[1]

Simon C, Daniel R. 2011. Metagenomic analyses: past and future trends. Applied and Environmental Microbiology 77:1153−61

doi: 10.1128/AEM.02345-10
[2]

Tringe SG, Rubin EM. 2005. Metagenomics: DNA sequencing of environmental samples. Nature Reviews Genetics 6:805−14

doi: 10.1038/nrg1709
[3]

Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. 2017. Shotgun metagenomics, from sampling to analysis. Nature Biotechnology 35:833−44

doi: 10.1038/nbt.3935
[4]

Scholz MB, Lo CC, Chain PSG. 2012. Next generation sequencing and bioinformatic bottlenecks: the current state of metagenomic data analysis. Current Opinion in Biotechnology 23:9−15

doi: 10.1016/j.copbio.2011.11.013
[5]

Pereira MB, Wallroth M, Jonsson V, Kristiansson E. 2018. Comparison of normalization methods for the analysis of metagenomic gene abundance data. BMC Genomics 19:274

doi: 10.1186/s12864-018-4637-6
[6]

Liu YX, Qin Y, Chen T, Lu M, Qian X, et al. 2021. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein & cell 12:315−30

doi: 10.1007/s13238-020-00724-8
[7]

Li H, Durbin R. 2009. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25:1754−60

doi: 10.1093/bioinformatics/btp324
[8]

Langmead B, Trapnell C, Pop M, Salzberg SL. 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology 10:R25

doi: 10.1186/gb-2009-10-3-r25
[9]

Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. Nature Methods 9:357−59

doi: 10.1038/nmeth.1923
[10]

Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. 2017. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 14:417−19

doi: 10.1038/nmeth.4197
[11]

Chen H, Li DH, Jiang AJ, Li XG, Wu SJ, et al. 2022. Metagenomic analysis reveals wide distribution of phototrophic bacteria in hydrothermal vents on the ultraslow-spreading Southwest Indian Ridge. Marine Life Science & Technology 4:255−67

doi: 10.1007/s42995-021-00121-y
[12]

Cui G, Liu Z, Xu W, Gao Y, Yang S, et al. 2022. Metagenomic exploration of antibiotic resistance genes and their hosts in aquaculture waters of the semi-closed Dongshan Bay (China). Science of the Total Environment 838:155784

doi: 10.1016/j.scitotenv.2022.155784
[13]

Liang Y, Wang L, Wang Z, Zhao J, Yang Q, et al. 2019. Metagenomic analysis of the diversity of DNA viruses in the surface and deep sea of the South China Sea. Frontiers in Microbiology 10:1951

doi: 10.3389/fmicb.2019.01951
[14]

Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, et al. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11:119

doi: 10.1186/1471-2105-11-119
[15]

Wen C, Zheng Z, Shao T, Liu L, Xie Z, et al. 2017. Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis. Genome Biology 18:142

doi: 10.1186/s13059-017-1271-6
[16]

Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, et al. 2015. Structure and function of the global ocean microbiome. Science 348:1261359

doi: 10.1126/science.1261359
[17]

Li H. 2013. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv In press:1303.3997

doi: 10.48550/arXiv.1303.3997
[18]

Chen MY, Teng WK, Zhao L, Hu CX, Zhou YK, et al. 2021. Comparative genomics reveals insights into cyanobacterial evolution and habitat adaptation. The ISME Journal 15:211−27

doi: 10.1038/s41396-020-00775-z
[19]

Chen S, Arifeen MZU, Li M, Xu S, Wang H, et al. 2024. Diel patterns in the composition and activity of planktonic microbes in a subtropical bay. Ocean-Land-Atmosphere Research 3:0044

doi: 10.34133/olar.0044
[20]

Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, et al. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. Journal of Computational Biology A Journal of Computational Molecular Cell Biology 19:455

doi: 10.1089/cmb.2012.0021
[21]

Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, et al. 2021. Twelve years of SAMtools and BCFtools. Gigascience 10:giab008

doi: 10.1093/gigascience/giab008
[22]

Srivastava A, Malik L, Sarkar H, Zakeri M, Almodaresi F, et al. 2020. Alignment and mapping methodology influence transcript abundance estimation. Genome Biology 21:239

doi: 10.1186/s13059-020-02151-8
[23]

Mistry J, Finn RD, Eddy SR, Bateman A, Punta M. 2013. Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions. Nucleic Acids Research 41:e121

doi: 10.1093/nar/gkt263
[24]

Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15:550

doi: 10.1186/s13059-014-0550-8
[25]

Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, et al. 2009. Circos: an information aesthetic for comparative genomics. Genome Research 19:1639−45

doi: 10.1101/gr.092759.109
[26]

Parks DH, Chuvochina M, Rinke C, Mussig AJ, Chaumeil PA, et al. 2022. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Research 50:D785−D794

doi: 10.1093/nar/gkab776
[27]

Jonsson V, Österlund T, Nerman O, Kristiansson E. 2017. Variability in metagenomic count data and its influence on the identification of differentially abundant genes. Journal of Computational Biology 24:311−26

doi: 10.1089/cmb.2016.0180
[28]

Ausiannikava D, Mitchell L, Marriott H, Smith V, Hawkins M, et al. 2018. Evolution of genome architecture in archaea: spontaneous generation of a new chromosome in Haloferax volcanii. Molecular Biology and Evolution 35:1855−68

doi: 10.1093/molbev/msy075
[29]

Rocha EPC. 2008. The organization of the bacterial genome. Annual Review of Genetics 42:211−33

doi: 10.1146/annurev.genet.42.110807.091653
[30]

Dong MJ, Luo H, Gao F. 2023. DoriC 12.0: an updated database of replication origins in both complete and draft prokaryotic genomes. Nucleic Acids Research 51:D117−D120

doi: 10.1093/nar/gkac964
[31]

Ishiura M, Kutsuna S, Aoki S, Iwasaki H, Andersson CR, et al. 1998. Expression of a gene cluster kaiABC as a circadian feedback process in cyanobacteria. Science 281:1519−23

doi: 10.1126/science.281.5382.1519
[32]

Wu DC, Yao J, Ho KS, Lambowitz AM, Wilke CO. 2018. Limitations of alignment-free tools in total RNA-seq quantification. BMC Genomics 19:510

doi: 10.1186/s12864-018-4869-5
[33]

Teng W, Liao B, Chen M, Shu W. 2023. Genomic legacies of ancient adaptation illuminate GC-content evolution in bacteria. Microbiology Spectrum 11:e02145-22

doi: 10.1128/spectrum.02145-22
[34]

Kang DD, Froula J, Egan R, Wang Z. 2015. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3:e1165

doi: 10.7717/peerj.1165
[35]

Gilbert JA, Dupont CL. 2011. Microbial metagenomics: beyond the genome. Annual Review of Marine Science 3:347−71

doi: 10.1146/annurev-marine-120709-142811
[36]

Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, et al. 2016. A survey of best practices for RNA-seq data analysis. Genome Biology 17:13

doi: 10.1186/s13059-016-0881-8
[37]

Zhao Y, Li MC, Konaté MM, Chen L, Das B, et al. 2021. TPM, FPKM, or normalized counts? A comparative study of quantification measures for the analysis of RNA-seq data from the NCI patient-derived models repository. Journal of Translational Medicine 19:269

doi: 10.1186/s12967-021-02936-w
[38]

Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research 25:1043−55

doi: 10.1101/gr.186072.114