, K., Peterson, D., Nicholas, P., Glen, S., Masatoshi, N., et al. (2011). MEGA5: Molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance and maximum parsimony methods. Molecular Biology and Evolution. 28(10): 2731-2739.

  47. Torsten, S. (2014). Prokka: Rapid prokaryotic genome annotation. Bioinformatics. 30(14): 2068-2069.

  48. Treangen, T.J., Koren, S., Sommer, D.D., Liu, B., Astrovskaya, I. et al. (2013). MetAMOS: A modular and open source metagenomic assembly and analysis pipeline. Genome Biology. 14(1): R2.

  49. Uchiyama, T., Abe, T., Ikemura, T., Watanabe, K. (2005). Substrate-induced gene-expression screening of environmental metagenome libraries for isolation of catabolic genes. Nature Biotechnology. 23: 88-93.

  50. Wu, Y.W., Tang, Y.H., Tringe, S.G., Simmons, B.A., Singer, S.W. (2014). MaxBin: An automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2: 26.

  51. Yasir, B., Salam, P.S., Bolin, K.K. (2014). Metagenomics: An Application Based Perspective. Chinese Journal of Biology. 2014: 146030.

  52. Zhang, L., Chen, F., Zeng, Z., Xu, M., Sun, F., et al. (2021). Advances in metagenomics and its application in environmental microorganisms. Frontiers in Microbiology. 12: 766364.

Editorial Board

View all (0)