MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data

NFnetFu: A novel workflow for microbiome data fusion

Microbiome data analysis and its interpretation into meaningful biological insights remain very challenging for numerous reasons, perhaps most prominently, due to the need to account for multiple factors, including collinearity, sparsity (excessive zeros) and effect size, that the complex experimental workflow and subsequent downstream data analysis require. Moreover, a meaningful microbiome data analysis necessitates the development of interpretable models that incorporate inferences across available data as well as background biomedical knowledge. We developed a multimodal framework that considers sparsity (excessive zeros), lower effect size, intrinsically microbial correlations, i.e., collinearity, as well as background biomedical knowledge in the form of a cluster-infused enriched network architecture. Finally, our framework also provides a candidate taxa/Operational Taxonomic Unit (OTU) that can be targeted for future validation experiments. We have developed a tool, the term NFnetFU (Neuro Fuzzy network Fusion), that encompasses our framework and have made it freely available at https://github.com/VartikaBisht6197/NFnetFu.

Comparative Analysis of Gut Microbiota in Captive and Wild Oriental White Storks: Implications for Conservation Biology

The oriental white stork (Ciconia boyciana) is considered an endangered species based on the International Union for Conservation of Nature (IUCN) Red List. This study presents the first evidence on comparative analysis of gut microbial diversity of C. boyciana from various breeding conditions. To determine the species composition and community structure of the gut microbiota, 24 fecal samples from Tianjin Zoo and Tianjin Qilihai Wetland were characterized by sequencing 16S rRNA gene amplicons using the Illumina MiSeq platform. Firmicutes was found to be the predominant phylum. Analysis of community structure revealed significant differences in the species diversity and richness between the populations of the two breeding conditions. The greatest α-diversity was found in wild C. boyciana, while artificial breeding storks from Tianjin Zoo had the least α-diversity. Principal coordinates analysis showed that the microbial communities were different between the two studied groups. In conclusion, this study reveals the species composition and structure of the gut microbiota of oriental white storks under two breeding conditions, and our findings could contribute to the integrative conservation of this endangered bird.

Metagenomics in ophthalmology: current findings and future prospectives

Less than 1% of all microorganisms of the available environmental microbiota can be cultured with the currently available techniques. Metagenomics is a new methodology of high-throughput DNA sequencing, able to provide taxonomic and functional profiles of microbial communities without the necessity to culture microbes in the laboratory. Metagenomics opens to a ‘hypothesis-free’ approach, giving important details for future research and treatment of ocular diseases in ophthalmology, such as ocular infection and ocular surface diseases.

Reliable Biomarker discovery from Metagenomic data via RegLRSD algorithm

Background

Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms. Therefore, a robust biomarker detection algorithm that ensures the consistency of the results irrespective of the natural diversity present in the samples is needed.

Results

Toward this end, this paper proposes a novel Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm. RegLRSD models the bacterial abundance data as a superposition between a sparse matrix and a low-rank matrix, which account for the differentially and non-differentially abundant microbes, respectively. Hence, the biomarker detection problem is cast as a matrix decomposition problem. In order to yield more consistent and solid biological conclusions, RegLRSD incorporates the prior knowledge that the irrelevant microbes do not exhibit significant variation between samples belonging to different phenotypes. Moreover, an efficient algorithm to extract the sparse matrix is proposed. Comprehensive comparisons of RegLRSD with the state-of-the-art algorithms on three realistic datasets are presented. The obtained results demonstrate that RegLRSD consistently outperforms the other algorithms in terms of reproducibility performance and provides a marker list with high classification accuracy.

Conclusions

The proposed RegLRSD algorithm for biomarker detection provides high reproducibility and classification accuracy performance regardless of the dataset complexity and the number of selected biomarkers. This renders RegLRSD as a reliable and powerful tool for identifying potential metagenomic biomarkers.