FlexTaxD: flexible modification of taxonomy databases for improved sequence classification

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

Co-Occurrence of Francisella, Spotted Fever Group Rickettsia, and Midichloria in Avian-Associated Hyalomma rufipes

The migratory behavior of wild birds contributes to the geographical spread of ticks and their microorganisms. In this study, we aimed to investigate the dispersal and co-occurrence of Francisella and spotted fever group Rickettsia (SFGR) in ticks infesting birds migrating northward in the African-Western Palaearctic region (AWPR). Birds were trapped with mist nests across the Mediterranean basin during the 2014 and 2015 spring migration. In total, 575 ticks were collected from 244 birds. We screened the ticks for the species Francisella tularensis, the genus Francisella, and SFGR by microfluidic real-time PCR. Confirmatory analyses and metagenomic sequencing were performed on tick samples that putatively tested positive for F. tularensis during initial screenings. Hyalomma rufipes was the most common tick species and had a high prevalence of Francisella, including co-occurrence of Francisella and SFGR. Metagenomic analysis of total DNA extracted from two H. rufipes confirmed the presence of Francisella, Rickettsia, and Midichloria. Average nucleotide identity and phylogenetic inference indicated the highest identity of the metagenome-assembled genomes to a Francisella-like endosymbiont (FLE), Rickettsia aeschlimannii, and Midichloria mitochondrii. The results of this study suggest that (i) FLE- and SFGR-containing ticks are dispersed by northbound migratory birds in the AWPR, (ii) H. rufipes likely is not involved in transmission of F. tularensis in the AWPR, and (iii) a dual endosymbiosis of FLEs and Midichloria may support some of the nutritional requirements of H. rufipes.

Comprehensive mouse microbiota genome catalog reveals major difference to its human counterpart

Mouse is the most used model for studying the impact of microbiota on its host, but the repertoire of species from the mouse gut microbiome remains largely unknown. Accordingly, the similarity between human and mouse microbiomes at a low taxonomic level is not clear. We construct a comprehensive mouse microbiota genome (CMMG) catalog by assembling all currently available mouse gut metagenomes and combining them with published reference and metagenome-assembled genomes. The 41’798 genomes cluster into 1’573 species, of which 78.1% are uncultured, and we discovered 226 new genera, seven new families, and one new order. CMMG enables an unprecedented coverage of the mouse gut microbiome exceeding 86%, increases the mapping rate over four-fold, and allows functional microbiota analyses of human and mouse linking them to the driver species. Comparing CMMG to microbiota from the unified human gastrointestinal genomes shows an overlap of 62% at the genus but only 10% at the species level, demonstrating that human and mouse gut microbiota are largely distinct. CMMG contains the most comprehensive collection of consistently functionally annotated species of the mouse and human microbiome to date, setting the ground for analysis of new and reanalysis of existing datasets at an unprecedented depth.

The microbiome plays an indispensable role in our health. Metagenomics enables valuable insights into the composition and functional potential of microbial populations. The analysis of metagenomic data is complex and depends on the availability of reference genomes. The mouse is the most used model for studying the impact of microbiota on its host. However, the microbial species living in the mouse gut remain poorly characterized. We created a comprehensive catalog of all bacterial species commonly living in the gut of laboratory mice by analyzing all publicly available metagenomes from the mouse gut. We collected almost 42 thousand bacterial genomes from 1’573 species, of which 78.1% are uncultured. Our catalog effectively answers the need for a genome reference for this microbiome and allows efficient analysis of mouse gut metagenomes down to the species level. We discovered that mice and humans harbor a largely distinct set of species in their gastrointestinal tracts, a hereto unfeasible analysis.