Python microbiome

MOTIVATION: MetaCerberus is a massively parallel, fast, low memory, scalable annotation tool for inference gene function across genomes to metacommunities. MetaCerberus provides an elusive HMM/HMMER-based tool at a rapid scale with low memory. It offers scalable gene elucidation to major public databases, including KEGG (KO), COGs, CAZy, FOAM, and specific databases for viruses, including VOGs and PHROGs, from single genomes to metacommunities. RESULTS: MetaCerberus is 1...

BACKGROUND: Increasing evidence suggests a close association between the intestinal microbiome and the respiratory system, drawing attention to studying the gut-lung axis. This research employs bibliometric methods to conduct a visual analysis of literature in the field of intestinal microbiota and lung diseases over the past two decades. It offers scientific foundations for research directions and critical issues in this field. METHODS: We retrieved all articles on intestinal microbiota and lung diseases from the SCI-Expanded of WoSCC on October 25, 2023...

MOTIVATION: DNA barcoding has become a powerful tool for assessing the fitness of strains in a variety of studies, including random transposon mutagenesis screens, attenuation of site-directed mutants, and population dynamics of isogenic strain pools. However, the statistical analysis, visualization and contextualization of the data resulting from such experiments can be complex and require bioinformatic skills. RESULTS: Here, we developed mBARq, a user-friendly tool designed to simplify these steps for diverse experimental setups...

CAZymes or carbohydrate-active enzymes are critically important for human gut health, lignocellulose degradation, global carbon recycling, soil health, and plant disease. We developed dbCAN as a web server in 2012 and actively maintain it for automated CAZyme annotation. Considering data privacy and scalability, we provide run_dbcan as a standalone software package since 2018 to allow users perform more secure and scalable CAZyme annotation on their local servers. Here, we offer a comprehensive computational protocol on automated CAZyme annotation of microbiome sequencing data, covering everything from short read pre-processing to data visualization of CAZyme and glycan substrate occurrence and abundance in multiple samples...

Viruses are increasingly being recognized as important components of human and environmental microbiomes. However, viruses in microbiomes remain difficult to study because of the difficulty in culturing them and the lack of sufficient model systems. As a result, computational methods for identifying and analyzing uncultivated viral genomes from metagenomes have attracted significant attention. Such bioinformatics approaches facilitate the screening of viruses from enormous sequencing datasets originating from various environments...

BACKGROUND: Phylogenomic analysis has become an inseparable part of studies of bacterial diversity and evolution, and many different bacterial core genes have been collated and used for phylogenomic tree reconstruction. However, these genes have been selected based on their presence and single-copy ratio in all bacterial genomes, leaving out the gene's 'phylogenetic fidelity' unexamined. RESULTS: From 30,522 complete genomes covering 11,262 species, we examined 148 bacterial core genes that have been previously used for phylogenomic analysis...

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, nonexpert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. RESULTS: We have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems...

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The human oral and nasal microbiota contains approximately 770 cultivable bacterial species. More than 2000 genome sequences of these bacteria can be found in the expanded Human Oral Microbiome Database (eHOMD). We developed HOMDscrape, a freely available Python software tool to programmatically retrieve and process amino acid sequences and sequence identifiers from BLAST results acquired from the eHOMD website. Using the data obtained through HOMDscrape, the phylogeny of proteins involved in bacterial flagellar motility, Type 4 pilus driven twitching motility, and Type 9 Secretion system (T9SS) driven gliding motility was constructed...

Supervised learning, such as regression and classification, is an essential tool for analyzing modern high-throughput sequencing data, for example in microbiome research. However, due to the compositionality and sparsity, existing techniques are often inadequate. Either they rely on extensions of the linear log-contrast model (which adjust for compositionality but cannot account for complex signals or sparsity) or they are based on black-box machine learning methods (which may capture useful signals, but lack interpretability due to the compositionality)...

The infamous "master manipulators"-intracellular bacteria of the genus Wolbachia -infect a broad range of phylogenetically diverse invertebrate hosts in terrestrial ecosystems. Wolbachia has an important impact on the ecology and evolution of their host with documented effects including induced parthenogenesis, male killing, feminization, and cytoplasmic incompatibility. Nonetheless, data on Wolbachia infections in non-terrestrial invertebrates are scarce. Sampling bias and methodological limitations are some of the reasons limiting the detection of these bacteria in aquatic organisms...

Experiments involving metagenomics data are become increasingly commonplace. Processing such data requires a unique set of considerations. Quality control of metagenomics data is critical to extracting pertinent insights. In this chapter, we outline some considerations in terms of study design and other confounding factors that can often only be realized at the point of data analysis.In this chapter, we outline some basic principles of quality control in metagenomics, including overall reproducibility and some good practices to follow...

AIM: To analyze and compare the root canal microbiome present in root-filled teeth of two different geographical populations, and to study their functional potential using next-a generation sequencing approach. METHODOLOGY: Sequencing data obtained from surgical specimens from previously treated teeth with periapical bone loss from Spain and USA were included in the study. Taxa were classified using SILVA v.138 database. Differences in genera abundances among the 10 most abundant genera were evaluated using a Kruskal-Wallis test...

INTRODUCTION: There remains a lack of knowledge regarding the effects of the intratumor microbiome on the tumor immune milieu. We aimed to investigate whether intratumoral bacterial RNA sequence abundance in gastric and esophageal cancers is associated with T-cell infiltrate features. METHODS: We assessed cases representing the stomach adenocarcinoma (STAD) and esophageal cancer (ESCA) databases of The Cancer Genome Atlas. RNA-seq data estimating intratumoral bacterial abundance was obtained from publicly available sources...

BACKGROUND: Construction of kinship matrices among individuals is an important step for both association studies and prediction studies based on different levels of omic data. Methods for constructing kinship matrices are becoming diverse and different methods have their specific appropriate scenes. However, software that can comprehensively calculate kinship matrices for a variety of scenarios is still in an urgent demand. RESULTS: In this study, we developed an efficient and user-friendly python module, PyAGH, that can accomplish (1) conventional additive kinship matrces construction based on pedigree, genotypes, abundance data from transcriptome or microbiome; (2) genomic kinship matrices construction in combined population; (3) dominant and epistatic effects kinship matrices construction; (4) pedigree selection, tracing, detection and visualization; (5) visualization of cluster, heatmap and PCA analysis based on kinship matrices...

We present here POSMM (pronounced 'Possum'), Python-Optimized Standard Markov Model classifier, which is a new incarnation of the Markov model approach to metagenomic sequence analysis. Built on the top of a rapid Markov model based classification algorithm SMM, POSMM reintroduces high sensitivity associated with alignment-free taxonomic classifiers to probe whole genome or metagenome datasets of increasingly prohibitive sizes. Logistic regression models generated and optimized using the Python sklearn library, transform Markov model probabilities to scores suitable for thresholding...

MOTIVATION: The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage...

UNLABELLED: Viruses are increasingly being recognized as important components of human and environmental microbiomes. However, viruses in microbiomes remain difficult to study because of difficulty in culturing them and the lack of sufficient model systems. As a result, computational methods for identifying and analyzing uncultivated viral genomes from metagenomes have attracted significant attention. Such bioinformatics approaches facilitate screening of viruses from enormous sequencing datasets originating from various environments...

MOTIVATION: The gut microbiome changes rapidly under the influence of different factors such as age, dietary changes, or medications to name just a few. To analyze and understand such changes we present a microbiome analysis toolbox. We implemented several methods for analysis and exploration to provide interactive visualizations for easy comprehension and reporting of longitudinal microbiome data. RESULTS: Based on the abundance of microbiome features such as taxa as well as functional capacity modules, and with the corresponding metadata per sample, the toolbox includes methods for 1) data analysis and exploration, 2) data preparation including dataset-specific preprocessing and transformation, 3) best feature selection for log-ratio denominators, 4) two-group analysis, 5) microbiome trajectory prediction with feature importance over time, 6) spline and linear regression statistical analysis for testing universality across different groups and differentiation of two trajectories, 7) longitudinal anomaly detection on the microbiome trajectory, and 8) simulated intervention to return anomaly back to a reference trajectory...

The human upper respiratory tract is the reservoir of a diverse community of commensals and potential pathogens (pathobionts), including Streptococcus pneumoniae (pneumococcus), Haemophilus influenzae , Moraxella catarrhalis , and Staphylococcus aureus , which occasionally turn into pathogens causing infectious diseases, while the contribution of many nasal microorganisms to human health remains undiscovered. To better understand the composition of the nasal microbiome community, we create a workflow of the community model, which mimics the human nasal environment...