Parallel‐Meta Suite: Interactive and rapid microbiome data analysis on multiple platforms

Chen, Yuzhu; Li, Jian; Zhang, Yufeng; Zhang, Mingqian; Sun, Zheng; Jing, Gongchao; Huang, Shi; Su, Xiaoquan

doi:10.1002/imt2.1

Cited by 32 publications

(20 citation statements)

References 39 publications

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“…Microbiome datasets used in this work and their information are listed in Table 1 . The original sequences were preprocessed by Parallel-Meta Suite ( Chen et al 2022 ), including chimera removal, pair-end merging, and ASV denoising. OTUs were then picked against Greengenes database (v13-8) with sequence similarity of 0.99, and relative abundance of microbes was normalized and corrected by 16S rRNA gene copy numbers.…”

Section: Methodsmentioning

confidence: 99%

Flex Meta-Storms elucidates the microbiome local beta-diversity under specific phenotypes

Zhang

Chen

et al. 2023

Bioinformatics

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Motivation Beta-diversity quantitatively measures the difference among microbial communities, thus enlightening the association between microbiome composition and environment properties or host phenotypes. The beta-diversity analysis mainly relies on distances among microbiomes that are calculated by all microbial features. However, in some cases, only a small fraction of members in a community plays crucial roles. Such tiny proportion is insufficient to alter the overall distance, which is always missed by end-to-end comparison. On the other hand, beta-diversity pattern can also be interfered due to the data sparsity when only focusing on non-abundant microbes. Results Here we develop Flex Meta-Storms (FMS) distance algorithm that implements the “local alignment” of microbiomes for the first time. Using a flexible extraction that considers the weighted phylogenetic and functional relations of microbes, FMS produces a normalized phylogenetic distance among members of interest for microbiome pairs. We demonstrated the advantage of FMS in detecting the subtle variations of microbiomes among different states using artificial and real datasets, which were neglected by regular distance metrics. Therefore, FMS effectively discriminates microbiomes with higher sensitivity and flexibility, thus contributing to in-depth comprehension of microbe-host interactions, as well as promoting the utilization of microbiome data such as disease screening and prediction. Availability FMS is implemented in C ++ and the source code is released at https://github.com/qdu-bioinfo/flex-meta-storms. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

Flex Meta-Storms elucidates the microbiome local beta-diversity under specific phenotypes

Zhang

Chen

et al. 2023

Bioinformatics

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“…The MOTHUR (Schloss et al, 2009 ) software was used to merge all 16S rRNA gene data. To avoid sequencing inaccuracy, the Parallel‐Meta Suite (PMS; V 3.7; Chen et al, 2022 ) was used to denoise (Callahan et al, 2017 ) and remove chimeras (Edgar et al, 2011 ). To eliminate the effect of using different sequencing intervals and sequencing depths of 16S rRNA data, PMS was used to cluster the sequences into operational taxonomic units (OTUs, with the conventional criterion of 97% sequence identity) and annotate the taxonomy (GreenGenes V13‐8) of each species, and the relative microbiome abundance table of each sample was obtained.…”

Section: Methodsmentioning

confidence: 99%

Convergent evolution of the gut microbiome in marine carnivores

Wang

Shang

et al. 2022

Ecology and Evolution

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The gut microbiome can help the host adapt to a variety of environments and is affected by many factors. Marine carnivores have unique habitats in extreme environments. The question of whether marine habitats surpass phylogeny to drive the convergent evolution of the gut microbiome in marine carnivores remains unanswered. In the present study, we compared the gut microbiomes of 16 species from different habitats. Principal component analysis (PCA) and principal coordinate analysis (PCoA) separated three groups according to their gut microbiomes: marine carnivores, terrestrial carnivores, and terrestrial herbivores. The alpha diversity and niche breadth of the gut microbiome of marine carnivores were lower than those of the gut microbiome of terrestrial carnivores and terrestrial herbivores. The gut microbiome of marine carnivores harbored many marine microbiotas, including those belonging to the phyla Planctomycetes, Cyanobacteria, and Proteobacteria, and the genus Peptoclostridium . Collectively, these results revealed that marine habitats drive the convergent evolution of the gut microbiome of marine carnivores. This study provides a new perspective on the adaptive evolution of marine carnivores.

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“…Huang et al [29] conducted a meta‐analysis of gut microbiota in a population with an Altitude diet. Innovative methods are the propeller to promote the development of science. To make user analysis and visualization easier, Chen et al [30] developed an integrative microbiome analysis pipeline. Michel‐Mata et al [31] introduced a deep learning algorithm to predict microbiome composition. Reviews are also essential for peers to learn and begin their studies readily.…”

Section: Figurementioning

confidence: 99%

“…-Innovative methods are the propeller to promote the development of science. To make user analysis and visualization easier, Chen et al [30] developed an integrative microbiome analysis pipeline. Michel-Mata et al [31] introduced a deep learning algorithm to predict microbiome composition.…”

mentioning

confidence: 99%