
@article{janssens_disbiome_2018,
	title = {Disbiome database: linking the microbiome to disease},
	volume = {18},
	issn = {1471-2180},
	url = {https://doi.org/10.1186/s12866-018-1197-5},
	doi = {10.1186/s12866-018-1197-5},
	abstract = {Recent research has provided fascinating indications and evidence that the host health is linked to its microbial inhabitants. Due to the development of high-throughput sequencing technologies, more and more data covering microbial composition changes in different disease types are emerging. However, this information is dispersed over a wide variety of medical and biomedical disciplines.},
	number = {1},
	journal = {BMC Microbiology},
	author = {Janssens, Yorick and Nielandt, Joachim and Bronselaer, Antoon and Debunne, Nathan and Verbeke, Frederick and Wynendaele, Evelien and Van Immerseel, Filip and Vandewynckel, Yves-Paul and De Tré, Guy and De Spiegeleer, Bart},
	year = {2018},
	pages = {50},
}

@article{galperin_cog_2021,
	title = {{COG} database update: focus on microbial diversity, model organisms, and widespread pathogens},
	volume = {49},
	issn = {0305-1048},
	url = {https://doi.org/10.1093/nar/gkaa1018},
	doi = {10.1093/nar/gkaa1018},
	abstract = {The Clusters of Orthologous Genes (COG) database, also referred to as the Clusters of Orthologous Groups of proteins, was created in 1997 and went through several rounds of updates, most recently, in 2014. The current update, available at https://www.ncbi.nlm.nih.gov/research/COG, substantially expands the scope of the database to include complete genomes of 1187 bacteria and 122 archaea, typically, with a single genome per genus. In addition, the current version of the COGs includes the following new features: (i) the recently deprecated NCBI’s gene index (gi) numbers for the encoded proteins are replaced with stable RefSeq or GenBank{\textbackslash}ENA{\textbackslash}DDBJ coding sequence (CDS) accession numbers; (ii) COG annotations are updated for \&gt;200 newly characterized protein families with corresponding references and PDB links, where available; (iii) lists of COGs grouped by pathways and functional systems are added; (iv) 266 new COGs for proteins involved in CRISPR-Cas immunity, sporulation in Firmicutes and photosynthesis in cyanobacteria are included; and (v) the database is made available as a web page, in addition to FTP. The current release includes 4877 COGs. Future plans include further expansion of the COG collection by adding archaeal COGs (arCOGs), splitting the COGs containing multiple paralogs, and continued refinement of COG annotations.},
	number = {D1},
	urldate = {2021-09-15},
	journal = {Nucleic Acids Research},
	author = {Galperin, Michael Y and Wolf, Yuri I and Makarova, Kira S and Vera Alvarez, Roberto and Landsman, David and Koonin, Eugene V},
	year = {2021},
	pages = {D274--D281},
}

@article{kanehisa_kegg_2017,
	title = {{KEGG}: new perspectives on genomes, pathways, diseases and drugs},
	volume = {45},
	issn = {0305-1048},
	url = {https://doi.org/10.1093/nar/gkw1092},
	doi = {10.1093/nar/gkw1092},
	abstract = {KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.},
	number = {D1},
	urldate = {2021-09-15},
	journal = {Nucleic Acids Research},
	author = {Kanehisa, Minoru and Furumichi, Miho and Tanabe, Mao and Sato, Yoko and Morishima, Kanae},
	year = {2017},
	pages = {D353--D361},
}

@article{wu_clusterprofiler_2021,
	title = {{clusterProfiler} 4.0: {A} universal enrichment tool for interpreting omics data},
	volume = {2},
	issn = {2666-6758},
	url = {https://www.sciencedirect.com/science/article/pii/S2666675821000667},
	doi = {10.1016/j.xinn.2021.100141},
	abstract = {Summary
Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.},
	number = {3},
	journal = {The Innovation},
	author = {Wu, Tianzhi and Hu, Erqiang and Xu, Shuangbin and Chen, Meijun and Guo, Pingfan and Dai, Zehan and Feng, Tingze and Zhou, Lang and Tang, Wenli and Zhan, Li and Fu, Xiaocong and Liu, Shanshan and Bo, Xiaochen and Yu, Guangchuang},
	year = {2021},
	keywords = {biological knowledge mining, clusterProfiler, enrichment analysis, functional analysis, visualization},
	pages = {100141},
}

@article{tong_alterations_2019,
	title = {Alterations to the {Lung} {Microbiome} in {Idiopathic} {Pulmonary} {Fibrosis} {Patients}},
	volume = {9},
	issn = {2235-2988},
	url = {https://www.frontiersin.org/article/10.3389/fcimb.2019.00149},
	doi = {10.3389/fcimb.2019.00149},
	abstract = {Lung microbiome ecosystem homeostasis in idiopathic pulmonary fibrosis (IPF) remains uncharacterized. The aims of this study were to identify unique microbial signatures of the lung microbiome and analyze microbial gene function in IPF patients. DNA isolated from BALF samples was obtained for high-throughput gene sequencing. Microbial metagenomic data were used for principal component analysis (PCA) and analyzed at different taxonomic levels. Shotgun metagenomic data were annotated using the KEGG database and were analyzed for functional and metabolic pathways. In this study, 17 IPF patients and 38 healthy subjects (smokers and non-smokers) were recruited. For the PCA, the first and the second principal component explained 16.3 and 13.4\% of the overall variability, respectively. The β diversity of microbiome was reduced in the IPF group. Signature of IPF's microbes was enriched of Streptococcus, Pseudobutyrivibrio, and Anaerorhabdus. The translocation of lung microbiome was shown that 32.84\% of them were from oral. After analysis of gene function, ABC transporter systems, biofilm formation, and two-component regulatory system were enriched in IPF patients' microbiome. Here we shown the microbiology characteristics in IPF patients. The microbiome may participate in altering internal conditions and involving in generating antibiotic resistance in IPF patients.},
	journal = {Frontiers in Cellular and Infection Microbiology},
	author = {Tong, Xunliang and Su, Fei and Xu, Xiaomao and Xu, Hongtao and Yang, Ting and Xu, Qixia and Dai, Huaping and Huang, Kewu and Zou, Lihui and Zhang, Wenna and Pei, Surui and Xiao, Fei and Li, Yanming and Wang, Chen},
	year = {2019},
	pages = {149},
}

@article{li_integrated_2014,
	title = {An integrated catalog of reference genes in the human gut microbiome},
	volume = {32},
	issn = {1546-1696},
	url = {https://doi.org/10.1038/nbt.2942},
	doi = {10.1038/nbt.2942},
	abstract = {Li et al. provide the largest catalog of genes in the human gut microbiome based on samples from {\textgreater}1,200 individuals across three continents.},
	number = {8},
	journal = {Nature Biotechnology},
	author = {Li, Junhua and Jia, Huijue and Cai, Xianghang and Zhong, Huanzi and Feng, Qiang and Sunagawa, Shinichi and Arumugam, Manimozhiyan and Kultima, Jens Roat and Prifti, Edi and Nielsen, Trine and Juncker, Agnieszka Sierakowska and Manichanh, Chaysavanh and Chen, Bing and Zhang, Wenwei and Levenez, Florence and Wang, Juan and Xu, Xun and Xiao, Liang and Liang, Suisha and Zhang, Dongya and Zhang, Zhaoxi and Chen, Weineng and Zhao, Hailong and Al-Aama, Jumana Yousuf and Edris, Sherif and Yang, Huanming and Wang, Jian and Hansen, Torben and Nielsen, Henrik Bjørn and Brunak, Søren and Kristiansen, Karsten and Guarner, Francisco and Pedersen, Oluf and Doré, Joel and Ehrlich, S Dusko and Pons, Nicolas and Le Chatelier, Emmanuelle and Batto, Jean-Michel and Kennedy, Sean and Haimet, Florence and Winogradski, Yohanan and Pelletier, Eric and LePaslier, Denis and Artiguenave, François and Bruls, Thomas and Weissenbach, Jean and Turner, Keith and Parkhill, Julian and Antolin, Maria and Casellas, Francesc and Borruel, Natalia and Varela, Encarna and Torrejon, Antonio and Denariaz, Gérard and Derrien, Muriel and van Hylckama Vlieg, Johan E T and Viega, Patrick and Oozeer, Raish and Knoll, Jan and Rescigno, Maria and Brechot, Christian and M'Rini, Christine and Mérieux, Alexandre and Yamada, Takuji and Tims, Sebastian and Zoetendal, Erwin G and Kleerebezem, Michiel and de Vos, Willem M and Cultrone, Antonella and Leclerc, Marion and Juste, Catherine and Guedon, Eric and Delorme, Christine and Layec, Séverine and Khaci, Ghalia and van de Guchte, Maarten and Vandemeulebrouck, Gaetana and Jamet, Alexandre and Dervyn, Rozenn and Sanchez, Nicolas and Blottière, Hervé and Maguin, Emmanuelle and Renault, Pierre and Tap, Julien and Mende, Daniel R and Bork, Peer and Wang, Jun and {MetaHIT Consortium}},
	year = {2014},
	pages = {834--841},
}

@article{xie_shotgun_2016,
	title = {Shotgun {Metagenomics} of 250 {Adult} {Twins} {Reveals} {Genetic} and {Environmental} {Impacts} on the {Gut} {Microbiome}},
	volume = {3},
	issn = {2405-4712},
	url = {https://www.sciencedirect.com/science/article/pii/S2405471216303234},
	doi = {10.1016/j.cels.2016.10.004},
	abstract = {Summary
The gut microbiota has been typically viewed as an environmental factor for human health. Twins are well suited for investigating the concordance of their gut microbiomes and decomposing genetic and environmental influences. However, existing twin studies utilizing metagenomic shotgun sequencing have included only a few samples. Here, we sequenced fecal samples from 250 adult twins in the TwinsUK registry and constructed a comprehensive gut microbial reference gene catalog. We demonstrate heritability of many microbial taxa and functional modules in the gut microbiome, including those associated with diseases. Moreover, we identified 8 million SNPs in the gut microbiome and observe a high similarity in microbiome SNPs between twins that slowly decreases after decades of living apart. The results shed new light on the genetic and environmental influences on the composition and function of the gut microbiome that could relate to risk of complex diseases.},
	number = {6},
	journal = {Cell Systems},
	author = {Xie, Hailiang and Guo, Ruijin and Zhong, Huanzi and Feng, Qiang and Lan, Zhou and Qin, Bingcai and Ward, Kirsten J. and Jackson, Matthew A. and Xia, Yan and Chen, Xu and Chen, Bing and Xia, Huihua and Xu, Changlu and Li, Fei and Xu, Xun and Al-Aama, Jumana Yousuf and Yang, Huanming and Wang, Jian and Kristiansen, Karsten and Wang, Jun and Steves, Claire J. and Bell, Jordana T. and Li, Junhua and Spector, Timothy D. and Jia, Huijue},
	year = {2016},
	keywords = {heritability, metabolic diseases, microbiome, SNPs},
	pages = {572--584.e3},
}

@article{ma_comprehensive_2020,
	title = {A comprehensive non-redundant gene catalog reveals extensive within-community intraspecies diversity in the human vagina},
	volume = {11},
	issn = {2041-1723},
	url = {https://doi.org/10.1038/s41467-020-14677-3},
	doi = {10.1038/s41467-020-14677-3},
	abstract = {Analysis of metagenomic and metatranscriptomic data is complicated and typically requires extensive computational resources. Leveraging a curated reference database of genes encoded by members of the target microbiome can make these analyses more tractable. In this study, we assemble a comprehensive human vaginal non-redundant gene catalog (VIRGO) that includes 0.95 million non-redundant genes. The gene catalog is functionally and taxonomically annotated. We also construct a vaginal orthologous groups (VOG) from VIRGO. The gene-centric design of VIRGO and VOG provides an easily accessible tool to comprehensively characterize the structure and function of vaginal metagenome and metatranscriptome datasets. To highlight the utility of VIRGO, we analyze 1,507 additional vaginal metagenomes, and identify a high degree of intraspecies diversity within and across vaginal microbiota. VIRGO offers a convenient reference database and toolkit that will facilitate a more in-depth understanding of the role of vaginal microorganisms in women’s health and reproductive outcomes.},
	number = {1},
	journal = {Nature Communications},
	author = {Ma, Bing and France, Michael T. and Crabtree, Jonathan and Holm, Johanna B. and Humphrys, Michael S. and Brotman, Rebecca M. and Ravel, Jacques},
	year = {2020},
	pages = {940},
}

@article{li_characterization_2021,
	title = {Characterization of the human skin resistome and identification of two microbiota cutotypes},
	volume = {9},
	issn = {2049-2618},
	url = {https://doi.org/10.1186/s40168-020-00995-7},
	doi = {10.1186/s40168-020-00995-7},
	abstract = {The human skin microbiota is considered to be essential for skin homeostasis and barrier function. Comprehensive analyses of its function would substantially benefit from a catalog of reference genes derived from metagenomic sequencing. The existing catalog for the human skin microbiome is based on samples from limited individuals from a single cohort on reference genomes, which limits the coverage of global skin microbiome diversity.},
	number = {1},
	journal = {Microbiome},
	author = {Li, Zhiming and Xia, Jingjing and Jiang, Liuyiqi and Tan, Yimei and An, Yitai and Zhu, Xingyu and Ruan, Jie and Chen, Zhihua and Zhen, Hefu and Ma, Yanyun and Jie, Zhuye and Xiao, Liang and Yang, Huanming and Wang, Jian and Kristiansen, Karsten and Xu, Xun and Jin, Li and Nie, Chao and Krutmann, Jean and Liu, Xiao and Wang, Jiucun},
	year = {2021},
	pages = {47},
}

@article{frolkis_smpdb_2010,
	title = {{SMPDB}: {The} {Small} {Molecule} {Pathway} {Database}},
	volume = {38},
	issn = {0305-1048},
	url = {https://doi.org/10.1093/nar/gkp1002},
	doi = {10.1093/nar/gkp1002},
	abstract = {The Small Molecule Pathway Database (SMPDB) is an interactive, visual database containing more than 350 small-molecule pathways found in humans. More than 2/3 of these pathways (\&gt;280) are not found in any other pathway database. SMPDB is designed specifically to support pathway elucidation and pathway discovery in clinical metabolomics, transcriptomics, proteomics and systems biology. SMPDB provides exquisitely detailed, hyperlinked diagrams of human metabolic pathways, metabolic disease pathways, metabolite signaling pathways and drug-action pathways. All SMPDB pathways include information on the relevant organs, organelles, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Each small molecule is hyperlinked to detailed descriptions contained in the Human Metabolome Database (HMDB) or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. All SMPDB pathways are accompanied with detailed descriptions, providing an overview of the pathway, condition or processes depicted in each diagram. The database is easily browsed and supports full text searching. Users may query SMPDB with lists of metabolite names, drug names, genes/protein names, SwissProt IDs, GenBank IDs, Affymetrix IDs or Agilent microarray IDs. These queries will produce lists of matching pathways and highlight the matching molecules on each of the pathway diagrams. Gene, metabolite and protein concentration data can also be visualized through SMPDB's mapping interface. All of SMPDB's images, image maps, descriptions and tables are downloadable. SMPDB is available at: http://www.smpdb.ca.},
	number = {suppl\_1},
	urldate = {2021-07-10},
	journal = {Nucleic Acids Research},
	author = {Frolkis, Alex and Knox, Craig and Lim, Emilia and Jewison, Timothy and Law, Vivian and Hau, David D. and Liu, Phillip and Gautam, Bijaya and Ly, Son and Guo, An Chi and Xia, Jianguo and Liang, Yongjie and Shrivastava, Savita and Wishart, David S.},
	year = {2010},
	pages = {D480--D487},
}
