Dataset Information

TAMA: improved metagenomic sequence classification through meta-analysis.

ABSTRACT: 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.

SUBMITTER: Sim M

PROVIDER: S-EPMC7218625 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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TAMA: improved metagenomic sequence classification through meta-analysis.

Sim Mikang M Lee Jongin J Lee Daehwan D Kwon Daehong D Kim Jaebum J

BMC bioinformatics 20200512 1

<h4>Background</h4>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 ...[more]

PMID: 32397982

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Project description:In recent years several different fields, such as ecology, medicine and microbiology, have experienced an unprecedented development due to the possibility of direct sequencing of microbioimic samples. Among problems that researchers in the field have to deal with, taxonomic classification of metagenomic reads is one of the most challenging. State of the art methods classify single reads with almost 100% precision. However, very often, the performance in terms of recall falls at about 50%. As a consequence, state-of-the-art methods are indeed capable of correctly classify only half of the reads in the sample. How to achieve better performances in terms of overall quality of classification remains a largely unsolved problem.In this paper we propose a method for metagenomics CLassification Improvement with Overlapping Reads (CLIOR), that exploits the information carried by the overlapping reads graph of the input read dataset to improve recall, f-measure, and the estimated abundance of species. In this work, we applied CLIOR on top of the classification produced by the classifier Clark-l. Experiments on simulated and synthetic metagenomes show that CLIOR can lead to substantial improvement of the recall rate, sometimes doubling it. On average, on simulated datasets, the increase of recall is paired with an higher precision too, while on synthetic datasets it comes at expenses of a small loss of precision. On experiments on real metagenomes CLIOR is able to assign many more reads while keeping the abundance ratios in line with previous studies.Our results showed that with CLIOR is possible to boost the recall of a state-of-the-art metagenomic classifier by inferring and/or correcting the assignment of reads with missing or erroneous labeling. CLIOR is not restricted to the reads classification algorithm used in our experiments, but it may be applied to other methods too. Finally, CLIOR does not need large computational resources, and it can be run on a laptop.

Project description:Bovine coronavirus (BCoV) is zoonotically transmissible among species, since BCoV-like viruses have been detected in wild ruminants and humans. BCoV causing enteric and respiratory disease is widespread in cattle farms worldwide; however, limited information is available regarding the molecular characterization of BCoV because of its large genome size, despite its significant economic impact. This study aimed to better understand the genomic characterization and evolutionary dynamics of BCoV via comparative sequence and phylogenetic analyses through whole genome sequence analysis using 67 BCoV isolates collected throughout Japan from 2006 to 2017. On comparing the genomic sequences of the 67 BCoVs, genetic variations were detected in 5 of 10 open reading frames (ORFs) in the BCoV genome. Phylogenetic analysis using whole genomes from the 67 Japanese BCoV isolates in addition to those from 16 reference BCoV strains, revealed the existence of two major genotypes (classical and US wild ruminant genotypes). All Japanese BCoV isolates originated from the US wild ruminant genotype, and they tended to form the same clusters based on the year and farm of collection, not the disease type. Phylogenetic trees on hemagglutinin-esterase protein (HE), spike glycoprotein (S), nucleocapsid protein (N) genes and ORF1 revealed clusters similar to that on whole genome, suggesting that the evolution of BCoVs may be closely associated with variations in these genes. Furthermore, phylogenetic analysis of BCoV S genes including those of European and Asian BCoVs and human enteric coronavirus along with the Japanese BCoVs revealed that BCoVs differentiated into two major types (European and American types). Moreover, the European and American types were divided into eleven and three genotypes, respectively. Our analysis also demonstrated that BCoVs with different genotypes periodically emerged and predominantly circulated within the country. These findings provide useful information to elucidate the detailed molecular characterization of BCoVs, which have spread worldwide. Further genomic analyses of BCoV are essential to deepen the understanding of the evolution of this virus.

Project description:The present dataset ("dataset 3") is a subset of a large metastudy on AML classfication. It contains normalized gene expression values of 1181 samples. In total, three datasets were generated, each containing data of a different platforms: dataset 1 (Affymetrix HG-U133 A microarrays), dataset 2 (Affymetrix HG-U133 2.0 microarrays) and dataset 3 (RNA-seq). Dataset 3 was generated using the following strategy: All data sets published in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) on 20 September 2017 were reviewed for inclusion in the present study. Basic criteria for inclusion were the cell type under study (human peripheral blood mononuclear cells (PMBCs) and/or bone marrow samples) as well as the species (Homo sapiens). Furthermore, GEO SuperSeries were excluded to avoid duplicated samples. We filtered the datasets for data generated with high-throughput RNA sequencing (RNA-seq) and excluded studies with very small sample sizes (< 10 samples). We then applied a disease-specific search, in which we filtered for acute myeloid leukemia, other leukemia and healthy or non-leukemia-related samples. The results of this search strategy were then internally reviewed and data were excluded based on the following criteria: (i) exclusion of duplicated samples, (ii) exclusion of studies that sorted single cell types (e.g. T cells or B cells) prior to gene expression profiling, (iii) exclusion of studies with inaccessible data. Other than that, no studies were excluded from our analysis. In total, the datasets contained samples from the following GSE Series: GSE63085, GSE32874, GSE58335, GSE86884, GSE63703, GSE63646, GSE63816, GSE72790, GSE81259, GSE85712, GSE45735, GSE64655, GSE87186, GSE49642, GSE52656, GSE62190, GSE66917, GSE67039, GSE61162, GSE67184, GSE49601, GSE78785, GSE79970. All raw data files were downloaded from GEO. Transcript abundances were calculated using kallisto version 0.43.0 and all data was normalized with the R package DESeq2 (R version R-3.2.4, DESeq2 version 1.12.4) with standard parameters. Genome build hg38 was used for read alignment. No filtering of low-expressed genes was performed.

Dataset Information

TAMA: improved metagenomic sequence classification through meta-analysis.

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TAMA: improved metagenomic sequence classification through meta-analysis.

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