Metagenomics approaches for studying the human microbiome

Figures & data

Figure 1. Representation of the human-associated microbiome (Krishna et al. 2019).

Figure 2. General metagenomics strategy for human microbial discovery and analysis (Chiu and Miller 2019). Figure reproduced with permission from Chiu Charles (2024).

Table 1. Standard 16S/18S/ITS amplicon metagenomics sequencing platform.

Types	Region	Fragment Length	Primer	Primer sequences (5′–3′)
Bacterial 16S	$V4$	300 bp	515F	GTGCCAGCMGCCGCGGTAA
			806R	GGACTACHVGGGTWTCTAAT
	$\text{V}3-\text{V}4$	470 bp	341F	CCTAYGGGRBGCASCAG
			806R	GGACTACNNGGGTATCTAAT
	V4-V5	450 bp	515F	GTGCCAGCMGCCGCGGTAA
			907R	CCGTCAATTCCTTTGAGTTT
	V5-V7 (for endophytic)	300-400 bp	799F	AACMGGATTAGATACCCKG
			1193R	ACGTCATCCCCACCTTCC
Archaeal 16S	V4 – V5	400-500bp	Arch519F	CAGCCGCCGCGGTAA
Archaeal	V8	300bp	Arch915R	GTGCTCCCCCGCCAATTCCT
			1106F	TTWAGTCAGGCAACGAGC
			1378R	TGTGCAAGGAGCAGGGAC
Fungal 18S	V4	350 bp	528F	GCGGTAATTCCAGCTCCAA
	V9	200 bp	706R	AATCCRAGAATTTCACCTCT
			1380F	CCCTGCCHTTTGTACACAC
			1510R	CCTTCYGCAGGTTCACCTAC
Fungal ITS	ITS1	200-400bp	ITS5-1737F	GGAAGTAAAAGTCGTAACAAGG
			ITS2-2043R	GCTGCGTTCTTCATCGATGC
	ITS2	380 bp	ITS3-2024F	GCATCGATGAAGAACGCAGC
	ITS1-1F (for endophytic	200-400 bp	ITS4-2409R	TCCTCCGCTTATTGATATGC
			ITS1-1F-F	CTTGGTCATTTAGAGGAAGTAA
			ITS1-1F-R	GCTGCGTTCTTCATCGATGC
Sample Type	Amount(Qubit®)	Concentration	Volume	Purity(NanoDrop^TM)
Genomic DNA	≥ 200ng	≥ 20 ng/μL	≥ 12 μL	OD260/280 = 1.8∼2.0 no degradation or contamination

Figure 3. Basic metagenomics data analysis steps and currently used bioinformatics tools (Martin et al. 2018).

Table 2. Metagenomics data analysis steps and popular bioinformatics tools with their functions

Metagenomics data Analysis	Software	Description	Website	Reference
Quality control:	fastp	Produces high-quality, compact visualizations of microbial genomes and metagenomes	http://segatalab.cibio.unitn.it/tools/graphlan	(Turner et al. 2011)
De-duplication	FASTX-Toolkit, Fulcrum, FastUniq Clumpify, Cdhit-454 software	Removing exact duplicates without mapping on reference genomes	http://hannonlab.cshl.edu/fastxtoolkit/	(Patel and Jain 2012).
Trimming	FASTX-Toolkit, PRINTSEQ, Trim Galore, BBTools, trimmomatic, Sickle and deFUME	Trim adapters and low-quality bases	http://www.cbs.dtu.dk/services/deFUME/ http://www.usadellab.org/cms/index.php? page = trimmomatic	(Karlsson et al. 2014)
Decontamination	FastQC Screen, Meta-QC-Chain, Deconseq, BBsplit ProDeGe,BBMap, KneadData, BBwrap,	Remove contaminating genomes and quality access	https://www.bioinformatics.babraham.ac. uk/projects/fastq_screen/	(Martin et al. 2018; Wang et al. 2015)
Microbial abundances	GRAMMy, Kraken, ConStrains, MBBC	Estimate the microbiome abundance	https://ccb.jhu.edu/software/kraken/	(Lin and Peddada 2020)
Binning	MetaBAT, Kraken, MetaPhlAn2, mOTU MyTaxa, CARMA3, MetAMOS, Bowtie2, Phylosift, MEGAN6, Centrifuge, Taxonomer	Binning millions of contigs from thousands of samples and diversity analysis	https://bitbucket.org/berkeleylab/metabat, http://enve-omics.ce.gatech.edu/mytaxa, http://ab.inf.uni- tuebingen.de/software/megan	(Kang et al. 2019)
Sequence assembly	TIGR, phrap, VICUNA, Omega, MetaVelvet,MIRA, Newbler, OSLay, khmer, RayMeta, Meta-IDBA	Aligning and merging reads with the aim of reconstructing the original genomic sequence	http://metavelvet.dna.bio.keio.ac.jp/	(Namiki et al. 2011)
Functional annotation/gene prediction	EBI	To compare functional analyses of sequence.	https://www.ebi.ac.uk/metagenomics	(Buza et al. 2007)
	PICRUSt	Functional profiling of a metagenome using 16S amplicon sequencing	http://picrust.github.com/	(Douglas et al. 2018)
▅Homology-based tools	IMG/M, MG-RAST, MetaGeneMark, RAMMCAP	Predicted protein sequences compared to reference protein sequences	http://metagenomics.anl.gov/; http://weizhonglab.ucsd.edu/ram mcap/cgibin/rammcap.cgi	(Nayfach et al. 2015)
▅Motif – or pattern-based tools	InterPro, Prodigal, CARD	Screen common motifs in metagenomic sequences	http://www.bork.embl.de/software/smash/	(Ruiz-Perez et al. 2021)
▅Context-based tools	IMG/M, BacMet, SmashCommunity	Screen new metagenomic sequences that do not share any homology pattern	https://img.jgi.doe.gov/m/	(Nayfach et al. 2015)
Putative annotations	PSORT, CELLO, DOLLOP, Lipo, SignalP, ISsaga, VFDB, MvirDB	Predicting functional properties (CAE, protein localizations),	https://www.psort.org/psortb/	(Bharti and Grimm 2021)
Microbial abundance	FANTOM	Comparative analysis of metagenomics abundance data	http://www.sysbio.se/Fantom/	(Bono et al. 2002)
Microbial Diversity	QIIME	Data trimming and filtering, diversity analysis, and visualization	http://qiime.org/	(Caporaso et al. 2010)
	Meta Microbes Online	Phylogenetic analysis of genes from metagenomes	http://meta.MicrobesOnline.org	(Nagpal et al. 2020)
Metabolic pathway reconstruction	SEED, HUMAnN and MetaCyc	Determine metabolic pathway	http://huttenhoer.sph.harvard.edu/human	(Franzosa et al. 2018)
Statistical downstream analysis	Calypso, MicrobiomeAnalys, Calypso, Metaviz and PUMA	To explore large metagenomics data using univariate and multivariate statistical experiments and data representations	http://www.microbiomeanalyst.ca/	(Bharti and Grimm 2021)
	Primer-E-Package (SIMPER)	Identification of the species, and identification of gene functions	https://www.primer-e.com/oursoftware/ primer-version-7/	(Eaton and Hamilton 2023)
	ANOSIM	Generation of multidimensional scaling plots, analysis of similarities	https://mb3is.megx.net/gustame/ hypothesis-tests/anosim	(Anderson and Walsh 2013)
	Shotgun FunctionalizeR, Microbiome Analyst	Evaluate functional differences between samples	http://www.microbiomeanalyst.ca/	(Muegge et al. 2011)

Krishna SB, Dubey A, Malla MA, Kothari R, Upadhyay CP, Adam JK, Kumar A. 2019. Integrating microbiome network: establishing linkages between plants, microbes and human health. Open J Med Microbiol. 13(1.

 Google Scholar

Chiu CY, Miller SA. 2019. Clinical metagenomics. Nat Rev Genet. 20(6):341–355. doi:10.1038/s41576-019-0113-7.

 PubMed Web of Science ®Google Scholar

Martin TC, Visconti A, Spector TD, Falchi M. 2018. Conducting metagenomic studies in microbiology and clinical research. Appl Microbiol Biotechnol. 102:8629–8646. doi:10.1007/s00253-018-9209-9.

 PubMed Web of Science ®Google Scholar

Turner S, Loren L, Yuki B, Christopher S, Dana C, Andrew T. 2011. Mariza de A, Kimberly FD, Jonathan LH, Geoffrey H, Gail J. Lan J, Iftikhar JK, Rongling L, Hua L, Teri AM, Martha M, Catherine AM, Andrew NM, Daniel BM, Justin EP, Elizabeth WP, Luke VR, Russell AW, Rebecca LZ, Marylyn DR: Quality control procedures fo r genome-wide association studies. Curr Prot Hum Genet. 1–19.

 Google Scholar

Patel RK, Jain M. 2012. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS One. 7(2):e30619. doi:10.1371/journal.pone.0030619.

 PubMed Web of Science ®Google Scholar

Karlsson FH, Nookaew I, Nielsen J. 2014. Metagenomic data utilization and analysis (MEDUSA) and construction of a global gut microbial gene catalogue. PLoS Comput Biol. 10(7):e1003706. doi:10.1371/journal.pcbi.1003706.

 PubMed Web of Science ®Google Scholar

Wang W-L, Xu S-Y, Ren Z-G, Tao L, Jiang J-W, Zheng S-S. 2015. Application of metagenomics in the human gut microbiome. World J Gastroenterol. 21(3):803. doi:10.3748/wjg.v21.i3.803.

 PubMed Web of Science ®Google Scholar

Lin H, Peddada SD. 2020. Analysis of microbial compositions: a review of normalization and differential abundance analysis. NPJ Biofilms Microbiomes. 6(1):60. doi:10.1038/s41522-020-00160-w.

 PubMed Web of Science ®Google Scholar

Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. 2019. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 7:e7359. doi:10.7717/peerj.7359.

 PubMed Web of Science ®Google Scholar

Namiki T, Hachiya T, Tanaka H, Sakakibara Y. 2011. MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine.

 Google Scholar

Buza TJ, McCarthy FM, Burgess SC. 2007. Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome. BMC Gen. 8:1–10. doi:10.1186/1471-2164-8-1.

 PubMedGoogle Scholar

Douglas GM, Beiko RG, Langille MG. 2018. Predicting the functional potential of the microbiome from marker genes using PICRUSt. Microbiome Anal: Methods Prot. 169–177. doi:10.1007/978-1-4939-8728-3_11.

 Google Scholar

Nayfach S, Bradley PH, Wyman SK, Laurent TJ, Williams A, Eisen JA, Pollard KS, Sharpton TJ. 2015. Automated and accurate estimation of gene family abundance from shotgun metagenomes. PLoS Comput Biol. 11(11):e1004573. doi:10.1371/journal.pcbi.1004573.

 PubMed Web of Science ®Google Scholar

Ruiz-Perez CA, Conrad RE, Konstantinidis KT. 2021. Propedia: a database for protein–peptide identification based on a hybrid clustering algorithm. BMC Bioinformatics. 22:1–16. doi:10.1186/s12859-020-03881-z.

 PubMed Web of Science ®Google Scholar

Bharti R, Grimm DG. 2021. Current challenges and best-practice protocols for microbiome analysis. Brief Bioinform. 22(1):178–193. doi:10.1093/bib/bbz155.

 PubMed Web of Science ®Google Scholar

Bono H, Kasukawa T, Furuno M, Hayashizaki Y, Okazaki Y. 2002. FANTOM DB: database of functional annotation of RIKEN mouse cDNA clones. Nucleic Acids Res. 30(1):116–118. doi:10.1093/nar/30.1.116.

 PubMed Web of Science ®Google Scholar

Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 7(5):335–336. doi:10.1038/nmeth.f.303.

 PubMed Web of Science ®Google Scholar

Nagpal S, Singh R, Yadav D, Mande SS. 2020. MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks. Nucleic Acids Res. 48(W1):W572–W579. doi:10.1093/nar/gkaa254.

 PubMed Web of Science ®Google Scholar

Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M, Weingart G, Lipson KS, Knight R, Caporaso JG, Segata N. 2018. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 15(11):962–968. doi:10.1038/s41592-018-0176-y.

 PubMed Web of Science ®Google Scholar

Eaton WD, Hamilton DA. 2023. Enhanced carbon, nitrogen and associated bacterial community compositional complexity, stability, evenness, and differences within the tree-soils of Inga punctata along an age gradient of planted trees in reforestation plots. Plant Soil. 484(1-2):327–346. doi:10.1007/s11104-022-05793-8.

 Web of Science ®Google Scholar

Anderson MJ, Walsh DC. 2013. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol Monogr. 83(4):557–574. doi:10.1890/12-2010.1.

 Web of Science ®Google Scholar

Muegge BD, Kuczynski J, Knights D, Clemente JC, González A, Fontana L, Henrissat B, Knight R, Gordon JI. 2011. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science. 332(6032):970–974. doi:10.1126/science.1198719.

 PubMed Web of Science ®Google Scholar

Data availability statement

Data sharing does not apply to this article as no new data were created or analyzed in this study.