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About

HLMA is a new database that assists user in investigating the abundance and prevalence of human lung microbes, which associated with both health and respiratory diseases. Lung microbes is now emerging as a potential contributor to chronic lung diseases, including cancer. In order to achieve a comprehensive understanding of the composition of lung microbiota and the correlation between specific microbes and respiratory diseases, HLMA was constructed by integrating xx datasets from multiple sources, including NGDC, ENA and NCBI , as well as the meta-data of corresponding human hosts. These datasets encompassed xx samples from the upper and lower respiratory tract (e.g., nasopharynx, oropharynx and lung), belonging to health and 38 respiratory diseases. Through a unified analysis workflow, a total of 6,350 species, spanning across 2,578 genera, were currently incorporated in HLMA. Additionally, a serious of analysis were performed for each dataset, including taxonomic structure, alpha diversity, beta diversity, co-occurrence network, taxa-phenotype association and LAD score among different conations.
HLMA supports Google Chrome and Microsoft Edge browsers.
Aims and features of HLMA:
Primary aims:
  • In order to promote the reusability of metagenomic data (e.g., mNGS, 16S rRNA and ITS) associated with human lung microbiome;
  • To facilitate the identification of microbes associated with health and respiratory diseases (e.g., COPD, asthma and NSCLC).
Features:
  • Browse or search specific microbes found in the upper and lower respiratory tracts, and investigate their associations with both respiratory health and diseases, such as COPD, cystic fibrosis and LUAD;
  • Explore and compare the composition and diversity of the microbiome between different groups within each independent dataset, allowing for insights into microbial variations across specific conditions or population groups;
  • Investigate potential microbes that are associated with respiratory diseases, aiding in the identification of microbial biomarkers of disease;
  • Perform a series of functions, such as ChartDoc for interactive communication with HLMA in the form of a conversation or dialogue, drug resistance analysis on certain datasets and summary of key studies;
  • Download all the obtained results and accompanying figures for further research.
Citation:
HLMA: a curated database for human lung microbes associated with health and respiratory diseases.

News

  • July 2023
    The development of the HLMA database was finished and it was released in Jul. 2023.
  • January 2023
    The development of the HLMA database began in Jan. 2023.
  • October 2022
    We commenced the process of requirements analysis and functional design for HLMAT since Oct. 2022.
  • September 2022
    A series of bioinformatic analysis workflow of metagenomic data were established and were performed since Sep. 2022.
  • July 2022
    The HLMA project launched and initiated the collection of metagenomic data from the human upper and lower respiratory tract in July 2022.

Help

  • 1. What is HLMA?
    The HLMA is a curated database for lung microbes across the upper and lower respiratory tract and their associations with health and respiratory diseases (e.g., COPD, asthma and LUAD). The development of HLMA aims to: (i) to promote the reusability of metagenomic data (e.g., mNGS, 16S rRNA and ITS) associated with human lung microbiome (e.g., archaea, bacteria, viruses and fungi); (ii) to assist users to browse or search the abundance and prevalence of lung microbes across different body sites and different disease types; (ii) To facilitate the identification of disease-related/phenotype-related lung microbes.
    The HLMA database encompasses the following features:
    • Browse or search microbes across upper and lower respiratory tract and their associations with health and respiratory diseases (including COPD, LUSC, LUAD, etc.);
    • Browse or search the composition and diversity of the microbiome between groups in each independent dataset;
    • Explore potential microbes associated with respiratory diseases;
    • Perform LAD score analysis on microbiome by submitting a new matrix containing of the abundance of microbes, along with the corresponding meta-data for each sample;
    • Download all results and figures for further research.
  • 2. What is lung microbiota and how can it be identified and quantified?
    A growing body of evidence indicates that lung microbiome is emerging as potential contributors to chronic lung diseases, even lung cancer. The lung microbiota is the set of archaea, bacteria, fungi viruses and protozoa, which reside in the lower airways. The oral and upper respiratory tract microbes shape the lung microbiota. So, in order to achieve a comprehensive understanding of the composition and characteristics of lung microbiota, thousands of specimens from six body sites, covering the upper and lower respiratory tract, were integrated in the HLMA database.
    With the advancement of sequencing technologies, metagenome sequencing technology (e.g., mNGS, 16S rRNA and ITS) is used by researchers to parse complex microbial communities and their functional capabilities. So far, we collected and curated xx metagenomic datasets from 2,578 metagenomic datasets from multiple public databases in HLMA, which contained xx samples and belonged to health and 38 diseases. The counts/abundance of each microbe per sample was quantified by using amplicon sequence variants (Qiime2 for 16S/ITS) or k-mer-based approach (Kraken2 for mNGS).
  • 3. Data processing and quality control
    The overall workflow of data processing is depicted in Fig.1, including a series of bioinformatic analysis (e.g., adapters trimming, low-quality reads removing, taxonomic assignment and relative abundance estimation). In HLMA, to ensure data quality, the following criteria were applied for all samples/runs: (i) samples/runs with fewer than 5,000 reads were excluded; and (ii) samples/runs in which a single species or genus that accounted for 99.99% or more of the total abundance were excluded.
    Fig. 1
    Fig. 1 The overall workflow of data processing in HLMA
  • 4. Database construction and web development
    The overall framework for HLMA development is illustrated in Fig. 2. All data in HLMA stored into the MySQL (v5.7.25) database. The development of the front-end webpages utilized HTML5 and JavaScript, while Java (v1.8) with the Spring framework (v1.1.2) for back-end webpages. We utilized jQuery (v1.8.3) and Bootstrap (v5.3.0) to bridge the front-end and back-end components, along with other open-source libraries such as echarts.js (v5.4). The website was hosted on a nginx server (v1.23.3).
    Fig. 2
    Fig. 2 The development of HLMA
  • 5. How to query the database?
    HLMA is a user-friendly web interface that provides to allow users to query the database from three distinct web pages, including homepage, search page and advanced search page, with multiple criteria, such as sampling type, genus/species, disease type, gender or age (Fig. 3A-C).
    Once quiring one genus/species, all relevant results are visualized, including the general description for the taxa (Fig. 4A), the relative abundance of prevalent of the taxa across various body sites (Fig. 4B), health and different respiratory diseases (Fig. 4C), as well as in different regions/countries (Fig. 4D). It is worth mentioning that the relative abundance of the taxa across different body sites was visualized using UMAP in the section "Body sites" (Fig. 4B). Additionally, the prevalence of the taxa was visualized using a global map, providing a visual representation of its distribution across different regions worldwide. This visualization not only highlights the overall prevalence but also allows for the exploration of its distribution among different diseases within each region (Fig. 4D).
    A detailed table is available below each figure, which presents detailed statistics of the taxa under different conditions. To enhance user convenience and accessibility, each table is equipped with functional features, including sort for certain column, download the table in TSV (Tab-Separated Values) format, and search by using a keyword.
    Fig. 3
    Fig. 3 Search function in HLMA, (A) Quick search across global database in the "Home" page by a keyword, such as Streptococcus, LUAD, and lung; (B) Keyword search in the "Search" page by using one and/or two criteria; (C) Advanced search in the "Advanced search" page by using multiple criteria, such as sampling type, genus/species, disease type, gender or age.
    Fig.4
    Fig.4 Taking the genus Streptococcus as an example and visualization of all relevant results. (A) The general description of the taxa, such as taxonomy and links to external databases; (B-D) The relative abundance of prevalent of the taxa across various body sites (B), health and different respiratory diseases (C), as well as in different regions/countries (D).
  • 6. Data overview and statistics.
    Currently, both the "Home" page and the "Statistics" page in HLMA display the distribution of manually collected datasets as well as related metadata. A total of 21,833 runs/samples from 92 independent datasets, covering the upper and lower respiratory tract, including 7 body sites (Fig.5A), were curated in HLMA (Fig.5).
    Fig. 5
    Fig.5 The pie chart illustrating project and sample sizes across different body sites (A) and various health/diseases (B) as well as barplots depicting the distribution of publication per year (C) and by region/country (D).
  • 7. How to use toolkit?
    Currently, three toolkits were embedded in HLMA, including Summary of key studies, Drug resistance analysis, and submission. The tutorial of those toolkits as follows:
    7.1 Summary of key studies
    This section displays current knowledge of the lung microbiome in human diseases, spanning across chronic, acute, and other types of lung diseases (Fig. 6). If a dataset is available, users can click on it to access a more detailed page, just like on the "Search" or "Browse" page.
    Fig. 6
    Fig.6 Summary of key studies on the lung microbiota in chronic, acute, and other lung diseases.
    7.2 Drug resistance analysis
    The drug resistance analysis is a helpful application to discover and identify the potential antibiotic resistance (AR) and virulence factors (VF) by using mNGS data. Once a special dataset the user chooses, the results are displayed as table format (Fig. 7).
    Fig. 7
    Fig.7 Taking PRJNA7248 as an example to display the results of drug resistance analysis.
    7.3 Differential abundance test
    The tool enables users to submit their new data for detection of differential abundance taxa. We used R package microeco (version 0.20.0) to identify condition-specific taxa (Fig. 8). On the "Differential abundance test" page, users firstly fill in some basic information about project (Fig. 8A). Then, some important parameters will need to set by users before executing the program, such as method (e.g., LEfSe, ANOVA), taxonomic level (e.g., genus,class) (Fig. 8B). Once a click on "Run", the related details will send to them in the format of an Email. The "*" symbol indicates the required fields.
    Fig. 8
    Fig.8 The toolkit "Differential abundance test" page in HLMA.
  • 8. What's ChartDoc?
    To enhance user experience, ChatDoctor (Cite as arXiv:2303.14070; https://arxiv.org/abs/2303.14070), a specialized medical chat model, is embedded in the upper right corner of the page. Once querying a keyword or one question, user can access detailed taxa and other related description anytime, day or night. This can be particularly beneficial for individuals seeking immediate information.
    ChartDoc

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Thousands of metagenomic data (e.g., mNGS, 16S rRNA and ITS), which generated from specimens of individuals with health conditions or respiratory diseases, were enrolled in HLMA. All relevant clinical/phenotype information were manually collected and integrated, including body sites (e.g., oral cavity, nasal cavity and lung), disease types (e.g., COPD, SCLC and LUAD), clinical features (e.g., antibiotic use, tumor stage, recurrence, treatment strategy and efficacy), and individual characteristics (e.g., age, gender and smoker), etc.
Tab. Detailed information of all datasets in HLMA.
# Project ID Species Nr. of samples SequencingType Year PMID Center.Name Country Description