scholarly journals MAAWf: An Integrated and Visual Tool for Microbiome Data Analyses

2020 ◽  
Author(s):  
Sibo Zhu ◽  
Tao Sun ◽  
Chengkai Zhu ◽  
Tao Qing ◽  
Yanfeng Jiang ◽  
...  
Keyword(s):  
Computing Time ◽  
Antibiotic Resistance Genes ◽  
Automated Analysis ◽  
Rrna Gene ◽  
Analysis Tool ◽  
Command Line ◽  
Protein Coding ◽  
Functional Annotations ◽  
Sequencing Technologies ◽  
Microbiome Data

Abstract BackgroundMicrobiomic research has grown in popularity in recent decades. The widespread use of next-generation sequencing technologies, including 16S rRNA gene-based and metagenomic shotgun-based methods, has produced a wealth of microbiome data. At present, most software and analysis workflows for analysis and processing of microbiomic data are command line-based, which requires considerable computing time and makes interaction difficult.ResultsTo provide a command-line free, integrated, user interface friendly and online/local deployable microbiome analysis tool, we developed Microbiome Automated Analysis Workflows (MAAWf). The MAAWf assesses taxonomy, protein-coding genes, metabolic pathways, carbohydrate-active enzymes (CAZy) and antibiotic resistance genes (ARGs) for WMS, and clusters operational taxonomic units, alpha-/beta-diversity and functional annotations for 16S. MAAWf generates similar results to currently popular tools, but the running time was much shorter. MAAWf is freely accessible and source code available at http://www.maawf.com.ConclusionsMAAWf is a visual, integrated, rapid analysis tool that enables remote and local computing of microbiome data.

scholarly journals MAAWf: A Multifunctional and Visual Tool for Microbiomic Data Analyses

2019 ◽  
Author(s):  
Sibo Zhu ◽  
Tao Sun ◽  
Chengkai Zhu ◽  
Tao Qing ◽  
Yanfeng Jiang ◽  
...  
Keyword(s):  
Computing Time ◽  
Antibiotic Resistance Genes ◽  
Automated Analysis ◽  
Rrna Gene ◽  
Analysis Tool ◽  
Command Line ◽  
16S Sequencing ◽  
Alpha And Beta Diversity ◽  
Analysis Workflow ◽  
Microbiome Data

Abstract Background: Microbiomic research has grown in popularity in recent decades. The widespread use of next-generation sequencing technologies, including 16S rRNA gene-based and metagenomic shotgun-based methods, has produced a wealth of microbiome data. At present, most software and analysis workflows for analysis and processing of microbiomic data are command line-based, which requires considerable computing time and makes interaction difficult. Results: To provide a command-line free, multifunctional, user interface friendly and online/local deployable microbiome analysis tool, we developed Microbiome Automated Analysis Workflows (MAAWf). MAAWf is composed of a whole metagenomic shotgun workflow (WMS) and a 16S Sequencing Workflow. The WMS analysis workflow assesses taxonomy, protein-coding genes, metabolic pathways, carbohydrate-active enzymes (CAZy) and antibiotic resistance genes (ARGs). The 16S ribosomal RNA (rRNA) analysis workflow counts and clusters operational taxonomic units (OTUs), estimates alpha- and beta-diversity and inter-group differences, and performs functional analysis. We also compared MAAWf with other commonly avaiable analysis tools using two public datasets. The MAAWf pipeline was established using the Ubuntu 16.04.6 LTS kernel with primary sequence files such as FASTQ format and taxonomic format such as OTU or BIOM formats. Following analysis of public 16S and WMS datasets, MAAWf obtained similar results to DIAMOND-MEGAN6, MG-RAST, DADA2 and QIIME2, but the running time was much shorter. Conclusions: MAAWf is a visual, integrated, rapid analysis tool that enables remote and local computing of microbiome data.

scholarly journals Benchmark of 16S rRNA gene amplicon sequencing using Japanese gut microbiome data from the V1–V2 and V3–V4 primer sets

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shoichiro Kameoka ◽  
Daisuke Motooka ◽  
Satoshi Watanabe ◽  
Ryuichi Kubo ◽  
Nicolas Jung ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Amplicon Sequencing ◽  
Analysis Data ◽  
Rrna Gene ◽  
Bacterial Composition ◽  
Sequencing Technologies ◽  
Primer Sets ◽  
Microbiome Data

Abstract Background 16S rRNA gene amplicon sequencing (16S analysis) is widely used to analyze microbiota with next-generation sequencing technologies. Here, we compared fecal 16S analysis data from 192 Japanese volunteers using the modified V1–V2 (V12) and the standard V3–V4 primer (V34) sets to optimize the gut microbiota analysis protocol. Results QIIME1 and QIIME2 analysis revealed a higher number of unclassified representative sequences in the V34 data than in the V12 data. The comparison of bacterial composition demonstrated that at the phylum level, Actinobacteria and Verrucomicrobia were detected at higher levels with V34 than with V12. Among these phyla, we observed higher relative compositions of Bifidobacterium and Akkermansia with V34. To estimate the actual abundance, we performed quantitative real-time polymerase chain reaction (qPCR) assays for Akkermansia and Bifidobacterium. We found that the abundance of Akkermansia as detected by qPCR was close to that in V12 data, but was markedly lower than that in V34 data. The abundance of Bifidobacterium detected by qPCR was higher than that in V12 and V34 data. Conclusions These results indicate that the bacterial composition derived from the V34 region might differ from the actual abundance for specific gut bacteria. We conclude that the use of the modified V12 primer set is more desirable in the 16S analysis of the Japanese gut microbiota.

scholarly journals Pedobacter Aquae Sp. Nov., A Multi-Drug Resistant Bacterium Isolated from Fresh Water

2021 ◽  
Author(s):  
Le Tran Tien Chau ◽  
Yong-Seok Kim ◽  
Chang-Jun Cha
Keyword(s):  
Fresh Water ◽  
Sequence Similarity ◽  
Phylogenetic Analyses ◽  
Antibiotic Resistance Genes ◽  
Multi Drug Resistance ◽  
Rrna Gene ◽  
Major Polar Lipid ◽  
Respiratory Quinone ◽  
Protein Coding ◽  
A Genome

Abstract A novel bacterial strain designated CJ43T was isolated from fresh water located in Jeongseon-gun, Gangwon-do, South Korea, displaying multi-drug resistance. The isolate was observed to be Gram-stain-negative, aerobic, orange-pigmented, and rod-shaped. Strain CJ43T grew optimally at 30 ℃ and pH 7 on R2A agar in the absence of NaCl. Phylogenetic analyses based on 16S rRNA gene sequences revealed that strain CJ43T belongs to the genus Pedobacter in the family Sphingobacteriaceae and was the most closely related to Pedobacter glucosidilyticus 1-2T (98.1% sequence similarity). The whole-genome sequencing of strain CJ43T was performed using the PacBio RS II platform, revealing a genome size of 3.9 Mb in a single contig with DNA G+C content of 34.9%. The genome included 3144 predicted protein-coding genes, as well as 55 tRNA, 9 rRNA and 3 ncRNA genes. The genome also contained 92 putative antibiotic resistance genes, reflecting its phenotypes. The average nucleotide identity and in silico digital DNA-DNA hybridization values between strain CJ43T and P. glucosidilyticus 1-2T were 88.7%, and 38.6%, respectively. The major fatty acids of strain CJ43T were iso-C15:0, iso-C17:0 3-OH, and summed feature 3 (C16:1 ω6c and/or C16:1 ω7c). Strain CJ43T contained phosphatidylethanolamine as a major polar lipid and menaquinone-7 as a sole respiratory quinone. Based on the polyphasic taxonomy data, strain CJ43T represents a novel species of the genus Pedobacter, for which the name Pedobacter aquae sp. nov. is proposed with the type strain CJ43T (= KACC 21350T = JCM 33709T).

scholarly journals Ecofriendly Synthesis of Silver Nanoparticles by Terrabacter humi sp. nov. and Their Antibacterial Application against Antibiotic-Resistant Pathogens

2020 ◽  
Vol 21 (24) ◽  
pp. 9746
Author(s):  
Shahina Akter ◽  
Sun-Young Lee ◽  
Muhammad Zubair Siddiqi ◽  
Sri Renukadevi Balusamy ◽  
Md. Ashrafudoulla ◽  
...  
Keyword(s):  
Silver Nanoparticles ◽  
Spherical Shape ◽  
Optimal Growth ◽  
Rrna Genes ◽  
Rrna Gene ◽  
Strictly Aerobic ◽  
Drug Resistant ◽  
Protein Coding ◽  
A Genome ◽  
The Fourier Transform

It is essential to develop and discover alternative eco-friendly antibacterial agents due to the emergence of multi-drug-resistant microorganisms. In this study, we isolated and characterized a novel bacterium named Terrabacter humi MAHUQ-38T, utilized for the eco-friendly synthesis of silver nanoparticles (AgNPs) and the synthesized AgNPs were used to control multi-drug-resistant microorganisms. The novel strain was Gram stain positive, strictly aerobic, milky white colored, rod shaped and non-motile. The optimal growth temperature, pH and NaCl concentration were 30 °C, 6.5 and 0%, respectively. Based on 16S rRNA gene sequence, strain MAHUQ-38T belongs to the genus Terrabacter and is most closely related to several Terrabacter type strains (98.2%–98.8%). Terrabacter humi MAHUQ-38T had a genome of 5,156,829 bp long (19 contigs) with 4555 protein-coding genes, 48 tRNA and 5 rRNA genes. The culture supernatant of strain MAHUQ-38T was used for the eco-friendly and facile synthesis of AgNPs. The transmission electron microscopy (TEM) image showed the spherical shape of AgNPs with a size of 6 to 24 nm, and the Fourier transform infrared (FTIR) analysis revealed the functional groups responsible for the synthesis of AgNPs. The synthesized AgNPs exhibited strong anti-bacterial activity against multi-drug-resistant pathogens, Escherichia coli and Pseudomonas aeruginosa. Minimal inhibitory/bactericidal concentrations against E. coli and P. aeruginosa were 6.25/50 and 12.5/50 μg/mL, respectively. The AgNPs altered the cell morphology and damaged the cell membrane of pathogens. This study encourages the use of Terrabacter humi for the ecofriendly synthesis of AgNPs to control multi-drug-resistant microorganisms.

scholarly journals Investigation of long non-coding RNAs as regulatory players of grapevine response to powdery and downy mildew infection

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Garima Bhatia ◽  
Santosh K. Upadhyay ◽  
Anuradha Upadhyay ◽  
Kashmir Singh
Keyword(s):  
Downy Mildew ◽  
Plasmopara Viticola ◽  
Defense Responses ◽  
Protein Coding ◽  
Functional Roles ◽  
Real Time Quantitative Pcr ◽  
Transcriptional Reprogramming ◽  
Sequencing Technologies ◽  
Non Coding Rnas ◽  
Fungal Phytopathogens

Abstract Background Long non-coding RNAs (lncRNAs) are regulatory transcripts of length > 200 nt. Owing to the rapidly progressing RNA-sequencing technologies, lncRNAs are emerging as considerable nodes in the plant antifungal defense networks. Therefore, we investigated their role in Vitis vinifera (grapevine) in response to obligate biotrophic fungal phytopathogens, Erysiphe necator (powdery mildew, PM) and Plasmopara viticola (downy mildew, DM), which impose huge agro-economic burden on grape-growers worldwide. Results Using computational approach based on RNA-seq data, 71 PM- and 83 DM-responsive V. vinifera lncRNAs were identified and comprehensively examined for their putative functional roles in plant defense response. V. vinifera protein coding sequences (CDS) were also profiled based on expression levels, and 1037 PM-responsive and 670 DM-responsive CDS were identified. Next, co-expression analysis-based functional annotation revealed their association with gene ontology (GO) terms for ‘response to stress’, ‘response to biotic stimulus’, ‘immune system process’, etc. Further investigation based on analysis of domains, enzyme classification, pathways enrichment, transcription factors (TFs), interactions with microRNAs (miRNAs), and real-time quantitative PCR of lncRNAs and co-expressing CDS pairs suggested their involvement in modulation of basal and specific defense responses such as: Ca2+-dependent signaling, cell wall reinforcement, reactive oxygen species metabolism, pathogenesis related proteins accumulation, phytohormonal signal transduction, and secondary metabolism. Conclusions Overall, the identified lncRNAs provide insights into the underlying intricacy of grapevine transcriptional reprogramming/post-transcriptional regulation to delay or seize the living cell-dependent pathogen growth. Therefore, in addition to defense-responsive genes such as TFs, the identified lncRNAs can be further examined and leveraged to candidates for biotechnological improvement/breeding to enhance fungal stress resistance in this susceptible fruit crop of economic and nutritional importance.

scholarly journals Citrobacter tructae sp. nov. Isolated from Kidney of Diseased Rainbow Trout (Oncorhynchus mykiss)

2021 ◽  
Vol 9 (2) ◽  
pp. 275
Author(s):  
Won Joon Jung ◽  
Hyoun Joong Kim ◽  
Sib Sankar Giri ◽  
Sang Guen Kim ◽  
Sang Wha Kim ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Oncorhynchus Mykiss ◽  
Biochemical Characterization ◽  
Antibiotic Resistance Genes ◽  
Housekeeping Genes ◽  
Rrna Gene ◽  
Bacterial Identification ◽  
Rainbow Trout Oncorhynchus Mykiss ◽  
The 16S Rrna Gene ◽  
Genome Distance

A novel Citrobacter species was isolated from the kidney of diseased rainbow trout (Oncorhynchus mykiss) reared on a trout farm. Biochemical characterization and phylogenetic analysis were performed for bacterial identification. Sequencing of the 16S rRNA gene and five housekeeping genes indicated that the strain belongs to the Citrobacter genus. However, multilocus sequence analysis, a comparison of average nucleotide identity, and genome-to-genome distance values revealed that strain SNU WT2 is distinct and forms a separate clade from other Citrobacter species. Additionally, the phenotype characteristics of the strain differed from those of other Citrobacter species. Quinone analysis indicated that the predominant isoprenoid quinone is Q-10. Furthermore, strain virulence was determined by a rainbow trout challenge trial, and the strain showed resistance to diverse antibiotics including β-lactams, quinolone, and aminoglycosides. The complete genome of strain SNU WT2 is 4,840,504 bp with a DNA G + C content of 51.94% and 106,068-bp plasmid. Genome analysis revealed that the strain carries virulence factors on its chromosome and antibiotic resistance genes on its plasmid. This strain represents a novel species in the genus Citrobacter for which the name C. tructae has been proposed, with SNU WT2 (=KCTC 72517 = JCM 33612) as the type strain.

Complete Genome Sequence of Bacteriophage EFC1 Infecting Enterococcus Faecalis From Chicken

2021 ◽  
Author(s):  
Qi Wang ◽  
Na Liu
Keyword(s):  
Enterococcus Faecalis ◽  
Virulence Genes ◽  
Large Subunit ◽  
Antibiotic Resistance Genes ◽  
Lytic Phage ◽  
High Similarity ◽  
Protein Coding ◽  
Circular Dna ◽  
Intron Structure ◽  
Comparative Phylogenetic Analysis

Abstract In response to Enterococcus faecalis infection of chicken origin, a multi host lytic phage, EFC1 was isolated and characterized the double-stranded circular DNA genome with size of 56099 bp, containing 89 predicted protein coding genes as well as 2 tRNAs involved in intron, structure, transcription, packaging, DNA replication, modification, lysis. Observation of the structure by electron microscopy and comparative phylogenetic analysis of terminase large subunit showed that the phage EFC1 belongs to a new member of Siphoviridae, which is relatively distantly related to its high similarity phages. The phage EFC1 has no relevant virulence genes and antibiotic resistance genes.

Utilizing the Microbiota and Machine Learning Algorithms to Assess Risk of Salmonella Contamination in Poultry Rinsate

2021 ◽  
Author(s):  
Hannah Bolinger ◽  
David Tran ◽  
Kenneth Harary ◽  
George C. Paoli ◽  
Giselle Guron ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Diagnostic Tools ◽  
Sequencing Data ◽  
Testing Methods ◽  
16S Sequencing ◽  
Sequencing Technologies ◽  
Microbiological Testing ◽  
Microbiome Data ◽  
Larger Sample

Traditional microbiological testing methods are slow, and many molecular-based techniques rely on culture-based enrichment to overcome low limits of detection. Recent advancements in sequencing technologies may make it possible to utilize machine learning (ML) to identify patterns in microbiome data to potentially predict the presence or absence of pathogens. In this study, 299 poultry rinsate samples from various points in the processing chain were analyzed to determine if microbiota could inform about a sample’s risk for containing Salmonella . Samples were culture confirmed as Salmonella -positive or -negative following modified USDA MLG protocols. The culture confirmation result was used as a reference to compare with 16S sequencing data. Pre-chill samples tested positive (71/82) at a higher frequency than post-chill samples (30/217) and contained greater microbial diversity. Due to their larger sample size, post-chill samples were analyzed more deeply. Analysis of variance (ANOVA) identified a significant effect of chilling on the number of genera (p<0.001), but analysis of similarities (ANOSIM) failed to provide evidence for microbial dissimilarity between pre- and post-chill samples (p=0.001, R=0.443). Various ML models were trained using post-chill samples to predict if a sample contained Salmonella based on the samples’ microbiota pre-enrichment. The optimal model was a Random Forest-based model with a performance as follows: accuracy (88%), sensitivity (85%), specificity (90%). While the algorithms described in this paper are prototypes, these risk-based algorithms demonstrate the potential and need for further studies to provide insight alongside diagnostic tests. Combining risk-based information with diagnostic tools can help poultry processors make informed decisions to help identify and prevent the spread of Salmonella . These data add to the growing body of literature exploring novel ways to utilize microbiome data for predictive food safety.

Photobacterium arenosum sp. nov., isolated from marine sediment sand

2021 ◽  
Vol 71 (10) ◽  
Author(s):  
Veeraya Weerawongwiwat ◽  
Seokmin Yoon ◽  
Jong-Hwa Kim ◽  
Jung-Hoon Yoon ◽  
Jung Sook Lee ◽  
...  
Keyword(s):  
Marine Sediment ◽  
Type Species ◽  
Rrna Gene ◽  
Polar Lipid Profile ◽  
Protein Coding ◽  
Content Type ◽  
Link Type ◽  
Bacterium Strain ◽  
The Republic ◽  
The 16S Rrna Gene

A Gram-stain-negative, aerobic, motile, short rod-shaped, catalase-negative and oxidase-positive bacterium, strain CAU 1568T, was isolated from marine sediment sand sampled at Sido Island in the Republic of Korea. The optimum conditions for growth were at 25–30 °C, at pH 6.5–8.5 and with 0–4.0 % (w/v) NaCl. Phylogenetic analysis based on the 16S rRNA gene sequence indicated that strain CAU 1568T was a member of the genus Photobacterium with high similarity to Photobacterium salinisoli JCM 30852T (97.7 %), Photobacterium halotolerans KACC 17089T (97.3 %) and Photobacterium galatheae LMG F28894T (97.3 %). The predominant cellular fatty acids were C16 : 0, summed feature 3 (C16 : 1  ω6c and/or C16 : 1  ω7c) and summed feature 8 (C18 : 1  ω7c and/or C18 : 1  ω6c), with Q-8 as the major of isoprenoid quinone. The polar lipid profile consisted of diphosphatidylglycerol, phosphatidylglycerols, phosphatidylcholine, phosphatidylethanolamine, phospholipid, two aminophospholipids and three unidentified lipids. The whole genome size of strain CAU 1568T was 4.8 Mb with 50.1 mol% G+C content; including 38 contigs and 4233 protein-coding genes. These taxonomic data support CAU 1568T as representing a novel Photobacterium species, for which the name Photobacterium arenosum sp. nov. is proposed. The type strain of this novel species is CAU 1568T (=KCTC 82404T=MCCC 1K05668T).

scholarly journals Bioconductor workflow for microbiome data analysis: from raw reads to community analyses

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1492 ◽  
Author(s):  
Ben J. Callahan ◽  
Kris Sankaran ◽  
Julia A. Fukuyama ◽  
Paul J. McMurdie ◽  
Susan P. Holmes
Keyword(s):  
High Throughput Sequencing ◽  
Linear Models ◽  
Reference Database ◽  
Rrna Gene ◽  
Microbial Composition ◽  
Nonparametric Testing ◽  
Abundance Estimates ◽  
Taxonomic Markers ◽  
Microbiome Data ◽  
Microbiome Data Analysis

High-throughput sequencing of PCR-amplified taxonomic markers (like the 16S rRNA gene) has enabled a new level of analysis of complex bacterial communities known as microbiomes. Many tools exist to quantify and compare abundance levels or microbial composition of communities in different conditions. The sequencing reads have to be denoised and assigned to the closest taxa from a reference database. Common approaches use a notion of 97% similarity and normalize the data by subsampling to equalize library sizes. In this paper, we show that statistical models allow more accurate abundance estimates. By providing a complete workflow in R, we enable the user to do sophisticated downstream statistical analyses, including both parameteric and nonparametric methods. We provide examples of using the R packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests, partial least squares and linear models as well as nonparametric testing using community networks and the ggnetwork package.

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