NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition.

Dietary habits have been proven to have an impact on the microbial composition and health of the human gut. Over the past decade, researchers have discovered that gut microbiota can use nutrients to produce metabolites that have major implications for human physiology. However, there is no comprehensive system that specifically focuses on identifying nutrient deficiencies based on gut microbiota, making it difficult to interpret and compare gut microbiome data in the literature. This study proposes an analytical platform, NURECON, that can predict nutrient deficiency information in individuals by comparing their metagenomic information to a reference baseline. NURECON integrates a next-generation bacterial 16S rRNA analytical pipeline (QIIME2), metabolic pathway prediction tools (PICRUSt2 and KEGG), and a food compound database (FooDB) to enable the identification of missing nutrients and provide personalized dietary suggestions. Metagenomic information from total number of 287 healthy subjects was used to establish baseline microbial composition and metabolic profiles. The uploaded data is analyzed and compared to the baseline for nutrient deficiency assessment. Visualization results include gut microbial composition, related enzymes, pathways, and nutrient abundance. NURECON is a user-friendly online platform that provides nutritional advice to support dietitians' research or menu design.

HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery.

CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.

16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences.

Analyzing 16S ribosomal RNA (rRNA) sequences allows researchers to elucidate the prokaryotic composition of an environment. In recent years, third-generation sequencing technology has provided opportunities for researchers to perform full-length sequence analysis of bacterial 16S rRNA. RDP, SILVA, and Greengenes are the most widely used 16S rRNA databases. Many 16S rRNA classifiers have used these databases as a reference for taxonomic assignment tasks. However, some of the prokaryotic taxonomies only exist in one of the three databases. Furthermore, Greengenes and SILVA include a considerable number of taxonomies that do not have the resolution to the species level, which has limited the classifiers' performance. In order to improve the accuracy of taxonomic assignment at the species level for full-length 16S rRNA sequences, we manually curated the three databases and removed the sequences that did not have a species name. We then established a taxonomy-based integrated database by considering both taxonomies and sequences from all three 16S rRNA databases and validated it by a mock community. Results showed that our taxonomy-based integrated database had improved taxonomic resolution to the species level. The integrated database and the related datasets are available at https://github.com/yphsieh/ItgDB.

To compare the performance of prokaryotic taxonomy classifiers using curated 16S full-length rRNA sequences.

BACKGROUND: Taxonomic assignment is a vital step in the analytic pipeline of bacterial 16S ribosomal RNA (rRNA) sequencing. Over the past decade, most research in this field used next-generation sequencing technology to target V3∼V4 regions to analyze bacterial composition. However, focusing on only one or two hypervariable regions limited the taxonomic resolution to the species level. In recent years, third-generation sequencing technology has allowed researchers to easily access full-length prokaryotic 16S sequences and presented an opportunity to attain greater taxonomic depth. However, the accuracy of current taxonomic classifiers in analyzing 16S full-length sequence analysis remains unclear. OBJECTIVE: The purpose of this study is to compare the accuracy of several widely-used 16S sequence classifiers and to indicate the most suitable 16S training dataset for each classifier. METHODS: Both curated 16S full-length sequences and cross-validation datasets were used to validate the performance of seven classifiers, including QIIME2, mothur, SINTAX, SPINGO, Ribosomal Database Project (RDP), IDTAXA, and Kraken2. Different sequence training datasets, such as SILVA, Greengenes, and RDP, were used to train the classification models. RESULTS: The accuracy of each classifier to the species levels were illustrated. According to the experimental results, using RDP sequences as the training data, SINTAX and SPINGO provided the highest accuracy, and were recommended for the task of classifying prokaryotic 16S full-length rRNA sequences. CONCLUSION: The performance of the classifiers was affected by sequence training datasets. Therefore, different classifiers should use the most suitable 16S training data to improve the accuracy and taxonomy resolution in the taxonomic assignment.

EasyMAP: A user-friendly online platform for analyzing 16S ribosomal DNA sequencing data.

As next-generation sequencing technology has become more advanced, research on microbial 16S ribosomal DNA sequences has developed rapidly. Sequencing of 16S ribosomal DNA allows the composition of bacteria and archaea in a sample to be obtained and many analytical tools related to 16S ribosomal DNA sequences have been proposed; however, most do not include a user-friendly platform with a graphical user interface. Here, a comprehensive and easy-to-use online platform, Easy Microbiome Analysis Platform (EasyMAP), has been developed for analysis of 16S ribosomal DNA sequencing data. EasyMAP integrates the QIIME2, LefSe, and PICRUSt pipelines and includes temporal profiling analysis. Users can perform quality checks, taxonomy differential abundance analysis, microbial gene function prediction and longitudinal analysis with step-by-step guidance. EasyMAP is a user-friendly tool for comprehensive analysis of 16S ribosomal DNA sequencing data. The web server and documentation are freely available at http://easymap.cgm.ntu.edu.tw/.