Metagenomic sequencing has revolutionized microbial ecology by enabling culture-independent analysis of complex microbial communities. Recent advancements in sequencing technologies, computational tools, and functional annotation pipelines have dramatically improved resolution, accuracy, and scalability. This review explores the evolution of metagenomic platforms (e.g., short-read, long-read, and single-cell sequencing), their applications in diverse ecosystems (human gut, environmental, industrial), and persistent challenges such as data integration and bias correction. We highlight emerging trends, including portable sequencing devices and CRISPR-based enrichment, that promise to democratize metagenomic research further.
RinkeCSchwientekPSczyrbaA, et al.Insights into the phylogeny and coding potential of microbial dark matter. Nature2013; 499(7459): 431–437. https://doi.org/10.1038/nature12352
2.
SangerFNicklenSCoulsonAR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci1977; 74(12): 5463–5467. https://doi.org/10.1073/pnas.74.12.5463
3.
VenterJCRemingtonKHeidelbergJF, et al.Environmental genome shotgun sequencing of the Sargasso Sea. Science2004; 304(5667): 66–74. https://doi.org/10.1126/science.1093857
QinJLiRRaesJ, et al.A human gut microbial gene catalogue established by metagenomic sequencing. Nature2010; 464(7285): 59–65. https://doi.org/10.1038/nature08821
7.
NurkSMeleshkoDKorobeynikovA, et al.metaSPAdes: a new versatile metagenomic assembler. Genome Res2017; 27(5): 824–834. https://doi.org/10.1101/gr.213959.116
8.
GloorGBMacklaimJMPawlowsky-GlahnV, et al.Microbiome datasets are compositional: and this is not optional. Front Microbiol2017; 8: 2224. https://doi.org/10.3389/fmicb.2017.02224
9.
ParksDHRinkeCChuvochinaM, et al.Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol2018; 3(11): 1533–1542. https://doi.org/10.1038/s41564-017-0012-7
10.
TruongDTTettAPasolliE, et al.Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res2017; 27(4): 626–638. https://doi.org/10.1101/gr.216242.116
11.
CallahanBJWongJHeinerC, et al.High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res2019; 47(18): e103. https://doi.org/10.1093/nar/gkz569
12.
FranceMTMaBGajerP, et al.VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition. Microbiome2022; 10(1): 1–15. https://doi.org/10.1186/s40168-022-01286-z
13.
BickhartDMKolmogorovMTsengE, et al.Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities. Nat Biotechnol2022; 40(2): 227–235. https://doi.org/10.1038/s41587-021-01130-z
14.
WoodcroftBJSingletonCMBoydJA, et al.Genome-centric view of carbon processing in thawing permafrost. Nature2018; 560(7717): 49–54. https://doi.org/10.1038/s41586-018-0338-1
15.
QuickJLomanNJDuraffourS, et al.Real-time, portable genome sequencing for ebola surveillance. Nature2016; 530(7589): 228–232. https://doi.org/10.1038/nature16996
16.
KovakaSFanYNiB, et al.Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED. Nat Biotechnol2021; 39(4): 431–441. https://doi.org/10.1038/s41587-020-0731-9
17.
MartinSHeavensDLanY, et al.Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples. Genome Biol2022; 23(1): 1–14. https://doi.org/10.1186/s13059-022-02710-1
18.
CrossKLCampbellJHBalachandranM, et al.Targeted isolation and cultivation of uncultivated bacteria by reverse genomics. Nat Biotechnol2019; 37(11): 1314–1321. https://doi.org/10.1038/s41587-019-0260-6
LanFDemareeBAhmedN, et al.Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding. Nat Biotechnol2023; 41(2): 221–230. https://doi.org/10.1038/s41587-022-01585-8
21.
DankoDBezdanDAfshinEE, et al.A global metagenomic map of urban microbiomes and antimicrobial resistance. Cell2021; 184(13): 3376–3393. https://doi.org/10.1016/j.cell.2021.05.002
22.
WangYZhaoYBollasA, et al.Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol2023; 41(4): 512–520. https://doi.org/10.1038/s41587-022-01585-8
23.
ChengLZhouXWangY, et al.CRISPR-Cas9 enrichment of Akkermansia muciniphila genomes reveals strain-specific mucinase variants linked to colorectal cancer. Cell Host Microbe2022; 30(4): 567–579. https://doi.org/10.1016/j.chom.2022.03.002
24.
StämmlerFGläsnerJHiergeistA, et al.CRISPR-Assisted targeting of microbiome members with single-cell precision. Nat Commun2023; 14(1): 4633. https://doi.org/10.1038/s41467-023-40343-5
25.
RoumpekaDDWallaceRJEscalettesF,et al.A review of bioinformatics tools for bio-prospecting from metagenomic sequence data. Frontiers in Genetics. Epub ahead of print, 2017. https://doi.org/10.3389/fgene.2017.00023
26.
ArthurJCGharaibehRZPerez-ChanonaE, et al.Genotoxin-producing Escherichia coli drives colorectal carcinogenesis in murine models. Nat Microbiol2020; 5(9): 1123–1133. https://doi.org/10.1038/s41564-020-0736-7
27.
NakamuraKOkaSFujimotoA, et al.crAssphage-induced bile acid dysregulation exacerbates metabolic syndrome via hepatic FXR signaling. Cell Host Microbe2022; 31(4): 601–615. https://doi.org/10.1016/j.chom.2022.03.014
CaseRJBoucherYDahlloI, et al.Use of 16S rRNA and rpoB genes as molecular markers for microbial ecology studies. Appl Environ Microbiol2007; 73: 278–288. https://doi.org/10.1128/AEM.01177-06
30.
SchochCLSeifertKAHuhndorfS, et al.Nuclear ribosomal internal transcribed spacer (its) region as a universal DNA barcode marker for fungi. Proc Natl Acad Sci2012; 109: 6241–6246. https://doi.org/10.1073/pnas.1117018109
31.
BeheraBKPatraBChakrabortyHJ, et al.Metagenomic analysis reveals bacterial and fungal diversity in sediments of river Ganga and Yamuna, India. Environ Sci Pollut Res Int2020; 27(31): 39281–39292.
32.
BeheraBKPatraBDixitS, et al.Bacteriophage diversity in India’s major river Ganga: a repository to regulate pathogenic bacteria in the aquatic environment. Environ Sci Pollut Res Int2023; 30(12): 34101–34114.
33.
ParidaPKBeheraBKDehuryB, et al.Community structure and function of microbiomes in polluted stretches of river Yamuna in New Delhi, India, using shotgun metagenomics. Environ Sci Pollut Res Int2022; 29(47): 71311–71325. https://doi.org/10.1007/s11356-022-20766-1
34.
NilssonRHTedersooLAbarenkovK,et al.Methods Fungal community analysis by high-throughput sequencing of amplified markers – a user’ guide.
35.
WilkinsLGEEttingerCLJospinG,et al.Metagenome-assembled genomes provide new insight into the microbial diversity of two thermal pools in Kamchatka. Russia. 2019:1–15. https://doi.org/10.1038/s41598-019-39576-6
EdgarR.UCHIME2: improved chimera prediction for amplicon sequencing. bioRxiv. Epub ahead of print 2016.
38.
ScholzMWardDVPasolliE,et al.Strain-level microbial epidemiology and population genomics from shotgun metagenomics. Epub ahead of print2016;13https://doi.org/10.1038/nmeth.3802
39.
FangXMonkJMNurkS, et al.Analysis of Escherichia coli from a time-series of microbiome samples from a Crohns disease patient. 2018; 9: 1–14. https://doi.org/10.3389/fmicb.2018.02559
40.
WalkerAWMartinJCScottP, et al.16S rRNA gene-based profiling of the human infant gut microbiota is strongly influenced by sample processing and PCR primer choice. Microbiome2015; 1–11. https://doi.org/10.1186/s40168-015-0087-4
41.
ChenZHuiCHuiM, et al.crossm impact of preservation method and 16S rRNA hypervariable region on gut microbiota profiling. 2019; 4: 1–15. https://doi.org/10.1128/mSystems.00271-18
42.
ChoudhuryNSahuTKRaoAR, et al.An improved machine-learning-based approach to assess the microbial diversity in major North Indian river ecosystems. Genes2023; 14(5): 1082. https://doi.org/10.3390/genes14051082
43.
SzeMA.The impact of DNA polymerase and number of rounds of amplification in PCR on 16S rRNA gene sequence data. 2019; 4: 9–12. https://doi.org/10.1128/mSphere.00163-19
JiaoXZhengXMaL,et al.A benchmark study on error assessment and quality control of CCS reads derived from the PacBio RS. JData Mining Genomics Proteomics. Epub ahead of print July 2013;4https://doi.org/10.4172/2153-0602.1000136
50.
LaverTHarrisonJO’NeillPA, et al.Assessing the performance of the Oxford nanopore technologies MinION. Biomol Detect Quantif2015; 3: 1–8. https://doi.org/10.1016/j.bdq.2015.02.001
51.
EdgarRC.Accuracy of microbial community diversity estimated by closed- and open- reference Otus. Epub ahead of print 2017.
VerseuxCHeinickeCBilliD. Engineered cyanothece biofilms for oxygen production and perchlorate detoxification in Martian regolith simulants. Astrobiology2023; 23(5): 567–578. https://doi.org/10.1089/ast.2022.0123
QuinceCWalkerAWSimpsonJT, et al.Shotgun metagenomics, from sampling to analysis. Nat Biotechnol2017; 35: 833–844. https://doi.org/10.1038/nbt.3935
61.
KimYParkSLeeJ. DeepEC: a deep learning framework for enzyme commission number prediction reveals misclassification of promiscuous domains. Nucleic Acids Res2022; 50(18): e108. https://doi.org/10.1093/nar/gkac672
VurtureGWSedlazeckFJNattestadMet al.GenomeScope: Fast Reference-Free Genome Profiling from Short Reads. In: Bioinformatics2017. Epub ahead of print 2017.
65.
PengYLeungHCMYiuSM,et al. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2010. Epub ahead of print 2010. IDBA -A practical iterative De Bruijn graph De Novo assembler.
66.
PengYLeungHCMYiuSM, et al.IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics2012; 28: 1420–1428. https://doi.org/10.1093/bioinformatics/bts174
67.
MahadikKWrightCKulkarniM,et al.Scalable genome assembly through parallel de Bruijn graph construction for multiple k-mers. Sci Rep. Epub ahead of print 2019https://doi.org/10.1038/s41598-019-51284-9
68.
AfiahayatiSKSakakibaraY. MetaVelvet-SL: an extension of the velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA Res2015; 22: 69–77. https://doi.org/10.1093/dnares/dsu041
69.
NamikiTHachiyaTTanakaH, et al.MetaVelvet: an extension of velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res2012; 40: e155. https://doi.org/10.1093/nar/gks678
70.
World Health Organization (WHO). Global microbiome observatory 2024 report. WHO Press, 2024.
71.
ChenMShiSZhaoJ, et al.Propofol inhibits cell apoptosis and inflammatory response in ox-LDL-induced human umbilical vein endothelial cells through the modulation of the circ_0003645/miR-149-3p/TRAF7 axis. Clin Hemorheol Microcirc2022; 84(1): 1–18. https://doi.org/10.3233/CH-221437
