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Behl, Tapan, Ishnoor Kaur, Aayush Sehgal, Sukhbir Singh, Saurabh Bhatia, Ahmed Al-Harrasi, Gokhan Zengin, et al. "Bioinformatics Accelerates the Major Tetrad: A Real Boost for the Pharmaceutical Industry." International Journal of Molecular Sciences 22, no. 12 (June 8, 2021): 6184. http://dx.doi.org/10.3390/ijms22126184.
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With advanced technology and its development, bioinformatics is one of the avant-garde fields that has managed to make amazing progress in the pharmaceutical–medical field by modeling the infrastructural dimensions of healthcare and integrating computing tools in drug innovation, facilitating prevention, detection/more accurate diagnosis, and treatment of disorders, while saving time and money. By association, bioinformatics and pharmacovigilance promoted both sample analyzes and interpretation of drug side effects, also focusing on drug discovery and development (DDD), in which systems biology, a personalized approach, and drug repositioning were considered together with translational medicine. The role of bioinformatics has been highlighted in DDD, proteomics, genetics, modeling, miRNA discovery and assessment, and clinical genome sequencing. The authors have collated significant data from the most known online databases and publishers, also narrowing the diversified applications, in order to target four major areas (tetrad): DDD, anti-microbial research, genomic sequencing, and miRNA research and its significance in the management of current pandemic context. Our analysis aims to provide optimal data in the field by stratification of the information related to the published data in key sectors and to capture the attention of researchers interested in bioinformatics, a field that has succeeded in advancing the healthcare paradigm by introducing developing techniques and multiple database platforms, addressed in the manuscript.
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Biaudet, Véronique, Franck Samson, and Philippe Bessières. "Micado—a network-oriented database for microbial genomes." Bioinformatics 13, no. 4 (1997): 431–38. http://dx.doi.org/10.1093/bioinformatics/13.4.431.
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de la Cuesta-Zuluaga, Jacobo, Ruth E. Ley, and Nicholas D. Youngblut. "Struo: a pipeline for building custom databases for common metagenome profilers." Bioinformatics 36, no. 7 (November 28, 2019): 2314–15. http://dx.doi.org/10.1093/bioinformatics/btz899.
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Abstract Summary Taxonomic and functional information from microbial communities can be efficiently obtained by metagenome profiling, which requires databases of genes and genomes to which sequence reads are mapped. However, the databases that accompany metagenome profilers are not updated at a pace that matches the increase in available microbial genomes, and unifying database content across metagenome profiling tools can be cumbersome. To address this, we developed Struo, a modular pipeline that automatizes the acquisition of genomes from public repositories and the construction of custom databases for multiple metagenome profilers. The use of custom databases that broadly represent the known microbial diversity by incorporating novel genomes results in a substantial increase in mappability of reads in synthetic and real metagenome datasets. Availability and implementation Source code available for download at https://github.com/leylabmpi/Struo. Custom genome taxonomy database databases available at http://ftp.tue.mpg.de/ebio/projects/struo/. Supplementary information Supplementary data are available at Bioinformatics online.
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Richter, Michael, Ramon Rosselló-Móra, Frank Oliver Glöckner, and Jörg Peplies. "JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison." Bioinformatics 32, no. 6 (November 16, 2015): 929–31. http://dx.doi.org/10.1093/bioinformatics/btv681.
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Abstract Summary: JSpecies Web Server (JSpeciesWS) is a user-friendly online service for in silico calculating the extent of identity between two genomes, a parameter routinely used in the process of polyphasic microbial species circumscription. The service measures the average nucleotide identity (ANI) based on BLAST+ (ANIb) and MUMmer (ANIm), as well as correlation indexes of tetra-nucleotide signatures (Tetra). In addition, it provides a Tetra Correlation Search function, which allows to rapidly compare selected genomes against a continuously updated reference database with currently about 32 000 published whole and draft genome sequences. For comparison, own genomes can be uploaded and references can be selected from the JSpeciesWS reference database. The service indicates whether two genomes share genomic identities above or below the species embracing thresholds, and serves as a fast way to allocate unknown genomes in the frame of the hitherto sequenced species. Availability and implementation: JSpeciesWS is available at http://jspecies.ribohost.com/jspeciesws. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: mrichter@ribocon.com
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Neely, Christopher J., Elaina D. Graham, and Benjamin J. Tully. "MetaSanity: an integrated microbial genome evaluation and annotation pipeline." Bioinformatics 36, no. 15 (May 19, 2020): 4341–44. http://dx.doi.org/10.1093/bioinformatics/btaa512.
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Abstract Summary As the importance of microbiome research continues to become more prevalent and essential to understanding a wide variety of ecosystems (e.g. marine, built, host associated, etc.), there is a need for researchers to be able to perform highly reproducible and quality analysis of microbial genomes. MetaSanity incorporates analyses from 11 existing and widely used genome evaluation and annotation suites into a single, distributable workflow, thereby decreasing the workload of microbiologists by allowing for a flexible, expansive data analysis pipeline. MetaSanity has been designed to provide separate, reproducible workflows that (i) can determine the overall quality of a microbial genome, while providing a putative phylogenetic assignment, and (ii) can assign structural and functional gene annotations with varying degrees of specificity to suit the needs of the researcher. The software suite combines the results from several tools to provide broad insights into overall metabolic function. Importantly, this software provides built-in optimization for ‘big data’ analysis by storing all relevant outputs in an SQL database, allowing users to query all the results for the elements that will most impact their research. Availability and implementation MetaSanity is provided under the GNU General Public License v.3.0 and is available for download at https://github.com/cjneely10/MetaSanity. This application is distributed as a Docker image. MetaSanity is implemented in Python3/Cython and C++. Instructions for its installation and use are available within the GitHub wiki page at https://github.com/cjneely10/MetaSanity/wiki, and additional instructions are available at https://cjneely10.github.io/year-archive/. MetaSanity is optimized for users with limited programing experience. Supplementary information Supplementary data are available at Bioinformatics online.
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Médigue, Claudine, Alexandra Calteau, Stéphane Cruveiller, Mathieu Gachet, Guillaume Gautreau, Adrien Josso, Aurélie Lajus, et al. "MicroScope—an integrated resource for community expertise of gene functions and comparative analysis of microbial genomic and metabolic data." Briefings in Bioinformatics 20, no. 4 (September 12, 2017): 1071–84. http://dx.doi.org/10.1093/bib/bbx113.
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Abstract The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform the manual curation of gene function in a comparative genomics and metabolic context. In this article, we describe the free-of-charge MicroScope services for the annotation and analysis of microbial (meta)genomes, transcriptomic and re-sequencing data. Then, the functionalities of the platform are presented in a way providing practical guidance and help to the nonspecialists in bioinformatics. Newly integrated analysis tools (i.e. prediction of virulence and resistance genes in bacterial genomes) and original method recently developed (the pan-genome graph representation) are also described. Integrated environments such as MicroScope clearly contribute, through the user community, to help maintaining accurate resources.
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Quatrini, Raquel, Verónica Martínez, Hector Osorio, Felipe A. Veloso, Inti Pedroso, Jorge H. Valdés, Eugenia Jedlicki, and David S. Holmes. "Iron Homeostasis Strategies in Acidophilic Iron Oxidizers: Comparative Genomic Analyses." Advanced Materials Research 20-21 (July 2007): 531–34. http://dx.doi.org/10.4028/www.scientific.net/amr.20-21.531.
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An understanding of the physiology and metabolic complexity of microbial consortia involved in metal solubilization is a prerequisite for the rational improvement of bioleaching technologies. Among the most challenging aspects that remain to be addressed is how aerobic acidophiles, especially Fe(II)-oxidizers, contend with the paradoxical hazards of iron overload and iron deficiency, each with deleterious consequences for growth. Homeostatic mechanisms regulating the acquisition, utilization/oxidation, storage and intracellular mobilization of cellular iron are deemed to be critical for fitness and survival of bioleaching microbes. In an attempt to contribute to the comprehensive understanding of the biology and ecology of the microbial communities in bioleaching econiches, we have used comparative genomics and other bioinformatic tools to reconstruct the iron management strategies in newly sequenced acidithiobacilli and other biomining genomes available in public databases. Species-specific genes have been identified with distinctive functional roles in iron management as well as genes shared by several species in biomining consortia. Their analysis contributes to our understanding of the general survival strategies in acidic and iron loaded environments and suggests functions for genes with currently unknown functions that might reveal novel aspects of iron response in acidophiles. Comprehensive examination of the occurrence and conservation of regulatory functions and regulatory sites also allowed the prediction of the metal regulatory networks for these biomining microbes.
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Doster, Enrique, Steven M. Lakin, Christopher J. Dean, Cory Wolfe, Jared G. Young, Christina Boucher, Keith E. Belk, Noelle R. Noyes, and Paul S. Morley. "MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data." Nucleic Acids Research 48, no. D1 (November 13, 2019): D561—D569. http://dx.doi.org/10.1093/nar/gkz1010.
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Abstract Antimicrobial resistance (AMR) is a threat to global public health and the identification of genetic determinants of AMR is a critical component to epidemiological investigations. High-throughput sequencing (HTS) provides opportunities for investigation of AMR across all microbial genomes in a sample (i.e. the metagenome). Previously, we presented MEGARes, a hand-curated AMR database and annotation structure developed to facilitate the analysis of AMR within metagenomic samples (i.e. the resistome). Along with MEGARes, we released AmrPlusPlus, a bioinformatics pipeline that interfaces with MEGARes to identify and quantify AMR gene accessions contained within a metagenomic sequence dataset. Here, we present MEGARes 2.0 (https://megares.meglab.org), which incorporates previously published resistance sequences for antimicrobial drugs, while also expanding to include published sequences for metal and biocide resistance determinants. In MEGARes 2.0, the nodes of the acyclic hierarchical ontology include four antimicrobial compound types, 57 classes, 220 mechanisms of resistance, and 1,345 gene groups that classify the 7,868 accessions. In addition, we present an updated version of AmrPlusPlus (AMR ++ version 2.0), which improves accuracy of classifications, as well as expanding scalability and usability.
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Wickramarachchi, Anuradha, Vijini Mallawaarachchi, Vaibhav Rajan, and Yu Lin. "MetaBCC-LR: metagenomics binning by coverage and composition for long reads." Bioinformatics 36, Supplement_1 (July 1, 2020): i3—i11. http://dx.doi.org/10.1093/bioinformatics/btaa441.
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Abstract Motivation Metagenomics studies have provided key insights into the composition and structure of microbial communities found in different environments. Among the techniques used to analyse metagenomic data, binning is considered a crucial step to characterize the different species of micro-organisms present. The use of short-read data in most binning tools poses several limitations, such as insufficient species-specific signal, and the emergence of long-read sequencing technologies offers us opportunities to surmount them. However, most current metagenomic binning tools have been developed for short reads. The few tools that can process long reads either do not scale with increasing input size or require a database with reference genomes that are often unknown. In this article, we present MetaBCC-LR, a scalable reference-free binning method which clusters long reads directly based on their k-mer coverage histograms and oligonucleotide composition. Results We evaluate MetaBCC-LR on multiple simulated and real metagenomic long-read datasets with varying coverages and error rates. Our experiments demonstrate that MetaBCC-LR substantially outperforms state-of-the-art reference-free binning tools, achieving ∼13% improvement in F1-score and ∼30% improvement in ARI compared to the best previous tools. Moreover, we show that using MetaBCC-LR before long-read assembly helps to enhance the assembly quality while significantly reducing the assembly cost in terms of time and memory usage. The efficiency and accuracy of MetaBCC-LR pave the way for more effective long-read-based metagenomics analyses to support a wide range of applications. Availability and implementation The source code is freely available at: https://github.com/anuradhawick/MetaBCC-LR. Supplementary information Supplementary data are available at Bioinformatics online.
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Wu, Meng, Qingdai Li, and Hongbing Wang. "Identification of Novel Biomarkers Associated With the Prognosis and Potential Pathogenesis of Breast Cancer via Integrated Bioinformatics Analysis." Technology in Cancer Research & Treatment 20 (January 1, 2021): 153303382199208. http://dx.doi.org/10.1177/1533033821992081.
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Background: Breast cancer is the most commonly diagnosed malignancy and a major cause of cancer-related deaths in women globally. Identification of novel prognostic and pathogenesis biomarkers play a pivotal role in the management of the disease. Methods: Three data sets from the GEO database were used to identify differentially expressed genes (DEGs) in breast cancer. Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes pathway analyses were performed to elucidate the functional roles of the DEGs. Besides, we investigated the translational and protein expression levels and survival data of the DEGs in patients with breast cancer from the Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine, Human Protein Atlas, and Kaplan Meier plotter tool databases. The corresponding change in the expression level of microRNAs in the DEGs was also predicted using miRWalk and TargetScan, and the expression profiles were analyzed using OncomiR. Finally, the expression of novel DEGs were validated in Chinese breast cancer tissues by RT-qPCR. Results: A total of 46 DEGs were identified, and GO analysis revealed that these genes were mainly associated with biological processes involved in fatty acid, lipid localization, and regulation of lipid metabolism. Two novel biomarkers, ADH1A and IGSF10, and 4 other genes ( APOD, KIT, RBP4, and SFRP1) that were implicated in the prognosis and pathogenesis of breast cancer, exhibited low expression levels in breast cancer tissues. Besides, 14/25 microRNAs targeting 6 genes were first predicted to be associated with breast cancer prognosis. RT-qPCR results of ADH1A and IGSF10 expression in Chinese breast cancer tissues were consistent with the database analysis and showed significant down-regulation. Conclusion: ADH1A, IGSF10, and the 14 microRNAs were found to be potential novel biomarkers for the diagnosis, treatment, and prognosis of breast cancer.
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Nikitina, Irina L., Leyla R. Sarakaeva, Anna A. Kostareva, and Elena K. Kudryashova. "Clinical heterogeneity and molecular genetic causes in a cohort of patients with disorders/differences of sex development." Pediatrics. Consilium Medicum, no. 2 (June 15, 2021): 194–202. http://dx.doi.org/10.26442/26586630.2021.2.200903.
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Background. Disorders of sex development (DSD) are a group of rare congenital conditions. Clinical management of patients with DSD is often difficult and requires multidisciplinary approach.
Aim. Analysis of the frequency of establishing genetic causes in various forms of DSD by using an original targeted sequencing panel with subsequent establishment of associations of the identified genetic variants with the nature of clinical manifestations.
Materials and methods. Conducted a clinical examination, karyotype analysis followed by the next generation sequencing (NGS) using MiSeq (Illumina) with the twenty-eight patients with different forms of 46, XY DSD were included. We designed HaloPlex (Agilent) gene panel that included coding regions of 80 candidate genes associated with DSD. All variants identified by NGS were confirmed by Sanger sequencing. We performed bioinformatics analysis using OMIM, 1000 genomes, ESP6500, Genome Aggregation Database projects. To assess the clinical significance of the identified variants we used ClinVar database and American College of Medical Genetics and Genomics criteria.
Results. Out of 28 patients pathogenic, likely pathogenic, variants with unknown significance were identified in 11 patients (39%). In combination with clinical phenotype these variants were determined as causative for DSD. Nine patients (82%) had likely causative variants in one gene (of monogenic origin), while 18% had variants in two genes simultaneously (of oligogenic origin). 43% of the identified gene variants have not been previously reported. The variants in NR5A1 were associated with gonadal dysgenesis in two patients; the variants in MAP3K1 were also found in another two patients with gonadal dysgenesis, variants in AR in three patients with CAIS, variant in MAMLD1 was associated with proximal form of hypospadias, variant in CYP17A1 was associated with testosterone biosynthetic defect. Among the two patients with variants of oligogenic origin, one had variants in MAP3K1 and MAMLD1 genes and was clinically characterized by hypospadias; the second had variants in AR and SEMA3A and was diagnosed with PAIS. There were also two patients with variants in NR5A1 of familial inheritance.
Conclusion. NGS-based targeted sequencing is a promising technique to improve the differential diagnosis, genetic counseling and management strategies for patients with DSD. Complex clinical examination followed by molecular genetic analysis improves the diagnosis, genetic counseling, and management strategies for patients with DSD including the assignment of sex of rearing.
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Cheng, Q., X. Chen, H. Wu, and Y. Du. "AB0042 THREE HEMATOLOGIC/IMMUNE SYSTEM-SPECIFIC EXPRESSED GENES ARE CONSIDERED AS THE POTENTIAL BIOMARKERS FOR THE DIAGNOSIS OF EARLY RHEUMATOID ARTHRITIS THROUGH BIOINFORMATICS ANALYSIS." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 1053.1–1054. http://dx.doi.org/10.1136/annrheumdis-2021-eular.135.
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Background:Rheumatoid arthritis (RA) is a common chronic autoimmune connective tissue disease that mainly involves the joints. The incidence of RA is 5 to 10 per 1000 people[1]. Early diagnosis and treatment of RA can effectively prevent disease progression, joint damage, and other complications in 90% of patients[2]. At present, serum biomarkers used in the diagnosis of established RA are rheumatoid factor and anti-cyclic citrullinated peptide antibody[3]. However, early RA especially serum RF and anti-CCP antibody-negative is difficult to diagnose due to the lack of effective biomarkers. Therefore, it is vital to identify new and effective biomarkers for the early diagnosis and treatment of RA.Objectives:This study aimed to identify new biomarkers and mechanisms for RA disease progression at the transcriptome level through a combination of microarray and bioinformatics analyses.Methods:Microarray datasets for synovial tissue in RA or osteoarthritis (OA) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by R software. Tissue/organ-specific genes were recognized by BioGPS. Enrichment analyses were performed and protein-protein interaction (PPI) networks were constructed to understand the functions and enriched pathways of DEGs and to identify hub genes. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. Biomarkers with high diagnostic value for the early diagnosis of RA were validated by GEO datasets. The ggpubr package was used to perform statistical analyses with Student’s t-test.Results:A total of 275 DEGs were identified between 16 RA samples and 10 OA samples from the datasets GSE77298 and GSE82107. Among these DEGs, 71 tissue/organ-specific expressed genes were recognized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that DEGs are mostly enriched in immune response, immune-related biological process, immune system, and cytokine signal pathways. Fifteen hub genes and gene cluster modules were identified by Cytoscape. Eight haematologic/immune system-specific expressed hub genes were verified by GEO datasets. GZMA, PRC1, and TTK may be biomarkers for diagnosis of early RA through combined the analysis of the verification results and the receiver operating characteristic (ROC) curve. NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158- miR-212-3p/miR-132-3p/miR-129-5p-TTK might be potential RNA regulatory pathways to regulate the disease progression of early RA.Conclusion:This work identified three haematologic/immune system-specific expressed genes, namely, GZMA, PRC1, and TTK, as potential biomarkers for the early diagnosis and treatment of RA and provided insight into the mechanisms of disease development in RA at the transcriptome level. In addition, we proposed that NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK are potential RNA regulatory pathways that control disease progression in early RA.References:[1]Smolen JS, Aletaha D, McInnes IB: Rheumatoid arthritis.Lancet 2016, 388:2023-2038.[2]Aletaha D, Smolen JS: Diagnosis and Management of Rheumatoid Arthritis: A Review.Jama 2018, 320:1360-1372.[3]Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO, 3rd, Birnbaum NS, Burmester GR, Bykerk VP, Cohen MD, et al: 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative.Arthritis Rheum 2010, 62:2569-2581.Disclosure of Interests:None declared
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Mavrich, Travis N., Christian Gauthier, Lawrence Abad, Charles A. Bowman, Steven G. Cresawn, and Graham F. Hatfull. "pdm_utils: a SEA-PHAGES MySQL phage database management toolkit." Bioinformatics, November 23, 2020. http://dx.doi.org/10.1093/bioinformatics/btaa983.
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Abstract Summary Bacteriophages (phages) are incredibly abundant and genetically diverse. The volume of phage genomics data is rapidly increasing, driven in part by the SEA-PHAGES program, which isolates, sequences and manually annotates hundreds of phage genomes each year. With an ever-expanding genomics dataset, there are many opportunities for generating new biological insights through comparative genomic and bioinformatic analyses. As a result, there is a growing need to be able to store, update, explore and analyze phage genomics data. The package pdm_utils provides a collection of tools for MySQL phage database management designed to meet specific needs in the SEA-PHAGES program and phage genomics generally. Availability and implementation https://pypi.org/project/pdm-utils/.
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Stanton, Richard A., Nicholas Vlachos, and Alison Laufer Halpin. "GAMMA: a tool for the rapid identification, classification and annotation of translated gene matches from sequencing data." Bioinformatics, August 20, 2021. http://dx.doi.org/10.1093/bioinformatics/btab607.
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Abstract Motivation Tools used to identify genes in microbial sequences using a reference database generally report matches as a percent identity, which can be difficult to interpret in cases with <100% sequence identity, as changes to specific amino acids can have dramatic effects on protein function, such as when they occur in substrate binding regions or enzyme active sites, which in turn can have dramatic effects on phenotypes like antimicrobial resistance or virulence. Results Here, we present GAMMA, an open-source tool for Gene Allele Mutation Microbial Assessment, which uses protein coding-level identity to make gene calls from any gene database and generates a classification (e.g. mutant, truncation) and translated annotation (e.g. Y190S mutation, truncation at residue 110) for these calls. GAMMA accurately called antimicrobial resistance genes from a large set of genomes faster than three other tools. It can also be used with any gene database, as we demonstrated by identifying virulence genes in the same genome set. Because of its speed and flexibility, GAMMA can be used to rapidly find and annotate any gene matches of interest in microbial sequencing data. Availability and implementation GAMMA is freely available as a Bioconda package (https://bioconda.github.io/recipes/gamma/README.html) and as a command line script (https://github.com/rastanton/GAMMA). Supplementary information Supplementary data are available at Bioinformatics online.
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Lind, Abigail L., and Katherine S. Pollard. "Accurate and sensitive detection of microbial eukaryotes from whole metagenome shotgun sequencing." Microbiome 9, no. 1 (March 3, 2021). http://dx.doi.org/10.1186/s40168-021-01015-y.
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Abstract Background Microbial eukaryotes are found alongside bacteria and archaea in natural microbial systems, including host-associated microbiomes. While microbial eukaryotes are critical to these communities, they are challenging to study with shotgun sequencing techniques and are therefore often excluded. Results Here, we present EukDetect, a bioinformatics method to identify eukaryotes in shotgun metagenomic sequencing data. Our approach uses a database of 521,824 universal marker genes from 241 conserved gene families, which we curated from 3713 fungal, protist, non-vertebrate metazoan, and non-streptophyte archaeplastida genomes and transcriptomes. EukDetect has a broad taxonomic coverage of microbial eukaryotes, performs well on low-abundance and closely related species, and is resilient against bacterial contamination in eukaryotic genomes. Using EukDetect, we describe the spatial distribution of eukaryotes along the human gastrointestinal tract, showing that fungi and protists are present in the lumen and mucosa throughout the large intestine. We discover that there is a succession of eukaryotes that colonize the human gut during the first years of life, mirroring patterns of developmental succession observed in gut bacteria. By comparing DNA and RNA sequencing of paired samples from human stool, we find that many eukaryotes continue active transcription after passage through the gut, though some do not, suggesting they are dormant or nonviable. We analyze metagenomic data from the Baltic Sea and find that eukaryotes differ across locations and salinity gradients. Finally, we observe eukaryotes in Arabidopsis leaf samples, many of which are not identifiable from public protein databases. Conclusions EukDetect provides an automated and reliable way to characterize eukaryotes in shotgun sequencing datasets from diverse microbiomes. We demonstrate that it enables discoveries that would be missed or clouded by false positives with standard shotgun sequence analysis. EukDetect will greatly advance our understanding of how microbial eukaryotes contribute to microbiomes.
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Whon, Tae Woong, Seung Woo Ahn, Sungjin Yang, Joon Yong Kim, Yeon Bee Kim, Yujin Kim, Ji-Man Hong, et al. "ODFM, an omics data resource from microorganisms associated with fermented foods." Scientific Data 8, no. 1 (April 20, 2021). http://dx.doi.org/10.1038/s41597-021-00895-x.
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AbstractODFM is a data management system that integrates comprehensive omics information for microorganisms associated with various fermented foods, additive ingredients, and seasonings (e.g. kimchi, Korean fermented vegetables, fermented seafood, solar salt, soybean paste, vinegar, beer, cheese, sake, and yogurt). The ODFM archives genome, metagenome, metataxonome, and (meta)transcriptome sequences of fermented food-associated bacteria, archaea, eukaryotic microorganisms, and viruses; 131 bacterial, 38 archaeal, and 28 eukaryotic genomes are now available to users. The ODFM provides both the Basic Local Alignment Search Tool search-based local alignment function as well as average nucleotide identity-based genetic relatedness measurement, enabling gene diversity and taxonomic analyses of an input query against the database. Genome sequences and annotation results of microorganisms are directly downloadable, and the microbial strains registered in the archive library will be available from our culture collection of fermented food-associated microorganisms. The ODFM is a comprehensive database that covers the genomes of an entire microbiome within a specific food ecosystem, providing basic information to evaluate microbial isolates as candidate fermentation starters for fermented food production.
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Wilson, Michael R., Greg Fedewa, Mark D. Stenglein, Judith Olejnik, Linda J. Rennick, Sham Nambulli, Friederike Feldmann, et al. "Multiplexed Metagenomic Deep Sequencing To Analyze the Composition of High-Priority Pathogen Reagents." mSystems 1, no. 4 (July 19, 2016). http://dx.doi.org/10.1128/msystems.00058-16.
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ABSTRACT Both the integrity and reproducibility of experiments using select agents depend in large part on unbiased validation to ensure the correct identity and purity of the species in question. Metagenomic deep sequencing (MDS) provides the required level of validation by allowing for an unbiased and comprehensive assessment of all the microbes in a laboratory stock. Laboratories studying high-priority pathogens need comprehensive methods to confirm microbial species and strains while also detecting contamination. Metagenomic deep sequencing (MDS) inventories nucleic acids present in laboratory stocks, providing an unbiased assessment of pathogen identity, the extent of genomic variation, and the presence of contaminants. Double-stranded cDNA MDS libraries were constructed from RNA extracted from in vitro-passaged stocks of six viruses (La Crosse virus, Ebola virus, canine distemper virus, measles virus, human respiratory syncytial virus, and vesicular stomatitis virus). Each library was dual indexed and pooled for sequencing. A custom bioinformatics pipeline determined the organisms present in each sample in a blinded fashion. Single nucleotide variant (SNV) analysis identified viral isolates. We confirmed that (i) each sample contained the expected microbe, (ii) dual indexing of the samples minimized false assignments of individual sequences, (iii) multiple viral and bacterial contaminants were present, and (iv) SNV analysis of the viral genomes allowed precise identification of the viral isolates. MDS can be multiplexed to allow simultaneous and unbiased interrogation of mixed microbial cultures and (i) confirm pathogen identity, (ii) characterize the extent of genomic variation, (iii) confirm the cell line used for virus propagation, and (iv) assess for contaminating microbes. These assessments ensure the true composition of these high-priority reagents and generate a comprehensive database of microbial genomes studied in each facility. MDS can serve as an integral part of a pathogen-tracking program which in turn will enhance sample security and increase experimental rigor and precision. IMPORTANCE Both the integrity and reproducibility of experiments using select agents depend in large part on unbiased validation to ensure the correct identity and purity of the species in question. Metagenomic deep sequencing (MDS) provides the required level of validation by allowing for an unbiased and comprehensive assessment of all the microbes in a laboratory stock.
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Petruschke, Hannes, Christian Schori, Sebastian Canzler, Sarah Riesbeck, Anja Poehlein, Rolf Daniel, Daniel Frei, et al. "Discovery of novel community-relevant small proteins in a simplified human intestinal microbiome." Microbiome 9, no. 1 (February 23, 2021). http://dx.doi.org/10.1186/s40168-020-00981-z.
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Abstract Background The intestinal microbiota plays a crucial role in protecting the host from pathogenic microbes, modulating immunity and regulating metabolic processes. We studied the simplified human intestinal microbiota (SIHUMIx) consisting of eight bacterial species with a particular focus on the discovery of novel small proteins with less than 100 amino acids (= sProteins), some of which may contribute to shape the simplified human intestinal microbiota. Although sProteins carry out a wide range of important functions, they are still often missed in genome annotations, and little is known about their structure and function in individual microbes and especially in microbial communities. Results We created a multi-species integrated proteogenomics search database (iPtgxDB) to enable a comprehensive identification of novel sProteins. Six of the eight SIHUMIx species, for which no complete genomes were available, were sequenced and de novo assembled. Several proteomics approaches including two earlier optimized sProtein enrichment strategies were applied to specifically increase the chances for novel sProtein discovery. The search of tandem mass spectrometry (MS/MS) data against the multi-species iPtgxDB enabled the identification of 31 novel sProteins, of which the expression of 30 was supported by metatranscriptomics data. Using synthetic peptides, we were able to validate the expression of 25 novel sProteins. The comparison of sProtein expression in each single strain versus a multi-species community cultivation showed that six of these sProteins were only identified in the SIHUMIx community indicating a potentially important role of sProteins in the organization of microbial communities. Two of these novel sProteins have a potential antimicrobial function. Metabolic modelling revealed that a third sProtein is located in a genomic region encoding several enzymes relevant for the community metabolism within SIHUMIx. Conclusions We outline an integrated experimental and bioinformatics workflow for the discovery of novel sProteins in a simplified intestinal model system that can be generically applied to other microbial communities. The further analysis of novel sProteins uniquely expressed in the SIHUMIx multi-species community is expected to enable new insights into the role of sProteins on the functionality of bacterial communities such as those of the human intestinal tract.
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Zhao, Jie, Hongjie Gao, and Yun He. "Data mining of differentially expressed genes in epithelial ovarian carcinoma: Implications in precise medicine." Current Topics in Medicinal Chemistry 21 (July 8, 2021). http://dx.doi.org/10.2174/1568026621666210708093649.
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Background: Epithelial ovarian carcinoma (EOC) is a ubiquitous gynecological malignancy with complicated pathogenesis. Genetic risk factors and pathways involved in the prognosis of this cancer are not yet understood completely. Determining genetic markers with diagnostic and prognostic values would pave the way for efficient management of cancer. Objective: This study aimed to investigate the genes and the regulatory networks involved in the occurrence and prognosis of EOC through different bioinformatics analysis tools. In addition, recent advances in using bioinformatic analysis approach based on the genes and regulatory networks, particularly differentially expressed genes (DEGs), in improving the diagnosis and prognosis of EOC are discussed. Methods: The gene expression profiles of GSE18520, GSE54388, and GSE27651 were downloaded from the Gene Expression Omnibus (GEO) database and further analyzed with different analyses in R language. Current literature on using bioinformatics based on DEGs and associated regulatory networks to improve the diagnosis and prognosis of EOC were reviewed. Results: Analyses of the gene expression levels between the malignant tissue against normal tissue unveiled 163 DEGs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on the target genes using clusterProfiler package, and Cytoscape package was employed to assess the protein interaction network of these genes. The protein-protein interaction network was analyzed using the CytoHubba plug-in to identify 20 hub genes. In addition, we analyzed the prognosis of the hub genes using the Kaplan-Meier survival analysis that revealed evident differences in the prognosis of 13 genes. The malignant tissues exhibited a differential expression of 12 genes against healthy tissues, as shown by Gene Expression Profiling Interactive Analysis (GEPIA) analysis. Conclusion: Findings of this study revealed 12 genes to be significantly up-regulated, and the prognosis was significantly different, which could be employed to potentially target EOC in clinical practice.
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Juma, Meshack, Arun Sankaradoss, Redcliff Ndombi, Patrick Mwaura, Tina Damodar, Junaid Nazir, Awadhesh Pandit, et al. "Antimicrobial Resistance Profiling and Phylogenetic Analysis of Neisseria gonorrhoeae Clinical Isolates From Kenya in a Resource-Limited Setting." Frontiers in Microbiology 12 (July 27, 2021). http://dx.doi.org/10.3389/fmicb.2021.647565.
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BackgroundAfrica has one of the highest incidences of gonorrhea. Neisseria gonorrhoeae is gaining resistance to most of the available antibiotics, compromising treatment across the world. Whole-genome sequencing (WGS) is an efficient way of predicting AMR determinants and their spread in the population. Recent advances in next-generation sequencing technologies like Oxford Nanopore Technology (ONT) have helped in the generation of longer reads of DNA in a shorter duration with lower cost. Increasing accuracy of base-calling algorithms, high throughput, error-correction strategies, and ease of using the mobile sequencer MinION in remote areas lead to its adoption for routine microbial genome sequencing. To investigate whether MinION-only sequencing is sufficient for WGS and downstream analysis in resource-limited settings, we sequenced the genomes of 14 suspected N. gonorrhoeae isolates from Nairobi, Kenya.MethodsUsing WGS, the isolates were confirmed to be cases of N. gonorrhoeae (n = 9), and there were three co-occurrences of N. gonorrhoeae with Moraxella osloensis and N. meningitidis (n = 2). N. meningitidis has been implicated in sexually transmitted infections in recent years. The near-complete N. gonorrhoeae genomes (n = 10) were analyzed further for mutations/factors causing AMR using an in-house database of mutations curated from the literature.ResultsWe observe that ciprofloxacin resistance is associated with multiple mutations in both gyrA and parC. Mutations conferring tetracycline (rpsJ) and sulfonamide (folP) resistance and plasmids encoding beta-lactamase were seen in all the strains, and tet(M)-containing plasmids were identified in nine strains. Phylogenetic analysis clustered the 10 isolates into clades containing previously sequenced genomes from Kenya and countries across the world. Based on homology modeling of AMR targets, we see that the mutations in GyrA and ParC disrupt the hydrogen bonding with quinolone drugs and mutations in FolP may affect interaction with the antibiotic.ConclusionHere, we demonstrate the utility of mobile DNA sequencing technology in producing a consensus genome for sequence typing and detection of genetic determinants of AMR. The workflow followed in the study, including AMR mutation dataset creation and the genome identification, assembly, and analysis, can be used for any clinical isolate. Further studies are required to determine the utility of real-time sequencing in outbreak investigations, diagnosis, and management of infections, especially in resource-limited settings.
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Shahzad, Khuram, Vincenzo Lopreiato, Yusheng Liang, Erminio Trevisi, Johan S. Osorio, Chuang Xu, and Juan J. Loor. "Hepatic metabolomics and transcriptomics to study susceptibility to ketosis in response to prepartal nutritional management." Journal of Animal Science and Biotechnology 10, no. 1 (December 2019). http://dx.doi.org/10.1186/s40104-019-0404-z.
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Abstract Background Ketosis in dairy cows is associated with body fat mobilization during the peripartal period. Sub-clinical and clinical ketosis arise more frequently in cows that are overfed energy during the entire dry (last 50 to 45 days prior to parturition) or close-up period (last ~ 28 days prepartum). Methods A retrospective analysis was performed on 12 cows from a larger cohort that were fed a higher-energy diet [1.54 Mcal/kg of dry matter (DM); 35.9% of DM corn silage and 13% of DM ground corn] during the close-up dry period, of which 6 did not develop clinical ketosis (OVE, 0.83 mmol/L plasma hydroxybutyrate; BHB) and 6 were diagnosed with clinical ketosis (KET, 1.4 mmol/L BHB) during the first week postpartum. A whole-transcriptome bovine microarray (Agilent Technologies) and metabolomics (GC-MS, LC-MS; Metabolon® Inc.) were used to perform transcript and metabolite profiling of liver tissue harvested at − 10 days relative to parturition which allowed to establish potential associations between prepartal transcriptome/metabolome profiles and susceptibility to clinical ketosis postpartum. Results Cows in KET had greater (P = 0.01) overall body weight between − 2 and 1 week around parturition, but similar body condition score than OVE. Although dry matter intake (DMI) did not differ prepartum, KET cows had lower (P < 0.01) DMI and similar milk yield as OVE cows during the first week postpartum. Transcriptome analysis revealed a total of 3065 differentially expressed genes (DEG; P ≤ 0.05) in KET. Metabolomics identified 15 out of 313 total biochemical compounds significantly affected (P ≤ 0.10) in KET. Among those, greater concentrations (P ≤ 0.06, + 2.3-fold) of glycochenodeoxycholate in KET cows also have been detected in humans developing non-alcoholic fatty liver disease. Bioinformatics analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database and the DEG revealed that, among the top 20 most-impacted metabolic pathway categories in KET, 65% were overall downregulated. Those included ‘Metabolism of cofactors and vitamins’, ‘Biosynthesis of other secondary metabolites’, ‘Lipid’, ‘Carbohydrate’, and ‘Glycan biosynthesis and metabolism’. The lower relative concentration of glucose-6-phosphate and marked downregulation of fructose-1,6-bisphosphatase 2 and pyruvate dehydrogenase kinase 4 support a strong impairment in gluconeogenesis in prepartal liver of cows developing KET postpartum. Among the top 20 most-impacted non-metabolic pathways, 85% were downregulated. Pathways such as ‘mTOR signalling’ and ‘Insulin signalling’ were among those. ‘Ribosome’, ‘Nucleotide excision repair’, and ‘Adherens junctions’ were the only upregulated pathways in cows with KET. Conclusions The combined data analyses revealed more extensive alterations of the prepartal liver transcriptome than metabolome in cows overfed energy and developing ketosis postpartum. The causative link between these tissue-level adaptations and onset of clinical ketosis needs to be studied further.