Workshop
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Similar Datasets
Project description:high throughput sequencing of the metagenome of mexican children Overall design: RNA seq from fecal samples of Mexican children
2020-03-16 | GSE143207 | GEO
Project description:BACKGROUND:Microorganisms are important occupants of many different environments. Identifying the composition of microbes and estimating their abundance promote understanding of interactions of microbes in environmental samples. To understand their environments more deeply, the composition of microorganisms in environmental samples has been studied using metagenomes, which are the collections of genomes of the microorganisms. Although many tools have been developed for taxonomy analysis based on different algorithms, variability of analysis outputs of existing tools from the same input metagenome datasets is the main obstacle for many researchers in this field. RESULTS:Here, we present a novel meta-analysis tool for metagenome taxonomy analysis, called TAMA, by intelligently integrating outputs from three different taxonomy analysis tools. Using an integrated reference database, TAMA performs taxonomy assignment for input metagenome reads based on a meta-score by integrating scores of taxonomy assignment from different taxonomy classification tools. TAMA outperformed existing tools when evaluated using various benchmark datasets. It was also successfully applied to obtain relative species abundance profiles and difference in composition of microorganisms in two types of cheese metagenome and human gut metagenome. CONCLUSION:TAMA can be easily installed and used for metagenome read classification and the prediction of relative species abundance from multiple numbers and types of metagenome read samples. TAMA can be used to more accurately uncover the composition of microorganisms in metagenome samples collected from various environments, especially when the use of a single taxonomy analysis tool is unreliable. TAMA is an open source tool, and can be downloaded at https://github.com/jkimlab/TAMA.
2020-01-01 | S-EPMC7218625 | BioStudies
Project description:Bacterial diversity and archaeal diversity in metagenome of the Lonar soda lake sediment were assessed by bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). Metagenome comprised 5093 sequences with 2,531,282 bp and 53 ± 2% G + C content. Metagenome sequence data are available at NCBI under the Bioproject database with accession no. PRJNA218849. Metagenome sequence represented the presence of 83.1% bacterial and 10.5% archaeal origin. A total of 14 different bacteria demonstrating 57 species were recorded with dominating species like Coxiella burnetii (17%), Fibrobacter intestinalis (12%) and Candidatus Cloacamonas acidaminovorans (11%). Occurrence of two archaeal phyla representing 24 species, among them Methanosaeta harundinacea (35%), Methanoculleus chikugoensis (12%) and Methanolinea tarda (11%) were dominating species. Significant presence of 11% sequences as an unclassified indicated the possibilities for unknown novel prokaryotes from the metagenome.
2015-01-01 | S-EPMC4535755 | BioStudies
Project description:BACKGROUND:Despite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition. In this study, we evaluated the performance of three commonly used metagenome prediction tools (PICRUSt, PICRUSt2, and Tax4Fun) by comparing the significance of the differential abundance of predicted functional gene profiles to those from shotgun metagenome sequencing across different environments. RESULTS:We selected 7 datasets of human, non-human animal, and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between predicted gene compositions and gene relative abundance measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the abundance of genes were permuted across samples. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of metagenome prediction tools. As an alternative, we compared the performance of genes predicted with PICRUSt, PICRUSt2, and Tax4Fun to sequenced metagenome genes in inference models associated with metadata within each dataset. With this approach, we found reasonable performance for human datasets, with the metagenome prediction tools performing better for inference on genes related to "housekeeping" functions. However, their performance degraded sharply outside of human datasets when used for inference. CONCLUSION:We conclude that the utility of PICRUSt, PICRUSt2, and Tax4Fun for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted. Video abstract.
2020-01-01 | S-EPMC7118876 | BioStudies
Project description:We report Metagenome from the saline desert soil sample of Little Rann of Kutch, Gujarat State, India. Metagenome consisted of 633,760 sequences with size 141,307,202 bp and 56% G + C content. Metagenome sequence data are available at EBI under EBI Metagenomics database with accession no. ERP005612. Community metagenomics revealed total 1802 species belonged to 43 different phyla with dominating Marinobacter (48.7%) and Halobacterium (4.6%) genus in bacterial and archaeal domain respectively. Remarkably, 18.2% sequences in a poorly characterized group and 4% gene for various stress responses along with versatile presence of commercial enzyme were evident in a functional metagenome analysis.
2015-01-01 | S-EPMC4535752 | BioStudies