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metagenomics</title> <meta name="description" content="Search results for: metagenomics"> <meta name="keywords" content="metagenomics"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="metagenomics"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 33</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: metagenomics</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">33</span> Metagenomics Features of The Gut Microbiota in Metabolic Syndrome</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anna%20D.%20Kotrova">Anna D. Kotrova</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandr%20N.%20Shishkin"> Alexandr N. Shishkin</a>, <a href="https://publications.waset.org/abstracts/search?q=Elena%20I.%20Ermolenko"> Elena I. Ermolenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim. To study the quantitative and qualitative colon bacteria ratio from patients with metabolic syndrome. Materials and methods. Fecal samples from patients of 2 groups were identified and analyzed: the first group was formed by patients with metabolic syndrome, the second one - by healthy individuals. The metagenomics method was used with the analysis of 16S rRNA gene sequences. The libraries of the variable sites (V3 and V4) gene 16S RNA were analyzed using the MiSeq device (Illumina). To prepare the libraries was used the standard recommended by Illumina, a method based on two rounds of PCR. Results. At the phylum level in the microbiota of patients with metabolic syndrome compared to healthy individuals, the proportion of Tenericutes was reduced, the proportion of Actinobacteria was increased. At the genus level, in the group with metabolic syndrome, relative to the second group was increased the proportion of Lachnospira. Conclusion. Changes in the colon bacteria ratio in the gut microbiota of patients with metabolic syndrome were found both at the type and the genus level. In the metabolic syndrome group, there is a decrease in the proportion of bacteria that do not have a cell wall. To confirm the revealed microbiota features in patients with metabolic syndrome, further study with a larger number of samples is required. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gut%20microbiota" title="gut microbiota">gut microbiota</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolic%20syndrome" title=" metabolic syndrome"> metabolic syndrome</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=tenericutes" title=" tenericutes"> tenericutes</a> </p> <a href="https://publications.waset.org/abstracts/130125/metagenomics-features-of-the-gut-microbiota-in-metabolic-syndrome" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130125.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">32</span> Harnessing Deep-Level Metagenomics to Explore the Three Dynamic One Health Areas: Healthcare, Domiciliary and Veterinary</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christina%20Killian">Christina Killian</a>, <a href="https://publications.waset.org/abstracts/search?q=Katie%20Wall"> Katie Wall</a>, <a href="https://publications.waset.org/abstracts/search?q=S%C3%A9amus%20Fanning"> Séamus Fanning</a>, <a href="https://publications.waset.org/abstracts/search?q=Guerrino%20Macori"> Guerrino Macori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep-level metagenomics offers a useful technical approach to explore the three dynamic One Health axes: healthcare, domiciliary and veterinary. There is currently limited understanding of the composition of complex biofilms, natural abundance of AMR genes and gene transfer occurrence in these ecological niches. By using a newly established small-scale complex biofilm model, COMBAT has the potential to provide new information on microbial diversity, antimicrobial resistance (AMR)-encoding gene abundance, and their transfer in complex biofilms of importance to these three One Health axes. Shotgun metagenomics has been used to sample the genomes of all microbes comprising the complex communities found in each biofilm source. A comparative analysis between untreated and biocide-treated biofilms is described. The basic steps include the purification of genomic DNA, followed by library preparation, sequencing, and finally, data analysis. The use of long-read sequencing facilitates the completion of metagenome-assembled genomes (MAG). Samples were sequenced using a PromethION platform, and following quality checks, binning methods, and bespoke bioinformatics pipelines, we describe the recovery of individual MAGs to identify mobile gene elements (MGE) and the corresponding AMR genotypes that map to these structures. High-throughput sequencing strategies have been deployed to characterize these communities. Accurately defining the profiles of these niches is an essential step towards elucidating the impact of the microbiota on each niche biofilm environment and their evolution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=COMBAT" title="COMBAT">COMBAT</a>, <a href="https://publications.waset.org/abstracts/search?q=biofilm" title=" biofilm"> biofilm</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=high-throughput%20sequencing" title=" high-throughput sequencing"> high-throughput sequencing</a> </p> <a href="https://publications.waset.org/abstracts/182703/harnessing-deep-level-metagenomics-to-explore-the-three-dynamic-one-health-areas-healthcare-domiciliary-and-veterinary" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182703.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">56</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31</span> Metagenomics Profile during the Bioremediation of Fischer-Tropsch Derived Short-Chain Alcohols and Volatile Fatty Acids Using a Moving Bed Biofilm Reactor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mabtho%20Moreroa-Monyelo">Mabtho Moreroa-Monyelo</a>, <a href="https://publications.waset.org/abstracts/search?q=Grace%20Ijoma"> Grace Ijoma</a>, <a href="https://publications.waset.org/abstracts/search?q=Rosina%20Nkuna"> Rosina Nkuna</a>, <a href="https://publications.waset.org/abstracts/search?q=Tonderayi%20Matambo"> Tonderayi Matambo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A moving bed biofilm reactor (MBBR) was used for the bioremediation of high strength chemical oxygen demand (COD) Fisher-Tropsch (FT) wastewater. The aerobic MBBR system was operated over 60 days. For metagenomics profile assessment of the targeted 16S sequence of bacteria involved in the bioremediation of the chemical compounds, sludge samples were collected every second day of operation. Parameters such as pH and COD were measured daily to compare the system efficiency as the changedin microbial diversity progressed. The study revealed that pH was a contributing factor to microbial diversity, which further affected the efficiency of the MBBR system. The highest COD removal rate of 86.4% was achieved at pH 8.3. It was observed that when there was more, A higher bacterial diversity led to an improvement in the reduction of COD. Furthermore, an OTUof 4530 was obtained, which were divided into 12 phyla, 27 classes, 44 orders, 74 families, and 138 genera across all sludge samples from the MBBR. A determination of the relative abundance of microorganisms at phyla level indicates that the most abundant phylum on day it was Firmicutes (50%); thereafter, the most abundant phylum changed toProteobacteria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biodegradation" title="biodegradation">biodegradation</a>, <a href="https://publications.waset.org/abstracts/search?q=fischer-tropsch%20wastewater" title=" fischer-tropsch wastewater"> fischer-tropsch wastewater</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20bed%20biofilm%20reactor" title=" moving bed biofilm reactor"> moving bed biofilm reactor</a> </p> <a href="https://publications.waset.org/abstracts/150542/metagenomics-profile-during-the-bioremediation-of-fischer-tropsch-derived-short-chain-alcohols-and-volatile-fatty-acids-using-a-moving-bed-biofilm-reactor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150542.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30</span> Metagenomics Composition During and After Wet Deposition and the Presence of Airborne Microplastics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yee%20Hui%20Lim">Yee Hui Lim</a>, <a href="https://publications.waset.org/abstracts/search?q=Elena%20Gusareva"> Elena Gusareva</a>, <a href="https://publications.waset.org/abstracts/search?q=Irvan%20Luhung"> Irvan Luhung</a>, <a href="https://publications.waset.org/abstracts/search?q=Yulia%20Frank"> Yulia Frank</a>, <a href="https://publications.waset.org/abstracts/search?q=Stephan%20Christoph%20Schuster"> Stephan Christoph Schuster</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Environmental pollution from microplastics (MPs) is an emerging concern worldwide. While the presence of microplastics has been well established in the marine and terrestrial environments, the prevalence of microplastics in the atmosphere is still poorly understood. Wet depositions such as rain or snow scavenge impurities from the atmosphere as it falls to the ground. These wet depositions serve as a useful tool in the removal of airborne particles that are suspended in the air. Therefore, the aim of this study is to investigate the presence of atmospheric microplastics and fibres through the analysis of air, rainwater and snow samples. Air samples were collected with filter-based air samplers from outdoor locations in Singapore. The sampling campaigns were conducted during and after each rain event. Rainwater samples from Singapore and Siberia were collected as well. Snow samples were also collected from Siberia as part of the ongoing study. Genomic DNA was then extracted from the samples and sequenced with shotgun metagenomics approach. qPCR analysis was conducted to quantify the total bacteria and fungi in the air, rainwater and snow samples. The results compared the bioaerosol profiles of all the samples. To observe the presence of microplastics, scanning electron microscope (SEM) was used. From the preliminary results, microplastics were detected. It can be concluded that there is a significant amount of atmospheric microplastics present, and its occurrence should be investigated in greater detail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=atmospheric%20microplastics" title="atmospheric microplastics">atmospheric microplastics</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=scanning%20electron%20microscope" title=" scanning electron microscope"> scanning electron microscope</a>, <a href="https://publications.waset.org/abstracts/search?q=wet%20deposition" title=" wet deposition"> wet deposition</a> </p> <a href="https://publications.waset.org/abstracts/153093/metagenomics-composition-during-and-after-wet-deposition-and-the-presence-of-airborne-microplastics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153093.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29</span> Ribotaxa: Combined Approaches for Taxonomic Resolution Down to the Species Level from Metagenomics Data Revealing Novelties</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oshma%20Chakoory">Oshma Chakoory</a>, <a href="https://publications.waset.org/abstracts/search?q=Sophie%20Comtet-Marre"> Sophie Comtet-Marre</a>, <a href="https://publications.waset.org/abstracts/search?q=Pierre%20Peyret"> Pierre Peyret</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Metagenomic classifiers are widely used for the taxonomic profiling of metagenomic data and estimation of taxa relative abundance. Small subunit rRNA genes are nowadays a gold standard for the phylogenetic resolution of complex microbial communities, although the power of this marker comes down to its use as full-length. We benchmarked the performance and accuracy of rRNA-specialized versus general-purpose read mappers, reference-targeted assemblers and taxonomic classifiers. We then built a pipeline called RiboTaxa to generate a highly sensitive and specific metataxonomic approach. Using metagenomics data, RiboTaxa gave the best results compared to other tools (Kraken2, Centrifuge (1), METAXA2 (2), PhyloFlash (3)) with precise taxonomic identification and relative abundance description, giving no false positive detection. Using real datasets from various environments (ocean, soil, human gut) and from different approaches (metagenomics and gene capture by hybridization), RiboTaxa revealed microbial novelties not seen by current bioinformatics analysis opening new biological perspectives in human and environmental health. In a study focused on corals’ health involving 20 metagenomic samples (4), an affiliation of prokaryotes was limited to the family level with Endozoicomonadaceae characterising healthy octocoral tissue. RiboTaxa highlighted 2 species of uncultured Endozoicomonas which were dominant in the healthy tissue. Both species belonged to a genus not yet described, opening new research perspectives on corals’ health. Applied to metagenomics data from a study on human gut and extreme longevity (5), RiboTaxa detected the presence of an uncultured archaeon in semi-supercentenarians (aged 105 to 109 years) highlighting an archaeal genus, not yet described, and 3 uncultured species belonging to the Enorma genus that could be species of interest participating in the longevity process. RiboTaxa is user-friendly, rapid, allowing microbiota structure description from any environment and the results can be easily interpreted. This software is freely available at https://github.com/oschakoory/RiboTaxa under the GNU Affero General Public License 3.0. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metagenomics%20profiling" title="metagenomics profiling">metagenomics profiling</a>, <a href="https://publications.waset.org/abstracts/search?q=microbial%20diversity" title=" microbial diversity"> microbial diversity</a>, <a href="https://publications.waset.org/abstracts/search?q=SSU%20rRNA%20genes" title=" SSU rRNA genes"> SSU rRNA genes</a>, <a href="https://publications.waset.org/abstracts/search?q=full-length%20phylogenetic%20marker" title=" full-length phylogenetic marker"> full-length phylogenetic marker</a> </p> <a href="https://publications.waset.org/abstracts/154374/ribotaxa-combined-approaches-for-taxonomic-resolution-down-to-the-species-level-from-metagenomics-data-revealing-novelties" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154374.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">120</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28</span> Metagenomics Analysis on Microbial Communities of Sewage Sludge from Nyeri-Kangemi Wastewater Treatment Plant, Nyeri County-Kenya</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Allan%20Kiptanui%20Kimisto">Allan Kiptanui Kimisto</a>, <a href="https://publications.waset.org/abstracts/search?q=Geoffrey%20Odhiambo%20Ongondo"> Geoffrey Odhiambo Ongondo</a>, <a href="https://publications.waset.org/abstracts/search?q=Anastasia%20Wairimu%20Muia"> Anastasia Wairimu Muia</a>, <a href="https://publications.waset.org/abstracts/search?q=Cyrus%20Ndungu%20Kimani"> Cyrus Ndungu Kimani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The major challenge to proper sewage sludge treatment processes is the poor understanding of sludge microbiome diversities. This study applied the whole-genome. shotgun metagenomics technique to profile the microbial composition of sewage sludge in two active digestion lagoons at the Nyeri-Kangemi Wastewater Treatment Plant in Nyeri County, Kenya. Total microbial community DNA was extracted from samples using the available ZymoBIOMICS™ DNA Miniprep Kit and sequenced using Shotgun metagenomics. Samples were analyzed using MG-RAST software (Project ID: mgp100988), which allowed for comparing taxonomic diversity before β-diversities studies for Bacteria, Archaea and Eukaryotes. The study identified 57 phyla, 145 classes, 301 orders, 506 families, 963 genera, and 1980 species. Bacteria dominated the microbes and comprised 28 species, 51 classes, 110 orders, 243 families, 597 genera, and 1518 species. The Bacteroides(6.77%) were dominant, followed by Acinetobacter(1.44%) belonging to the Gammaproteobacteria and Acidororax (1.36%), Bacillus (1.24%) and Clostridium (1.02%) belonging to Betaproteobacteria. Archaea recorded 5 phyla, 13 classes, 19 orders, 29 families, 60 genera,and87 species, with the dominant genera being Methanospirillum (16.01%), methanosarcina (15.70%), and Methanoregula(14.80%) and Methanosaeta (8.74%), Methanosphaerula(5.48%) and Methanobrevibacter(5.03%) being the subdominant group. The eukaryotes were the least in abundance and comprised 24 phyla, 81 classes, 301 orders, 506 families, 963 genera, and 980 species. Arabidopsis (4.91%) and Caenorhabditis (4.81%) dominated the eukaryotes, while Dityostelium (3.63%) and Drosophila(2.08%) were the subdominant genera. All these microbes play distinct roles in the anaerobic treatment process of sewage sludge. The local sludge microbial composition and abundance variations may be due to age difference differences between the two digestion lagoons in operation at the plant and the different degradation rales played by the taxa. The information presented in this study can help in the genetic manipulation or formulation of optimal microbial ratios to improve their effectiveness in sewage sludge treatment. This study recommends further research on how the different taxa respond to environmental changes over time and space. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=shotgun%20metagenomics" title="shotgun metagenomics">shotgun metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=sludge" title=" sludge"> sludge</a>, <a href="https://publications.waset.org/abstracts/search?q=bacteria" title=" bacteria"> bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=archaea" title=" archaea"> archaea</a>, <a href="https://publications.waset.org/abstracts/search?q=eukaryotes" title=" eukaryotes"> eukaryotes</a> </p> <a href="https://publications.waset.org/abstracts/157198/metagenomics-analysis-on-microbial-communities-of-sewage-sludge-from-nyeri-kangemi-wastewater-treatment-plant-nyeri-county-kenya" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157198.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">99</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27</span> Predicting Potential Protein Therapeutic Candidates from the Gut Microbiome </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prasanna%20Ramachandran">Prasanna Ramachandran</a>, <a href="https://publications.waset.org/abstracts/search?q=Kareem%20Graham"> Kareem Graham</a>, <a href="https://publications.waset.org/abstracts/search?q=Helena%20Kiefel"> Helena Kiefel</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunit%20Jain"> Sunit Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Todd%20DeSantis"> Todd DeSantis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microbes that reside inside the mammalian GI tract, commonly referred to as the gut microbiome, have been shown to have therapeutic effects in animal models of disease. We hypothesize that specific proteins produced by these microbes are responsible for this activity and may be used directly as therapeutics. To speed up the discovery of these key proteins from the big-data metagenomics, we have applied machine learning techniques. Using amino acid sequences of known epitopes and their corresponding binding partners, protein interaction descriptors (PID) were calculated, making a positive interaction set. A negative interaction dataset was calculated using sequences of proteins known not to interact with these same binding partners. Using Random Forest and positive and negative PID, a machine learning model was trained and used to predict interacting versus non-interacting proteins. Furthermore, the continuous variable, cosine similarity in the interaction descriptors was used to rank bacterial therapeutic candidates. Laboratory binding assays were conducted to test the candidates for their potential as therapeutics. Results from binding assays reveal the accuracy of the machine learning prediction and are subsequently used to further improve the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=protein-interactions" title="protein-interactions">protein-interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=machine-learning" title=" machine-learning"> machine-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=microbiome" title=" microbiome"> microbiome</a> </p> <a href="https://publications.waset.org/abstracts/62501/predicting-potential-protein-therapeutic-candidates-from-the-gut-microbiome" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62501.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">26</span> Data Analysis for Taxonomy Prediction and Annotation of 16S rRNA Gene Sequences from Metagenome Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suchithra%20V.">Suchithra V.</a>, <a href="https://publications.waset.org/abstracts/search?q=Shreedhanya"> Shreedhanya</a>, <a href="https://publications.waset.org/abstracts/search?q=Kavya%20Menon"> Kavya Menon</a>, <a href="https://publications.waset.org/abstracts/search?q=Vidya%20Niranjan"> Vidya Niranjan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Skin metagenomics has a wide range of applications with direct relevance to the health of the organism. It gives us insight to the diverse community of microorganisms (the microbiome) harbored on the skin. In the recent years, it has become increasingly apparent that the interaction between skin microbiome and the human body plays a prominent role in immune system development, cancer development, disease pathology, and many other biological implications. Next Generation Sequencing has led to faster and better understanding of environmental organisms and their mutual interactions. This project is studying the human skin microbiome of different individuals having varied skin conditions. Bacterial 16S rRNA data of skin microbiome is downloaded from SRA toolkit provided by NCBI to perform metagenomics analysis. Twelve samples are selected with two controls, and 3 different categories, i.e., sex (male/female), skin type (moist/intermittently moist/sebaceous) and occlusion (occluded/intermittently occluded/exposed). Quality of the data is increased using Cutadapt, and its analysis is done using FastQC. USearch, a tool used to analyze an NGS data, provides a suitable platform to obtain taxonomy classification and abundance of bacteria from the metagenome data. The statistical tool used for analyzing the USearch result is METAGENassist. The results revealed that the top three abundant organisms found were: Prevotella, Corynebacterium, and Anaerococcus. Prevotella is known to be an infectious bacterium found on wound, tooth cavity, etc. Corynebacterium and Anaerococcus are opportunist bacteria responsible for skin odor. This result infers that Prevotella thrives easily in sebaceous skin conditions. Therefore it is better to undergo intermittently occluded treatment such as applying ointments, creams, etc. to treat wound for sebaceous skin type. Exposing the wound should be avoided as it leads to an increase in Prevotella abundance. Moist skin type individuals can opt for occluded or intermittently occluded treatment as they have shown to decrease the abundance of bacteria during treatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bacterial%2016S%20rRNA" title="bacterial 16S rRNA ">bacterial 16S rRNA </a>, <a href="https://publications.waset.org/abstracts/search?q=next%20generation%20sequencing" title=" next generation sequencing"> next generation sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=skin%20metagenomics" title=" skin metagenomics"> skin metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=skin%20microbiome" title=" skin microbiome"> skin microbiome</a>, <a href="https://publications.waset.org/abstracts/search?q=taxonomy" title=" taxonomy"> taxonomy</a> </p> <a href="https://publications.waset.org/abstracts/99878/data-analysis-for-taxonomy-prediction-and-annotation-of-16s-rrna-gene-sequences-from-metagenome-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99878.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">172</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25</span> Viral Metagenomics Revealed a Novel Cardiovirus in Feces of Wild Rats</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asif%20Mahmood">Asif Mahmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Shama%20Shama"> Shama Shama</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Ni"> Hao Ni</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Wang"> Hao Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu%20Ling"> Yu Ling</a>, <a href="https://publications.waset.org/abstracts/search?q=Hui%20Xu"> Hui Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shixing%20Yang"> Shixing Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qais%20Ahmad%20Naseer"> Qais Ahmad Naseer</a>, <a href="https://publications.waset.org/abstracts/search?q=Wen%20Zhang"> Wen Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cardiovirus is a genus of viruses belonging to the family Picornaviridae. Here, we used viral metagenomic techniques to detect the viral nucleic acid in the fecal samples from wild rats in Zhenjiang city in China. Fecal samples were collected from 20 wild rats and pooled into four sample pools and then subjected to libraries construction which were then sequenced on Illumina MiSeq platform. The sequenced reads were analyzed using viral metagenomic analysis pipeline. A novel cardiovirus from feces of a wild rat was identified, named amzj-2018, of which the complete genome was acquired. Phylogenetic analysis based on the complete amino acid sequence of polyprotein revealed that amzj-2018 formed a separate branch located between clusters of Saffold virus and Rat Theilovirus 1 (RTV-1). Phylogenetic analysis based on different regions of the polyproteins, including P1, P2, P3, and P2+P3, respectively, showed discordant trees, where the tree based on P3 region indicated that amzj-2018 clustered separately between Theiler's murine encephalomyelitis virus and RTV-1. The complete genome of a cardiovirus was determined from the feces of wild rats which belonged to a novel type of cardiovirus based on phylogenetic analysis. Whether it is associated with disease needs further investigation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardiovirus" title="cardiovirus">cardiovirus</a>, <a href="https://publications.waset.org/abstracts/search?q=viral%20metagenomics" title=" viral metagenomics"> viral metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=genomic%20organization" title=" genomic organization"> genomic organization</a>, <a href="https://publications.waset.org/abstracts/search?q=phylogenetic%20analysis" title=" phylogenetic analysis"> phylogenetic analysis</a> </p> <a href="https://publications.waset.org/abstracts/192230/viral-metagenomics-revealed-a-novel-cardiovirus-in-feces-of-wild-rats" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192230.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">18</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">24</span> Microbial Biogeography of Greek Olive Varieties Assessed by Amplicon-Based Metagenomics Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lena%20Payati">Lena Payati</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Kazou"> Maria Kazou</a>, <a href="https://publications.waset.org/abstracts/search?q=Effie%20Tsakalidou"> Effie Tsakalidou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Table olives are one of the most popular fermented vegetables worldwide, which along with olive oil, have a crucial role in the world economy. They are highly appreciated by the consumers for their characteristic taste and pleasant aromas, while several health and nutritional benefits have been reported as well. Until recently, microbial biogeography, i.e., the study of microbial diversity over time and space, has been mainly associated with wine. However, nowadays, the term 'terroir' has been extended to other crops and food products so as to link the geographical origin and environmental conditions to quality aspects of fermented foods. Taking the above into consideration, the present study focuses on the microbial fingerprinting of the most important olive varieties of Greece with the state-of-the-art amplicon-based metagenomics analysis. Towards this, in 2019, 61 samples from 38 different olive varieties were collected at the final stage of ripening from 13 well spread geographical regions in Greece. For the metagenomics analysis, total DNA was extracted from the olive samples, and the 16S rRNA gene and ITS DNA region were sequenced and analyzed using bioinformatics tools for the identification of bacterial and yeasts/fungal diversity, respectively. Furthermore, principal component analysis (PCA) was also performed for data clustering based on the average microbial composition of all samples from each region of origin. According to the composition, results obtained, when samples were analyzed separately, the majority of both bacteria (such as Pantoea, Enterobacter, Roserbergiella, and Pseudomonas) and yeasts/fungi (such as Aureobasidium, Debaromyces, Candida, and Cladosporium) genera identified were found in all 61 samples. Even though interesting differences were observed at the relative abundance level of the identified genera, the bacterial genus Pantoea and the yeast/fungi genus Aureobasidium were the dominant ones in 35 and 40 samples, respectively. Of note, olive samples collected from the same region had similar fingerprint (genera identified and relative abundance level) regardless of the variety, indicating a potential association between the relative abundance of certain taxa and the geographical region. When samples were grouped by region of origin, distinct bacterial profiles per region were observed, which was also evident from the PCA analysis. This was not the case for the yeast/fungi profiles since 10 out of the 13 regions were grouped together mainly due to the dominance of the genus Aureobasidium. A second cluster was formed for the islands Crete and Rhodes, both of which are located in the Southeast Aegean Sea. These two regions clustered together mainly due to the identification of the genus Toxicocladosporium in relatively high abundances. Finally, the Agrinio region was separated from the others as it showed a completely different microbial fingerprinting. However, due to the limited number of olive samples from some regions, a subsequent PCA analysis with more samples from these regions is expected to yield in a more clear clustering. The present study is part of a bigger project, the first of its kind in Greece, with the ultimate goal to analyze a larger set of olive samples of different varieties and from different regions in Greece in order to have a reliable olives’ microbial biogeography. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amplicon-based%20metagenomics%20analysis" title="amplicon-based metagenomics analysis">amplicon-based metagenomics analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=bacteria" title=" bacteria"> bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=microbial%20biogeography" title=" microbial biogeography"> microbial biogeography</a>, <a href="https://publications.waset.org/abstracts/search?q=olive%20microbiota" title=" olive microbiota"> olive microbiota</a>, <a href="https://publications.waset.org/abstracts/search?q=yeasts%2Ffungi" title=" yeasts/fungi"> yeasts/fungi</a> </p> <a href="https://publications.waset.org/abstracts/134198/microbial-biogeography-of-greek-olive-varieties-assessed-by-amplicon-based-metagenomics-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134198.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">114</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">23</span> TAXAPRO, A Streamlined Pipeline to Analyze Shotgun Metagenomes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Sehli">Sofia Sehli</a>, <a href="https://publications.waset.org/abstracts/search?q=Zainab%20El%20Ouafi"> Zainab El Ouafi</a>, <a href="https://publications.waset.org/abstracts/search?q=Casey%20Eddington"> Casey Eddington</a>, <a href="https://publications.waset.org/abstracts/search?q=Soumaya%20Jbara"> Soumaya Jbara</a>, <a href="https://publications.waset.org/abstracts/search?q=Kasambula%20Arthur%20Shem"> Kasambula Arthur Shem</a>, <a href="https://publications.waset.org/abstracts/search?q=Islam%20El%20Jaddaoui"> Islam El Jaddaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayorinde%20Afolayan"> Ayorinde Afolayan</a>, <a href="https://publications.waset.org/abstracts/search?q=Olaitan%20I.%20Awe"> Olaitan I. Awe</a>, <a href="https://publications.waset.org/abstracts/search?q=Allissa%20Dillman"> Allissa Dillman</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Ghazal"> Hassan Ghazal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ability to promptly sequence whole genomes at a relatively low cost has revolutionized the way we study the microbiome. Microbiologists are no longer limited to studying what can be grown in a laboratory and instead are given the opportunity to rapidly identify the makeup of microbial communities in a wide variety of environments. Analyzing whole genome sequencing (WGS) data is a complex process that involves multiple moving parts and might be rather unintuitive for scientists that don’t typically work with this type of data. Thus, to help lower the barrier for less-computationally inclined individuals, TAXAPRO was developed at the first Omics Codeathon held virtually by the African Society for Bioinformatics and Computational Biology (ASBCB) in June 2021. TAXAPRO is an advanced metagenomics pipeline that accurately assembles organelle genomes from whole-genome sequencing data. TAXAPRO seamlessly combines WGS analysis tools to create a pipeline that automatically processes raw WGS data and presents organism abundance information in both a tabular and graphical format. TAXAPRO was evaluated using COVID-19 patient gut microbiome data. Analysis performed by TAXAPRO demonstrated a high abundance of Clostridia and Bacteroidia genera and a low abundance of Proteobacteria genera relative to others in the gut microbiome of patients hospitalized with COVID-19, consistent with the original findings derived using a different analysis methodology. This provides crucial evidence that the TAXAPRO workflow dispenses reliable organism abundance information overnight without the hassle of performing the analysis manually. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title="metagenomics">metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=shotgun%20metagenomic%20sequence%20analysis" title=" shotgun metagenomic sequence analysis"> shotgun metagenomic sequence analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=pipeline" title=" pipeline"> pipeline</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a> </p> <a href="https://publications.waset.org/abstracts/147152/taxapro-a-streamlined-pipeline-to-analyze-shotgun-metagenomes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147152.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">220</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22</span> Scalable and Accurate Detection of Pathogens from Whole-Genome Shotgun Sequencing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Janos%20Juhasz">Janos Juhasz</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandor%20Pongor"> Sandor Pongor</a>, <a href="https://publications.waset.org/abstracts/search?q=Balazs%20Ligeti"> Balazs Ligeti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Next-generation sequencing, especially whole genome shotgun sequencing, is becoming a common approach to gain insight into the microbiomes in a culture-independent way, even in clinical practice. It does not only give us information about the species composition of an environmental sample but opens the possibility to detect antimicrobial resistance and novel, or currently unknown, pathogens. Accurately and reliably detecting the microbial strains is a challenging task. Here we present a sensitive approach for detecting pathogens in metagenomics samples with special regard to detecting novel variants of known pathogens. We have developed a pipeline that uses fast, short read aligner programs (i.e., Bowtie2/BWA) and comprehensive nucleotide databases. Taxonomic binning is based on the lowest common ancestor (LCA) principle; each read is assigned to a taxon, covering the most significantly hit taxa. This approach helps in balancing between sensitivity and running time. The program was tested both on experimental and synthetic data. The results implicate that our method performs as good as the state-of-the-art BLAST-based ones, furthermore, in some cases, it even proves to be better, while running two orders magnitude faster. It is sensitive and capable of identifying taxa being present only in small abundance. Moreover, it needs two orders of magnitude less reads to complete the identification than MetaPhLan2 does. We analyzed an experimental anthrax dataset (B. anthracis strain BA104). The majority of the reads (96.50%) was classified as Bacillus anthracis, a small portion, 1.2%, was classified as other species from the Bacillus genus. We demonstrate that the evaluation of high-throughput sequencing data is feasible in a reasonable time with good classification accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title="metagenomics">metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=taxonomy%20binning" title=" taxonomy binning"> taxonomy binning</a>, <a href="https://publications.waset.org/abstracts/search?q=pathogens" title=" pathogens"> pathogens</a>, <a href="https://publications.waset.org/abstracts/search?q=microbiome" title=" microbiome"> microbiome</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20anthracis" title=" B. anthracis"> B. anthracis</a> </p> <a href="https://publications.waset.org/abstracts/99150/scalable-and-accurate-detection-of-pathogens-from-whole-genome-shotgun-sequencing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99150.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21</span> Exploring Emerging Viruses From a Protected Reserve</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nemat%20Sokhandan%20Bashir">Nemat Sokhandan Bashir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Threats from viruses to agricultural crops could be even larger than the losses caused by the other pathogens because, in many cases, the viral infection is latent but crucial from an epidemic point of view. Wild vegetation can be a source of many viruses that eventually find their destiny in crop plants. Although often asymptomatic in wild plants due to adaptation, they can potentially cause serious losses in crops. Therefore, exploring viruses in wild vegetation is very important. Recently, omics have been quite useful for exploring plant viruses from various plant sources, especially wild vegetation. For instance, we have discovered viruses such as Ambrossia asymptomatic virus I (AAV-1) through the application of metagenomics from Oklahoma Prairie Reserve. Accordingly, extracts from randomly-sampled plants are subjected to high speed and ultracentrifugation to separated virus-like particles (VLP), then nucleic acids in the form of DNA or RNA are extracted from such VLPs by treatment with phenol—chloroform and subsequent precipitation by ethanol. The nucleic acid preparations are separately treated with RNAse or DNAse in order to determine the genome component of VLPs. In the case of RNAs, the complementary cDNAs are synthesized before submitting to DNA sequencing. However, for VLPs with DNA contents, the procedure would be relatively straightforward without making cDNA. Because the length of the nucleic acid content of VPLs can be different, various strategies are employed to achieve sequencing. Techniques similar to so-called "chromosome walking" may be used to achieve sequences of long segments. When the nucleotide sequence data were obtained, they were subjected to BLAST analysis to determine the most related previously reported virus sequences. In one case, we determined that the novel virus was AAV-l because the sequence comparison and analysis revealed that the reads were the closest to the Indian citrus ringspot virus (ICRSV). AAV—l had an RNA genome with 7408 nucleotides in length and contained six open reading frames (ORFs). Based on phylogenies inferred from the replicase and coat protein ORFs of the virus, it was placed in the genus Mandarivirus. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wild" title="wild">wild</a>, <a href="https://publications.waset.org/abstracts/search?q=plant" title=" plant"> plant</a>, <a href="https://publications.waset.org/abstracts/search?q=novel" title=" novel"> novel</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a> </p> <a href="https://publications.waset.org/abstracts/176207/exploring-emerging-viruses-from-a-protected-reserve" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176207.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">80</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20</span> Metagenomic analysis of Irish cattle faecal samples using Oxford Nanopore MinION Next Generation Sequencing </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niamh%20Higgins">Niamh Higgins</a>, <a href="https://publications.waset.org/abstracts/search?q=Dawn%20Howard"> Dawn Howard </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Irish agri-food sector is of major importance to Ireland’s manufacturing sector and to the Irish economy through employment and the exporting of animal products worldwide. Infectious diseases and parasites have an impact on farm animal health causing profitability and productivity to be affected. For the sustainability of Irish dairy farming, there must be the highest standard of animal health. There can be a lack of information in accounting for > 1% of complete microbial diversity in an environment. There is the tendency of culture-based methods of microbial identification to overestimate the prevalence of species which grow easily on an agar surface. There is a need for new technologies to address these issues to assist with animal health. Metagenomic approaches provide information on both the whole genome and transcriptome present through DNA sequencing of total DNA from environmental samples producing high determination of functional and taxonomic information. Nanopore Next Generation Technologies have the ability to be powerful sequencing technologies. They provide high throughput, low material requirements and produce ultra-long reads, simplifying the experimental process. The aim of this study is to use a metagenomics approach to analyze dairy cattle faecal samples using the Oxford Nanopore MinION Next Generation Sequencer and to establish an in-house pipeline for metagenomic characterization of complex samples. Faecal samples will be obtained from Irish dairy farms, DNA extracted and the MinION will be used for sequencing, followed by bioinformatics analysis. Of particular interest, will be the parasite Buxtonella sulcata, which there has been little research on and which there is no research on its presence on Irish dairy farms. Preliminary results have shown the ability of the MinION to produce hundreds of reads in a relatively short time frame of eight hours. The faecal samples were obtained from 90 dairy cows on a Galway farm. The results from Oxford Nanopore ‘What’s in my pot’ (WIMP) using the Epi2me workflow, show that from a total of 926 classified reads, 87% were from the Kingdom Bacteria, 10% were from the Kingdom Eukaryota, 3% were from the Kingdom Archaea and < 1% were from the Kingdom Viruses. The most prevalent bacteria were those from the Genus Acholeplasma (71 reads), Bacteroides (35 reads), Clostridium (33 reads), Acinetobacter (20 reads). The most prevalent species present were those from the Genus Acholeplasma and included Acholeplasma laidlawii (39 reads) and Acholeplasma brassicae (26 reads). The preliminary results show the ability of the MinION for the identification of microorganisms to species level coming from a complex sample. With ongoing optimization of the pipe-line, the number of classified reads are likely to increase. Metagenomics has the potential in animal health for diagnostics of microorganisms present on farms. This would support wprevention rather than a cure approach as is outlined in the DAFMs National Farmed Animal Health Strategy 2017-2022. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=animal%20health" title="animal health">animal health</a>, <a href="https://publications.waset.org/abstracts/search?q=buxtonella%20sulcata" title=" buxtonella sulcata"> buxtonella sulcata</a>, <a href="https://publications.waset.org/abstracts/search?q=infectious%20disease" title=" infectious disease"> infectious disease</a>, <a href="https://publications.waset.org/abstracts/search?q=irish%20dairy%20cattle" title=" irish dairy cattle"> irish dairy cattle</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=minION" title=" minION"> minION</a>, <a href="https://publications.waset.org/abstracts/search?q=next%20generation%20sequencing" title=" next generation sequencing"> next generation sequencing</a> </p> <a href="https://publications.waset.org/abstracts/122126/metagenomic-analysis-of-irish-cattle-faecal-samples-using-oxford-nanopore-minion-next-generation-sequencing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122126.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">19</span> Microbial Dark Matter Analysis Using 16S rRNA Gene Metagenomics Sequences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hana%20Barak">Hana Barak</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20Sivan"> Alex Sivan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ariel%20Kushmaro"> Ariel Kushmaro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microorganisms are the most diverse and abundant life forms on Earth and account for a large portion of the Earth’s biomass and biodiversity. To date though, our knowledge regarding microbial life is lacking, as it is based mainly on information from cultivated organisms. Indeed, microbiologists have borrowed from astrophysics and termed the ‘uncultured microbial majority’ as ‘microbial dark matter’. The realization of how diverse and unexplored microorganisms are, actually stems from recent advances in molecular biology, and in particular from novel methods for sequencing microbial small subunit ribosomal RNA genes directly from environmental samples termed next-generation sequencing (NGS). This has led us to use NGS that generates several gigabases of sequencing data in a single experimental run, to identify and classify environmental samples of microorganisms. In metagenomics sequencing analysis (both 16S and shotgun), sequences are compared to reference databases that contain only small part of the existing microorganisms and therefore their taxonomy assignment may reveal groups of unknown microorganisms or origins. These unknowns, or the ‘microbial sequences dark matter’, are usually ignored in spite of their great importance. The goal of this work was to develop an improved bioinformatics method that enables more complete analyses of the microbial communities in numerous environments. Therefore, NGS was used to identify previously unknown microorganisms from three different environments (industrials wastewater, Negev Desert’s rocks and water wells at the Arava valley). 16S rRNA gene metagenome analysis of the microorganisms from those three environments produce about ~4 million reads for 75 samples. Between 0.1-12% of the sequences in each sample were tagged as ‘Unassigned’. Employing relatively simple methodology for resequencing of original gDNA samples through Sanger or MiSeq Illumina with specific primers, this study demonstrates that the mysterious ‘Unassigned’ group apparently contains sequences of candidate phyla. Those unknown sequences can be located on a phylogenetic tree and thus provide a better understanding of the ‘sequences dark matter’ and its role in the research of microbial communities and diversity. Studying this ‘dark matter’ will extend the existing databases and could reveal the hidden potential of the ‘microbial dark matter’. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bacteria" title="bacteria">bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=dark%20matter" title=" dark matter"> dark matter</a>, <a href="https://publications.waset.org/abstracts/search?q=Next%20Generation%20Sequencing" title=" Next Generation Sequencing"> Next Generation Sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=unknown" title=" unknown"> unknown</a> </p> <a href="https://publications.waset.org/abstracts/97387/microbial-dark-matter-analysis-using-16s-rrna-gene-metagenomics-sequences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97387.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18</span> Metagenomics Analysis of Bacteria in Sorghum Using next Generation Sequencing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kedibone%20Masenya">Kedibone Masenya</a>, <a href="https://publications.waset.org/abstracts/search?q=Memory%20Tekere"> Memory Tekere</a>, <a href="https://publications.waset.org/abstracts/search?q=Jasper%20Rees"> Jasper Rees</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sorghum is an important cereal crop in the world. In particular, it has attracted breeders due to capacity to serve as food, feed, fiber and bioenergy crop. Like any other plant, sorghum hosts a variety of microbes, which can either, have a neutral, negative and positive influence on the plant. In the current study, regions (V3/V4) of 16 S rRNA were targeted to extensively assess bacterial multitrophic interactions in the phyllosphere of sorghum. The results demonstrated that the presence of a pathogen has a significant effect on the endophytic bacterial community. Understanding these interactions is key to develop new strategies for plant protection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bacteria" title="bacteria">bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=multitrophic" title=" multitrophic"> multitrophic</a>, <a href="https://publications.waset.org/abstracts/search?q=sorghum" title=" sorghum"> sorghum</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20sequencing" title=" target sequencing"> target sequencing</a> </p> <a href="https://publications.waset.org/abstracts/73720/metagenomics-analysis-of-bacteria-in-sorghum-using-next-generation-sequencing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73720.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">283</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17</span> Metagenomics, Urinary Microbiome, and Chronic Prostatitis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elmira%20Davasaz%20Tabrizi">Elmira Davasaz Tabrizi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mushteba%20Sevil"> Mushteba Sevil</a>, <a href="https://publications.waset.org/abstracts/search?q=Ercan%20Arican"> Ercan Arican</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Directly or indirectly, the human microbiome, or the population of bacteria and other microorganisms living in the human body, has been linked with human health. Various research has examined the connection with both illness status and the composition of the human microbiome, even though current studies indicate that the gut microbiome influences the mucosa and immune system. A significant amount of effort is being put into understanding the human microbiome's natural history in terms of health outcomes while also expanding our comprehension of the molecular connections between the microbiome and the host. To maintain health and avoid disease, these efforts ultimately seek to find efficient methods for recovering human microbial communities. This review article describes how the human microbiome leads to chronic diseases and discusses evidence for an important significant disorder that is related to the microbiome and linked to prostate cancer: chronic prostatitis (CP). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urobiome" title="urobiome">urobiome</a>, <a href="https://publications.waset.org/abstracts/search?q=chronic%20prostatitis" title=" chronic prostatitis"> chronic prostatitis</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomic" title=" metagenomic"> metagenomic</a>, <a href="https://publications.waset.org/abstracts/search?q=urinary%20microbiome" title=" urinary microbiome"> urinary microbiome</a> </p> <a href="https://publications.waset.org/abstracts/159463/metagenomics-urinary-microbiome-and-chronic-prostatitis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159463.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16</span> Gut Microbial Dynamics in a Mouse Model of Inflammation-Linked Carcinogenesis as a Result of Diet Supplementation with Specific Mushroom Extracts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alvarez%20M.">Alvarez M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Chapela%20M.%20J."> Chapela M. J.</a>, <a href="https://publications.waset.org/abstracts/search?q=Balboa%20E."> Balboa E.</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubianes%20D."> Rubianes D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinde%20E."> Sinde E.</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernandez%20de%20Ana%20C."> Fernandez de Ana C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Rodr%C3%ADguez-Blanco%20A."> Rodríguez-Blanco A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The gut microbiota plays an important role as gut inflammation could contribute to colorectal cancer development; however, this role is still not fully understood, and tools able to prevent this progression are yet to be developed. The main objective of this study was to monitor the effects of a mushroom extracts formulation in gut microbial community composition of an Azoxymethane (AOM)/Dextran sodium sulfate (DSS) mice model of inflammation-linked carcinogenesis. For the in vivo study, 41 adult male mice of the C57BL / 6 strain were obtained. 36 of them have been induced in a state of colon carcinogenesis by a single intraperitoneal administration of AOM at a dose of 12.5 mg/kg; the control group animals received instead of the same volume of 0.9% saline. DSS is an extremely toxic polysaccharide sulfate that causes chronic inflammation of the colon mucosa, favoring the appearance of severe colitis and the production of tumors induced by AOM. Induction by AOM/DSS is an interesting platform for chemopreventive intervention studies. This time the model was used to monitor gut microbiota changes as a result of supplementation with a specific mushroom extracts formulation previously shown to have prebiotic activity. The animals have been divided into three groups: (i) Cancer + mushroom extracts formulation experimental group: to which the MicoDigest2.0 mushroom extracts formulation developed by Hifas da Terra S.L has been administered dissolved in drinking water at an estimated concentration of 100 mg / ml. (ii) Control group of animals with Cancer: to which normal water has been administered without any type of treatment. (iii) Control group of healthy animals: these are the animals that have not been induced cancer or have not received any treatment in drinking water. This treatment has been maintained for a period of 3 months, after which the animals were sacrificed to obtain tissues that were subsequently analyzed to verify the effects of the mushroom extract formulation. A microbiological analysis has been carried out to compare the microbial communities present in the intestines of the mice belonging to each of the study groups. For this, the methodology of massive sequencing by molecular analysis of the 16S gene has been used (Ion Torrent technology). Initially, DNA extraction and metagenomics libraries were prepared using the 16S Metagenomics kit, always following the manufacturer's instructions. This kit amplifies 7 of the 9 hypervariable regions of the 16S gene that will then be sequenced. Finally, the data obtained will be compared with a database that makes it possible to determine the degree of similarity of the sequences obtained with a wide range of bacterial genomes. Results obtained showed that, similarly to certain natural compounds preventing colorectal tumorigenesis, a mushroom formulation enriched the Firmicutes and Proteobacteria phyla and depleted Bacteroidetes. Therefore, it was demonstrated that the consumption of the mushroom extracts’ formulation developed could promote the recovery of the microbial balance that is disrupted in the mice model of carcinogenesis. More preclinical and clinical studies are needed to validate this promising approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=carcinogenesis" title="carcinogenesis">carcinogenesis</a>, <a href="https://publications.waset.org/abstracts/search?q=microbiota" title=" microbiota"> microbiota</a>, <a href="https://publications.waset.org/abstracts/search?q=mushroom%20extracts" title=" mushroom extracts"> mushroom extracts</a>, <a href="https://publications.waset.org/abstracts/search?q=inflammation" title=" inflammation"> inflammation</a> </p> <a href="https://publications.waset.org/abstracts/141902/gut-microbial-dynamics-in-a-mouse-model-of-inflammation-linked-carcinogenesis-as-a-result-of-diet-supplementation-with-specific-mushroom-extracts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141902.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15</span> Analysis of Taxonomic Compositions, Metabolic Pathways and Antibiotic Resistance Genes in Fish Gut Microbiome by Shotgun Metagenomics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anuj%20Tyagi">Anuj Tyagi</a>, <a href="https://publications.waset.org/abstracts/search?q=Balwinder%20Singh"> Balwinder Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveen%20Kumar%20B.%20T."> Naveen Kumar B. T.</a>, <a href="https://publications.waset.org/abstracts/search?q=Niraj%20K.%20Singh"> Niraj K. Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Characterization of diverse microbial communities in specific environment plays a crucial role in the better understanding of their functional relationship with the ecosystem. It is now well established that gut microbiome of fish is not the simple replication of microbiota of surrounding local habitat, and extensive species, dietary, physiological and metabolic variations in fishes may have a significant impact on its composition. Moreover, overuse of antibiotics in human, veterinary and aquaculture medicine has led to rapid emergence and propagation of antibiotic resistance genes (ARGs) in the aquatic environment. Microbial communities harboring specific ARGs not only get a preferential edge during selective antibiotic exposure but also possess the significant risk of ARGs transfer to other non-resistance bacteria within the confined environments. This phenomenon may lead to the emergence of habitat-specific microbial resistomes and subsequent emergence of virulent antibiotic-resistant pathogens with severe fish and consumer health consequences. In this study, gut microbiota of freshwater carp (Labeo rohita) was investigated by shotgun metagenomics to understand its taxonomic composition and functional capabilities. Metagenomic DNA, extracted from the fish gut, was subjected to sequencing on Illumina NextSeq to generate paired-end (PE) 2 x 150 bp sequencing reads. After the QC of raw sequencing data by Trimmomatic, taxonomic analysis by Kraken2 taxonomic sequence classification system revealed the presence of 36 phyla, 326 families and 985 genera in the fish gut microbiome. At phylum level, Proteobacteria accounted for more than three-fourths of total bacterial populations followed by Actinobacteria (14%) and Cyanobacteria (3%). Commonly used probiotic bacteria (Bacillus, Lactobacillus, Streptococcus, and Lactococcus) were found to be very less prevalent in fish gut. After sequencing data assembly by MEGAHIT v1.1.2 assembler and PROKKA automated analysis pipeline, pathway analysis revealed the presence of 1,608 Metacyc pathways in the fish gut microbiome. Biosynthesis pathways were found to be the most dominant (51%) followed by degradation (39%), energy-metabolism (4%) and fermentation (2%). Almost one-third (33%) of biosynthesis pathways were involved in the synthesis of secondary metabolites. Metabolic pathways for the biosynthesis of 35 antibiotic types were also present, and these accounted for 5% of overall metabolic pathways in the fish gut microbiome. Fifty-one different types of antibiotic resistance genes (ARGs) belonging to 15 antimicrobial resistance (AMR) gene families and conferring resistance against 24 antibiotic types were detected in fish gut. More than 90% ARGs in fish gut microbiome were against beta-lactams (penicillins, cephalosporins, penems, and monobactams). Resistance against tetracycline, macrolides, fluoroquinolones, and phenicols ranged from 0.7% to 1.3%. Some of the ARGs for multi-drug resistance were also found to be located on sequences of plasmid origin. The presence of pathogenic bacteria and ARGs on plasmid sequences suggested the potential risk due to horizontal gene transfer in the confined gut environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antibiotic%20resistance" title="antibiotic resistance">antibiotic resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=fish%20gut" title=" fish gut"> fish gut</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolic%20pathways" title=" metabolic pathways"> metabolic pathways</a>, <a href="https://publications.waset.org/abstracts/search?q=microbial%20diversity" title=" microbial diversity"> microbial diversity</a> </p> <a href="https://publications.waset.org/abstracts/99462/analysis-of-taxonomic-compositions-metabolic-pathways-and-antibiotic-resistance-genes-in-fish-gut-microbiome-by-shotgun-metagenomics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99462.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">14</span> A Comprehensive Review on Health Hazards and Challenges for Microbial Remediation of Persistent Organic Pollutants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nisha%20Gaur">Nisha Gaur</a>, <a href="https://publications.waset.org/abstracts/search?q=K.Narasimhulu"> K.Narasimhulu</a>, <a href="https://publications.waset.org/abstracts/search?q=Pydi%20Setty%20Yelamarthy"> Pydi Setty Yelamarthy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Persistent organic pollutants (POPs) have become a great concern due to their toxicity, transformation and bioaccumulation property. Therefore, this review highlights the types, sources, classification health hazards and mobility of organochlorine pesticides, industrial chemicals and their by-products. Moreover, with the signing of Aarhus and Stockholm convention on POPs there is an increased demand to identify and characterise such chemicals from industries and environment which are toxic in nature or to existing biota. Due to long life, persistent nature they enter into body through food and transfer to all tropic levels of ecological unit. In addition, POPs are lipophilic in nature and accumulate in lipid-containing tissues and organs which further indicates the adverse symptoms after the threshold limit. Though, several potential enzymes are reported from various categories of microorganism and their interaction with POPs may break down the complex compounds either through biodegradation, biostimulation or bioaugmentation process, however technological advancement and human activities have also indicated to explore the possibilities for the role of genetically modified organisms and metagenomics and metabolomics. Though many studies have been done to develop low cost, effective and reliable method for detection, determination and removal of ultra-trace concentration of persistent organic pollutants (POPs) but due to insufficient knowledge and non-feasibility of technique, the safe management of POPs is still a global challenge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=persistent%20organic%20pollutants" title="persistent organic pollutants">persistent organic pollutants</a>, <a href="https://publications.waset.org/abstracts/search?q=bioaccumulation" title=" bioaccumulation"> bioaccumulation</a>, <a href="https://publications.waset.org/abstracts/search?q=biostimulation" title=" biostimulation"> biostimulation</a>, <a href="https://publications.waset.org/abstracts/search?q=microbial%20remediation" title=" microbial remediation"> microbial remediation</a> </p> <a href="https://publications.waset.org/abstracts/76893/a-comprehensive-review-on-health-hazards-and-challenges-for-microbial-remediation-of-persistent-organic-pollutants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76893.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">298</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13</span> Interaction Between Gut Microorganisms and Endocrine Disruptors - Effects on Hyperglycaemia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karthika%20Durairaj">Karthika Durairaj</a>, <a href="https://publications.waset.org/abstracts/search?q=Buvaneswari%20G."> Buvaneswari G.</a>, <a href="https://publications.waset.org/abstracts/search?q=Gowdham%20M."> Gowdham M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Gilles%20M."> Gilles M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Velmurugan%20G."> Velmurugan G.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Hyperglycaemia is the primary cause of metabolic illness. Recently, researchers focused on the possibility that chemical exposure could promote metabolic disease. Hyperglycaemia causes a variety of metabolic diseases dependent on its etiologic conditions. According to animal and population-based research, individual chemical exposure causes health problems through alteration of endocrine function with the influence of microbial influence. We were intrigued by the function of gut microbiota variation in high fat and chemically induced hyperglycaemia. Methodology: C57/Bl6 mice were subjected to two different treatments to generate the etiologic-based diabetes model: I – a high-fat diet with a 45 kcal diet, and II - endocrine disrupting chemicals (EDCs) cocktail. The mice were monitored periodically for changes in body weight and fasting glucose. After 120 days of the experiment, blood anthropometry, faecal metagenomics and metabolomics were performed and analyzed through statistical analysis using one-way ANOVA and student’s t-test. Results: After 120 days of exposure, we found hyperglycaemic changes in both experimental models. The treatment groups also differed in terms of plasma lipid levels, creatinine, and hepatic markers. To determine the influence on glucose metabolism, microbial profiling and metabolite levels were significantly different between groups. The gene expression studies associated with glucose metabolism vary between hosts and their treatments. Conclusion: This research will result in the identification of biomarkers and molecular targets for better diabetes control and treatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyperglycaemia" title="hyperglycaemia">hyperglycaemia</a>, <a href="https://publications.waset.org/abstracts/search?q=endocrine-disrupting%20chemicals" title=" endocrine-disrupting chemicals"> endocrine-disrupting chemicals</a>, <a href="https://publications.waset.org/abstracts/search?q=gut%20microbiota" title=" gut microbiota"> gut microbiota</a>, <a href="https://publications.waset.org/abstracts/search?q=host%20metabolism" title=" host metabolism"> host metabolism</a> </p> <a href="https://publications.waset.org/abstracts/185837/interaction-between-gut-microorganisms-and-endocrine-disruptors-effects-on-hyperglycaemia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185837.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">40</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12</span> Illumina MiSeq Sequencing for Bacteria Identification on Audio-Visual Materials</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tereza%20Brany%C5%A1ov%C3%A1">Tereza Branyšová</a>, <a href="https://publications.waset.org/abstracts/search?q=Martina%20Kra%C4%8Dmarov%C3%A1"> Martina Kračmarová</a>, <a href="https://publications.waset.org/abstracts/search?q=Kate%C5%99ina%20Demnerov%C3%A1"> Kateřina Demnerová</a>, <a href="https://publications.waset.org/abstracts/search?q=Michal%20%C4%8Eurovi%C4%8D"> Michal Ďurovič</a>, <a href="https://publications.waset.org/abstracts/search?q=Hana%20Stiborov%C3%A1"> Hana Stiborová</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microbial deterioration threatens all objects of cultural heritage, including audio-visual materials. Fungi are commonly known to be the main factor in audio-visual material deterioration. However, although being neglected, bacteria also play a significant role. In addition to microbial contamination of materials, it is also essential to analyse air as a possible contamination source. This work aims to identify bacterial species in the archives of the Czech Republic that occur on audio-visual materials as well as in the air in the archives. For sampling purposes, the smears from the materials were taken by sterile polyurethane sponges, and the air was collected using a MAS-100 aeroscope. Metagenomic DNA from all collected samples was immediately isolated and stored at -20 °C. DNA library for the 16S rRNA gene was prepared using two-step PCR and specific primers and the concentration step was included due to meagre yields of the DNA. After that, the samples were sent to the University of Fairbanks, Alaska, for Illumina MiSeq sequencing. Subsequently, the analysis of the sequences was conducted in R software. The obtained sequences were assigned to the corresponding bacterial species using the DADA2 package. The impact of air contamination and the impact of different photosensitive layers that audio-visual materials were made of, such as gelatine, albumen, and collodion, were evaluated. As a next step, we will take a deeper focus on air contamination. We will select an appropriate culture-dependent approach along with a culture-independent approach to observe a metabolically active species in the air. Acknowledgment: This project is supported by grant no. DG18P02OVV062 of the Ministry of Culture of the Czech Republic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cultural%20heritage" title="cultural heritage">cultural heritage</a>, <a href="https://publications.waset.org/abstracts/search?q=Illumina%20MiSeq" title=" Illumina MiSeq"> Illumina MiSeq</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=microbial%20identification" title=" microbial identification"> microbial identification</a> </p> <a href="https://publications.waset.org/abstracts/136677/illumina-miseq-sequencing-for-bacteria-identification-on-audio-visual-materials" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136677.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> Sulfur-Containing Diet Shift Hydrogen Metabolism and Reduce Methane Emission and Modulated Gut Microbiome in Goats</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tsegay%20Teklebrhan%20Gebremariam">Tsegay Teklebrhan Gebremariam</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiliang"> Zhiliang</a>, <a href="https://publications.waset.org/abstracts/search?q=Arjan%20Jonker"> Arjan Jonker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study investigated that using corn gluten (CG) instead of cornmeal (CM) increased dietary sulfur shifted H₂ metabolism from methanogenesis to alternative sink and modulated microbiome in the rumen as well as hindgut segments of goats. Ruminal fermentation, CH₄ emissions and microbial abundance in goats (n = 24). The experiment was performed using a randomized block design with two dietary treatments (CM and CG with 400 g/kg DM each). Goats in CG increased sulfur, NDF and CP intake and decreased starch intake as compared with those in CM. Goats that received CG diet had decreased dissolved hydrogen (dH₂) (P = 0.01) and dissolved methane yield and emission (dCH₄) (P = 0.001), while increased dH₂S both in the rumen and hindgut segments than those fed CM. Goats fed CG had higher (p < 0.01) gene copies of microbiota and cellulolytic bacteria, whereas starch utilizing bacterial species were less in the rumen and hindgut than those fed CM. Higher (P < 0.05) methanogenic diversity and abundances of Methanimicrococcus and Methanomicrobium were observed in goats that consumed CG, whilst containing lower Methanobrevibacter populations than those receiving CM. The study suggested that goats fed corn gluten improved the gene copies of microbiota and fibrolytic bacterial species while reducing starch utilizing species in the rumen and hindgut segments as compared with that fed cornmeal. Goats consuming corn gluten had a more enriched methanogenic diversity and reduced Methanobrevibacter, a contributor to CH₄ emissions, as compared with goats fed CM. Corn gluten could be used as an alternative feed to decrease the enteric CH₄ emission in ruminant production. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dissolved%20gasses" title="dissolved gasses">dissolved gasses</a>, <a href="https://publications.waset.org/abstracts/search?q=methanogenesis" title=" methanogenesis"> methanogenesis</a>, <a href="https://publications.waset.org/abstracts/search?q=microbial%20community" title=" microbial community"> microbial community</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a> </p> <a href="https://publications.waset.org/abstracts/147510/sulfur-containing-diet-shift-hydrogen-metabolism-and-reduce-methane-emission-and-modulated-gut-microbiome-in-goats" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147510.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10</span> Metagenomics-Based Molecular Epidemiology of Viral Diseases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vyacheslav%20Furtak">Vyacheslav Furtak</a>, <a href="https://publications.waset.org/abstracts/search?q=Merja%20Roivainen"> Merja Roivainen</a>, <a href="https://publications.waset.org/abstracts/search?q=Olga%20Mirochnichenko"> Olga Mirochnichenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Majid%20Laassri"> Majid Laassri</a>, <a href="https://publications.waset.org/abstracts/search?q=Bella%20Bidzhieva"> Bella Bidzhieva</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatiana%20Zagorodnyaya"> Tatiana Zagorodnyaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20Chizhikov"> Vladimir Chizhikov</a>, <a href="https://publications.waset.org/abstracts/search?q=Konstantin%20Chumakov"> Konstantin Chumakov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Molecular epidemiology and environmental surveillance are parts of a rational strategy to control infectious diseases. They have been widely used in the worldwide campaign to eradicate poliomyelitis, which otherwise would be complicated by the inability to rapidly respond to outbreaks and determine sources of the infection. The conventional scheme involves isolation of viruses from patients and the environment, followed by their identification by nucleotide sequences analysis to determine phylogenetic relationships. This is a tedious and time-consuming process that yields definitive results when it may be too late to implement countermeasures. Because of the difficulty of high-throughput full-genome sequencing, most such studies are conducted by sequencing only capsid genes or their parts. Therefore the important information about the contribution of other parts of the genome and inter- and intra-species recombination to viral evolution is not captured. Here we propose a new approach based on the rapid concentration of sewage samples with tangential flow filtration followed by deep sequencing and reconstruction of nucleotide sequences of viruses present in the samples. The entire nucleic acids content of each sample is sequenced, thus preserving in digital format the complete spectrum of viruses. A set of rapid algorithms was developed to separate deep sequence reads into discrete populations corresponding to each virus and assemble them into full-length consensus contigs, as well as to generate a complete profile of sequence heterogeneities in each of them. This provides an effective approach to study molecular epidemiology and evolution of natural viral populations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=poliovirus" title="poliovirus">poliovirus</a>, <a href="https://publications.waset.org/abstracts/search?q=eradication" title=" eradication"> eradication</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20surveillance" title=" environmental surveillance"> environmental surveillance</a>, <a href="https://publications.waset.org/abstracts/search?q=laboratory%20diagnosis" title=" laboratory diagnosis"> laboratory diagnosis</a> </p> <a href="https://publications.waset.org/abstracts/37436/metagenomics-based-molecular-epidemiology-of-viral-diseases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37436.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">281</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9</span> Insights into Archaeological Human Sample Microbiome Using 16S rRNA Gene Sequencing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alisa%20Kazarina">Alisa Kazarina</a>, <a href="https://publications.waset.org/abstracts/search?q=Guntis%20Gerhards"> Guntis Gerhards</a>, <a href="https://publications.waset.org/abstracts/search?q=Elina%20Petersone-Gordina"> Elina Petersone-Gordina</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilva%20Pole"> Ilva Pole</a>, <a href="https://publications.waset.org/abstracts/search?q=Viktorija%20Igumnova"> Viktorija Igumnova</a>, <a href="https://publications.waset.org/abstracts/search?q=Janis%20Kimsis"> Janis Kimsis</a>, <a href="https://publications.waset.org/abstracts/search?q=Valentina%20Capligina"> Valentina Capligina</a>, <a href="https://publications.waset.org/abstracts/search?q=Renate%20Ranka"> Renate Ranka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human body is inhabited by a vast number of microorganisms, collectively known as the human microbiome, and there is a tremendous interest in evolutionary changes in human microbial ecology, diversity and function. The field of paleomicrobiology, study of ancient human microbiome, is powered by modern techniques of Next Generation Sequencing (NGS), which allows extracting microbial genomic data directly from archaeological sample of interest. One of the major techniques is 16S rRNA gene sequencing, by which certain 16S rRNA gene hypervariable regions are being amplified and sequenced. However, some limitations of this method exist including the taxonomic precision and efficacy of different regions used. The aim of this study was to evaluate the phylogenetic sensitivity of different 16S rRNA gene hypervariable regions for microbiome studies in the archaeological samples. Towards this aim, archaeological bone samples and corresponding soil samples from each burial environment were collected in Medieval cemeteries in Latvia. The Ion 16S™ Metagenomics Kit targeting different 16S rRNA gene hypervariable regions was used for library construction (Ion Torrent technologies). Sequenced data were analysed by using appropriate bioinformatic techniques; alignment and taxonomic representation was done using Mothur program. Sequences of most abundant genus were further aligned to E. coli 16S rRNA gene reference sequence using MEGA7 in order to identify the hypervariable region of the segment of interest. Our results showed that different hypervariable regions had different discriminatory power depending on the groups of microbes, as well as the nature of samples. On the basis of our results, we suggest that wider range of primers used can provide more accurate recapitulation of microbial communities in archaeological samples. Acknowledgements. This work was supported by the ERAF grant Nr. 1.1.1.1/16/A/101. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=16S%20rRNA%20gene" title="16S rRNA gene">16S rRNA gene</a>, <a href="https://publications.waset.org/abstracts/search?q=ancient%20human%20microbiome" title=" ancient human microbiome"> ancient human microbiome</a>, <a href="https://publications.waset.org/abstracts/search?q=archaeology" title=" archaeology"> archaeology</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=genomics" title=" genomics"> genomics</a>, <a href="https://publications.waset.org/abstracts/search?q=microbiome" title=" microbiome"> microbiome</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20biology" title=" molecular biology"> molecular biology</a>, <a href="https://publications.waset.org/abstracts/search?q=next-generation%20sequencing" title=" next-generation sequencing"> next-generation sequencing</a> </p> <a href="https://publications.waset.org/abstracts/78646/insights-into-archaeological-human-sample-microbiome-using-16s-rrna-gene-sequencing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78646.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">189</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> Molecular Approach for the Detection of Lactic Acid Bacteria in the Kenyan Spontaneously Fermented Milk, Mursik</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Masani%20Nduko">John Masani Nduko</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Wafula%20Matofari"> Joseph Wafula Matofari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many spontaneously fermented milk products are produced in Kenya, where they are integral to the human diet and play a central role in enhancing food security and income generation via small-scale enterprises. Fermentation enhances product properties such as taste, aroma, shelf-life, safety, texture, and nutritional value. Some of these products have demonstrated therapeutic and probiotic effects although recent reports have linked some to death, biotoxin infections, and esophageal cancer. These products are mostly processed from poor quality raw materials under unhygienic conditions resulting to inconsistent product quality and limited shelf-lives. Though very popular, research on their processing technologies is low, and none of the products has been produced under controlled conditions using starter cultures. To modernize the processing technologies for these products, our study aims at describing the microbiology and biochemistry of a representative Kenyan spontaneously fermented milk product, Mursik using modern biotechnology (DNA sequencing) and their chemical composition. Moreover, co-creation processes reflecting stakeholders’ experiences on traditional fermented milk production technologies and utilization, ideals and senses of value, which will allow the generation of products based on common ground for rapid progress will be discussed. Knowledge of the value of clean starting raw material will be emphasized, the need for the definition of fermentation parameters highlighted, and standard equipment employment to attain controlled fermentation discussed. This presentation will review the available information regarding traditional fermented milk (Mursik) and highlight our current research work on the application of molecular approaches (metagenomics) for the valorization of Mursik production process through starter culture/ probiotic strains isolation and identification, and quality and safety aspects of the product. The importance of the research and future research areas on the same subject will also be highlighted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lactic%20acid%20bacteria" title="lactic acid bacteria">lactic acid bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20throughput%20biotechnology" title=" high throughput biotechnology"> high throughput biotechnology</a>, <a href="https://publications.waset.org/abstracts/search?q=spontaneous%20fermentation" title=" spontaneous fermentation"> spontaneous fermentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Mursik" title=" Mursik"> Mursik</a> </p> <a href="https://publications.waset.org/abstracts/65041/molecular-approach-for-the-detection-of-lactic-acid-bacteria-in-the-kenyan-spontaneously-fermented-milk-mursik" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65041.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">293</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Towards End-To-End Disease Prediction from Raw Metagenomic Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maxence%20Queyrel">Maxence Queyrel</a>, <a href="https://publications.waset.org/abstracts/search?q=Edi%20Prifti"> Edi Prifti</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandre%20Templier"> Alexandre Templier</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Daniel%20Zucker"> Jean-Daniel Zucker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20prediction" title=" disease prediction"> disease prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=end-to-end%20machine%20learning" title=" end-to-end machine learning"> end-to-end machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20instance%20learning" title=" multiple instance learning"> multiple instance learning</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20medicine" title=" precision medicine"> precision medicine</a> </p> <a href="https://publications.waset.org/abstracts/131509/towards-end-to-end-disease-prediction-from-raw-metagenomic-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131509.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Metagenomic Assessment of the Effects of Genetically Modified Crops on Microbial Ecology and Physicochemical Properties of Soil</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Falana%20Yetunde%20Olaitan">Falana Yetunde Olaitan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ijah%20%20U.%20J.%20J"> Ijah U. J. J</a>, <a href="https://publications.waset.org/abstracts/search?q=Solebo%20Shakirat%20O."> Solebo Shakirat O.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetically modified crops are already phenomenally successful and are grown worldwide in more than eighteen countries on more than 67 million hectares. Nigeria, in October 2018, approved Bacillus thuringiensis (Bt) cotton and maize; therefore, the need to carry out environmental risk assessment studies. A total of 15 4L octagonal ceramic pots were filled with 4kg of soil and placed on the bench in 2 rows of 10 pots each and the 3rd row of 5 pots, 1st-row pots were used to plant GM cotton seeds, while the 2nd-row pots were used for non-GM cotton seeds and the 3rd row of 5 pots served as control, all in the screen house. Soil samples for metagenomic DNA extraction were collected at random and at the monthly interval after planting at a distance of 2mm from the plant’s root and at a depth of 10cm using a sterile spatula. Soil samples for physicochemical analysis were collected before planting and after harvesting the GM and non-GM crops as well as from the control soil. The DNA was extracted, quantified and sequenced; Sample 1A (DNA from GM cotton Soil at 1st interval) gave the lowest sequence read with 0.853M while sample 2B (DNA from GM cotton Soil at 2nd interval) gave the highest with 5.785M, others gave between 1.8M and 4.7M. The samples treatment were grouped into four, Group 1 (GM cotton soil from 1 to 3 intervals) had between 800,000 and 5,700,000 strains of microbes (SOM), Group 2 (non GM cotton soil from 1 to 3 intervals) had between 1,400,600 and 4,200,000 SOM, Group 3 (control soil) had between 900,000 and 3,600,000 SOM and Group 4 (initial soil) had between 3,700,000 and 4,000,000 SOM. The microbes observed were predominantly bacteria (including archaea), fungi, dark matter alongside protists and phages. The predominant bacterial groups were the Terrabacteria (Bacillus funiculus, Bacillus sp.), the Proteobacteria (Microvirga massiliensis, sphingomonas sp.) and the Archaea (Nitrososphaera sp.), while the fungi were Aspergillus fischeri and Fusarium falciforme. The comparative analysis between groups was done using JACCARD PERMANOVA beta diversity analysis at P-value not more than 0.76 and there was no significant pair found. The pH for initial, GM cotton, non-GM cotton and control soil were 6.28, 6.26, 7.25, 8.26 and the percentage moisture was 0.63, 0.78, 0.89 and 0.82, respectively, while the percentage Nitrogen was observed to be 17.79, 1.14, 1.10 and 0.56 respectively. Other parameters include, varying concentrations of Potassium (0.46, 1,284.47, 1,785.48, 1,252.83 mg/kg) and Phosphorus (18.76, 17.76, 16.87, 15.23 mg/kg) were recorded for the four treatments respectively. The soil consisted mainly of silt (32.09 to 34.66%) and clay (58.89 to 60.23%), reflecting the soil texture as silty – clay. The results were then tested with ANOVA at less than 0.05 P-value and no pair was found to be significant as well. The results suggest that the GM crops have no significant effect on microbial ecology and physicochemical properties of the soil and, in turn, no direct or indirect effects on human health. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetically%20modified%20crop" title="genetically modified crop">genetically modified crop</a>, <a href="https://publications.waset.org/abstracts/search?q=microbial%20ecology" title=" microbial ecology"> microbial ecology</a>, <a href="https://publications.waset.org/abstracts/search?q=physicochemical%20properties" title=" physicochemical properties"> physicochemical properties</a>, <a href="https://publications.waset.org/abstracts/search?q=metagenomics" title=" metagenomics"> metagenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA" title=" DNA"> DNA</a>, <a href="https://publications.waset.org/abstracts/search?q=soil" title=" soil"> soil</a> </p> <a href="https://publications.waset.org/abstracts/144917/metagenomic-assessment-of-the-effects-of-genetically-modified-crops-on-microbial-ecology-and-physicochemical-properties-of-soil" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144917.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">145</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Effect of Chemical Fertilizer on Plant Growth-Promoting Rhizobacteria in Wheat</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tessa%20E.%20Reid">Tessa E. Reid</a>, <a href="https://publications.waset.org/abstracts/search?q=Vanessa%20N.%20Kavamura"> Vanessa N. Kavamura</a>, <a href="https://publications.waset.org/abstracts/search?q=Maider%20Abadie"> Maider Abadie</a>, <a href="https://publications.waset.org/abstracts/search?q=Adriana%20Torres-Ballesteros"> Adriana Torres-Ballesteros</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20Pawlett"> Mark Pawlett</a>, <a href="https://publications.waset.org/abstracts/search?q=Ian%20M.%20Clark"> Ian M. Clark</a>, <a href="https://publications.waset.org/abstracts/search?q=Jim%20Harris"> Jim Harris</a>, <a href="https://publications.waset.org/abstracts/search?q=Tim%20Mauchline"> Tim Mauchline</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The deleterious effect of chemical fertilizer on rhizobacterial diversity has been well documented using 16S rRNA gene amplicon sequencing and predictive metagenomics. Biofertilization is a cost-effective and sustainable alternative; improving strategies depends on isolating beneficial soil microorganisms. Although culturing is widespread in biofertilization, it is unknown whether the composition of cultured isolates closely mirrors native beneficial rhizobacterial populations. This study aimed to determine the relative abundance of culturable plant growth-promoting rhizobacteria (PGPR) isolates within total soil DNA and how potential PGPR populations respond to chemical fertilization in a commercial wheat variety. It was hypothesized that PGPR will be reduced in fertilized relative to unfertilized wheat. Triticum aestivum cv. Cadenza seeds were sown in a nutrient depleted agricultural soil in pots treated with and without nitrogen-phosphorous-potassium (NPK) fertilizer. Rhizosphere and rhizoplane samples were collected at flowering stage (10 weeks) and analyzed by culture-independent (amplicon sequence variance (ASV) analysis of total rhizobacterial DNA) and -dependent (isolation using growth media) techniques. Rhizosphere- and rhizoplane-derived microbiota culture collections were tested for plant growth-promoting traits using functional bioassays. In general, fertilizer addition decreased the proportion of nutrient-solubilizing bacteria (nitrate, phosphate, potassium, iron and, zinc) isolated from rhizocompartments in wheat, whereas salt tolerant bacteria were not affected. A PGPR database was created from isolate 16S rRNA gene sequences and searched against total soil DNA, revealing that 1.52% of total community ASVs were identified as culturable PGPR isolates. Bioassays identified a higher proportion of PGPR in non-fertilized samples (rhizosphere (49%) and rhizoplane (91%)) compared to fertilized samples (rhizosphere (21%) and rhizoplane (19%)) which constituted approximately 1.95% and 1.25% in non-fertilized and fertilized total community DNA, respectively. The analyses of 16S rRNA genes and deduced functional profiles provide an in-depth understanding of the responses of bacterial communities to fertilizer; this study suggests that rhizobacteria, which potentially benefit plants by mobilizing insoluble nutrients in soil, are reduced by chemical fertilizer addition. This knowledge will benefit the development of more targeted biofertilization strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bacteria" title="bacteria">bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=fertilizer" title=" fertilizer"> fertilizer</a>, <a href="https://publications.waset.org/abstracts/search?q=microbiome" title=" microbiome"> microbiome</a>, <a href="https://publications.waset.org/abstracts/search?q=rhizoplane" title=" rhizoplane"> rhizoplane</a>, <a href="https://publications.waset.org/abstracts/search?q=rhizosphere" title=" rhizosphere"> rhizosphere</a> </p> <a href="https://publications.waset.org/abstracts/132075/effect-of-chemical-fertilizer-on-plant-growth-promoting-rhizobacteria-in-wheat" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132075.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">307</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Changing the Landscape of Fungal Genomics: New Trends</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Igor%20V.%20Grigoriev">Igor V. Grigoriev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding of biological processes encoded in fungi is instrumental in addressing future food, feed, and energy demands of the growing human population. Genomics is a powerful and quickly evolving tool to understand these processes. The Fungal Genomics Program of the US Department of Energy Joint Genome Institute (JGI) partners with researchers around the world to explore fungi in several large scale genomics projects, changing the fungal genomics landscape. The key trends of these changes include: (i) rapidly increasing scale of sequencing and analysis, (ii) developing approaches to go beyond culturable fungi and explore fungal ‘dark matter,’ or unculturables, and (iii) functional genomics and multi-omics data integration. Power of comparative genomics has been recently demonstrated in several JGI projects targeting mycorrhizae, plant pathogens, wood decay fungi, and sugar fermenting yeasts. The largest JGI project ‘1000 Fungal Genomes’ aims at exploring the diversity across the Fungal Tree of Life in order to better understand fungal evolution and to build a catalogue of genes, enzymes, and pathways for biotechnological applications. At this point, at least 65% of over 700 known families have one or more reference genomes sequenced, enabling metagenomics studies of microbial communities and their interactions with plants. For many of the remaining families no representative species are available from culture collections. To sequence genomes of unculturable fungi two approaches have been developed: (a) sequencing DNA from fruiting bodies of ‘macro’ and (b) single cell genomics using fungal spores. The latter has been tested using zoospores from the early diverging fungi and resulted in several near-complete genomes from underexplored branches of the Fungal Tree, including the first genomes of Zoopagomycotina. Genome sequence serves as a reference for transcriptomics studies, the first step towards functional genomics. In the JGI fungal mini-ENCODE project transcriptomes of the model fungus Neurospora crassa grown on a spectrum of carbon sources have been collected to build regulatory gene networks. Epigenomics is another tool to understand gene regulation and recently introduced single molecule sequencing platforms not only provide better genome assemblies but can also detect DNA modifications. For example, 6mC methylome was surveyed across many diverse fungi and the highest among Eukaryota levels of 6mC methylation has been reported. Finally, data production at such scale requires data integration to enable efficient data analysis. Over 700 fungal genomes and other -omes have been integrated in JGI MycoCosm portal and equipped with comparative genomics tools to enable researchers addressing a broad spectrum of biological questions and applications for bioenergy and biotechnology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fungal%20genomics" title="fungal genomics">fungal genomics</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20cell%20genomics" title=" single cell genomics"> single cell genomics</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20methylation" title=" DNA methylation"> DNA methylation</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20genomics" title=" comparative genomics"> comparative genomics</a> </p> <a href="https://publications.waset.org/abstracts/60383/changing-the-landscape-of-fungal-genomics-new-trends" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60383.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">208</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=metagenomics&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=metagenomics&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a 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