Journal articles: 'Microbial bioinformatics'

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Pallen, Mark J. "Microbial bioinformatics 2020." Microbial Biotechnology 9, no. 5 (2016): 681–86. http://dx.doi.org/10.1111/1751-7915.12389.

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Du, Rui Fang, Jing Yu Li, Jian Li Liu, and Ji Zhao Zhao. "Application of Bioinformatics in Microbial Ecology." Advanced Materials Research 955-959 (June 2014): 276–80. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.276.

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The major goal of microbial ecology is to study the structure and function of complex microbial communities. Various bioinformatics software were employed to handle a large number of genomic information emerged by using high throughput sequencing. This paper summarizes application of bioinformatics in microbial ecology and their corresponding software used in α, β-diversity studies; and finally expounds the important roles in establishment of four synthesis databases.

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Mbareche, Hamza, Nathan Dumont-Leblond, Guillaume J. Bilodeau, and Caroline Duchaine. "An Overview of Bioinformatics Tools for DNA Meta-Barcoding Analysis of Microbial Communities of Bioaerosols: Digest for Microbiologists." Life 10, no. 9 (2020): 185. http://dx.doi.org/10.3390/life10090185.

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High-throughput DNA sequencing (HTS) has changed our understanding of the microbial composition present in a wide range of environments. Applying HTS methods to air samples from different environments allows the identification and quantification (relative abundance) of the microorganisms present and gives a better understanding of human exposure to indoor and outdoor bioaerosols. To make full use of the avalanche of information made available by these sequences, repeated measurements must be taken, community composition described, error estimates made, correlations of microbiota with covariates (variables) must be examined, and increasingly sophisticated statistical tests must be conducted, all by using bioinformatics tools. Knowing which analysis to conduct and which tools to apply remains confusing for bioaerosol scientists, as a litany of tools and data resources are now available for characterizing microbial communities. The goal of this review paper is to offer a guided tour through the bioinformatics tools that are useful in studying the microbial ecology of bioaerosols. This work explains microbial ecology features like alpha and beta diversity, multivariate analyses, differential abundances, taxonomic analyses, visualization tools and statistical tests using bioinformatics tools for bioaerosol scientists new to the field. It illustrates and promotes the use of selected bioinformatic tools in the study of bioaerosols and serves as a good source for learning the “dos and don’ts” involved in conducting a precise microbial ecology study.

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Tabassum Khan, Nida. "The Emerging Role of Bioinformatics in Biotechnology." Journal of Biotechnology and Biomedical Science 1, no. 3 (2018): 13–24. http://dx.doi.org/10.14302/issn.2576-6694.jbbs-18-2173.

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Bioinformatic tools is widely used to manage the enormous genomic and proteomic data involving DNA/protein sequences management, drug designing, homology modelling, motif/domain prediction ,docking, annotation and dynamic simulation etc. Bioinformatics offers a wide range of applications in numerous disciplines such as genomics. Proteomics, comparative genomics, nutrigenomics, microbial genome, biodefense, forensics etc. Thus it offers promising future to accelerate scientific research in biotechnology

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Alkema, Wynand, Jos Boekhorst, Michiel Wels, and Sacha A. F. T. van Hijum. "Microbial bioinformatics for food safety and production." Briefings in Bioinformatics 17, no. 2 (2015): 283–92. http://dx.doi.org/10.1093/bib/bbv034.

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Anand, Deepsikha, Jeya Nasim, Sangeeta Yadav, and Dinesh Yadav. "Bioinformatics Insights Into Microbial Xylanase Protein Sequences." Biosciences, Biotechnology Research Asia 15, no. 2 (2018): 275–94. http://dx.doi.org/10.13005/bbra/2631.

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Microbial xylanases represents an industrially important group of enzymes associated with hydrolysis of xylan, a major hemicellulosic component of plant cell walls. A total of 122 protein sequences comprising of 58 fungal, 25 bacterial, 19actinomycetes and 20 yeasts xylanaseswere retrieved from NCBI, GenBank databases. These sequences were in-silico characterized for homology,sequence alignment, phylogenetic tree construction, motif assessment and physio-chemical attributes. The amino acid residues ranged from 188 to 362, molecular weights were in the range of 20.3 to 39.7 kDa and pI ranged from 3.93 to 9.69. The aliphatic index revealed comparatively less thermostability and negative GRAVY indicated that xylanasesarehydrophilicirrespective of the source organisms.Several conserved amino acid residues associated with catalytic domain of the enzyme were observed while different microbial sources also revealed few conserved amino acid residues. The comprehensive phylogenetic tree indicatedsevenorganismsspecific,distinct major clusters,designated as A, B, C, D, E, F and G. The MEME based analysis of 10 motifs indicated predominance of motifs specific to GH11 family and one of the motif designated as motif 3 with sequence GTVTSDGGTYDIYTTTRTNAP was found to be present in most of the xylanases irrespective of the sources.Sequence analysis of microbial xylanases provides an opportunity to develop strategies for molecular cloning and expression of xylanase genes and also foridentifying sites for genetic manipulation for developing novel xylanases with desired features as per industrial needs.

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Carriço, J. A., M. Rossi, J. Moran-Gilad, G. Van Domselaar, and M. Ramirez. "A primer on microbial bioinformatics for nonbioinformaticians." Clinical Microbiology and Infection 24, no. 4 (2018): 342–49. http://dx.doi.org/10.1016/j.cmi.2017.12.015.

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Theil, Sebastien, and Etienne Rifa. "rANOMALY: AmplicoN wOrkflow for Microbial community AnaLYsis." F1000Research 10 (January 7, 2021): 7. http://dx.doi.org/10.12688/f1000research.27268.1.

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Bioinformatic tools for marker gene sequencing data analysis are continuously and rapidly evolving, thus integrating most recent techniques and tools is challenging. We present an R package for data analysis of 16S and ITS amplicons based sequencing. This workflow is based on several R functions and performs automatic treatments from fastq sequence files to diversity and differential analysis with statistical validation. The main purpose of this package is to automate bioinformatic analysis, ensure reproducibility between projects, and to be flexible enough to quickly integrate new bioinformatic tools or statistical methods. rANOMALY is an easy to install and customizable R package, that uses amplicon sequence variants (ASV) level for microbial community characterization. It integrates all assets of the latest bioinformatics methods, such as better sequence tracking, decontamination from control samples, use of multiple reference databases for taxonomic annotation, all main ecological analysis for which we propose advanced statistical tests, and a cross-validated differential analysis by four different methods. Our package produces ready to publish figures, and all of its outputs are made to be integrated in Rmarkdown code to produce automated reports.

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Baldini, Federico, Almut Heinken, Laurent Heirendt, Stefania Magnusdottir, Ronan M. T. Fleming, and Ines Thiele. "The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities." Bioinformatics 35, no. 13 (2018): 2332–34. http://dx.doi.org/10.1093/bioinformatics/bty941.

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Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

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Sugawara, H., S. Miyazaki, J. Shimura, and Y. Ichiyanagi. "Bioinformatics tools for the study of microbial diversity." Journal of Industrial Microbiology & Biotechnology 17, no. 5-6 (1996): 490–97. http://dx.doi.org/10.1007/bf01574780.

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Costanza, Jole, Giovanni Carapezza, Claudio Angione, Pietro Lió, and Giuseppe Nicosia. "Robust design of microbial strains." Bioinformatics 28, no. 23 (2012): 3097–104. http://dx.doi.org/10.1093/bioinformatics/bts590.

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Goll, Johannes, Seesandra V. Rajagopala, Shen C. Shiau, Hank Wu, Brian T. Lamb, and Peter Uetz. "MPIDB: the microbial protein interaction database." Bioinformatics 24, no. 15 (2008): 1743–44. http://dx.doi.org/10.1093/bioinformatics/btn285.

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Tanaseichuk, Olga, James Borneman, and Tao Jiang. "Phylogeny-based classification of microbial communities." Bioinformatics 30, no. 4 (2013): 449–56. http://dx.doi.org/10.1093/bioinformatics/btt700.

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Petkau, Aaron, Matthew Stuart-Edwards, Paul Stothard, and Gary Van Domselaar. "Interactive microbial genome visualization with GView." Bioinformatics 26, no. 24 (2010): 3125–26. http://dx.doi.org/10.1093/bioinformatics/btq588.

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Akashi, H. "Metabolic economics and microbial proteome evolution." Bioinformatics 19, Suppl 2 (2003): ii15. http://dx.doi.org/10.1093/bioinformatics/btg1053.

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Petrosino, Joseph F., Sarah Highlander, Ruth Ann Luna, Richard A. Gibbs, and James Versalovic. "Metagenomic Pyrosequencing and Microbial Identification." Clinical Chemistry 55, no. 5 (2009): 856–66. http://dx.doi.org/10.1373/clinchem.2008.107565.

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Abstract Background: The Human Microbiome Project has ushered in a new era for human metagenomics and high-throughput next-generation sequencing strategies. Content: This review describes evolving strategies in metagenomics, with a special emphasis on the core technology of DNA pyrosequencing. The challenges of microbial identification in the context of microbial populations are discussed. The development of next-generation pyrosequencing strategies and the technical hurdles confronting these methodologies are addressed. Bioinformatics-related topics include taxonomic systems, sequence databases, sequence-alignment tools, and classifiers. DNA sequencing based on 16S rRNA genes or entire genomes is summarized with respect to potential pyrosequencing applications. Summary: Both the approach of 16S rDNA amplicon sequencing and the whole-genome sequencing approach may be useful for human metagenomics, and numerous bioinformatics tools are being deployed to tackle such vast amounts of microbiological sequence diversity. Metagenomics, or genetic studies of microbial communities, may ultimately contribute to a more comprehensive understanding of human health, disease susceptibilities, and the pathophysiology of infectious and immune-mediated diseases.

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Parmen, Adibah, MOHD NOOR MAT ISA, FARAH FADWA BENBELGACEM, Hamzah Mohd Salleh, and Ibrahim Ali Noorbatcha. "COMPARATIVE METAGENOMICS ANALYSIS OF PALM OIL MILL EFFLUENT (POME) USING THREE DIFFERENT BIOINFORMATICS PIPELINES." IIUM Engineering Journal 20, no. 1 (2019): 1–11. http://dx.doi.org/10.31436/iiumej.v20i1.909.

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ABSTRACT: The substantial cost reduction and massive production of next-generation sequencing (NGS) data have contributed to the progress in the rapid growth of metagenomics. However, production of the massive amount of data by NGS has revealed the challenges in handling the existing bioinformatics tools related to metagenomics. Therefore, in this research we have investigated an equal set of DNA metagenomics data from palm oil mill effluent (POME) sample using three different freeware bioinformatics pipelines’ websites of metagenomics RAST server (MG-RAST), Integrated Microbial Genomes with Microbiome Samples (IMG/M) and European Bioinformatics Institute (EBI) Metagenomics, in term of the taxonomic assignment and functional analysis. We found that MG-RAST is the quickest among these three pipelines. However, in term of analysis of results, IMG/M provides more variety of phylum with wider percent identities for taxonomical assignment and IMG/M provides the highest carbohydrates, amino acids, lipids, and coenzymes transport and metabolism functional annotation beside the highest in total number of glycoside hydrolase enzymes. Next, in identifying the conserved domain and family involved, EBI Metagenomics would be much more appropriate. All the three bioinformatics pipelines have their own specialties and can be used alternately or at the same time based on the user’s functional preference. ABSTRAK: Pengurangan kos dalam skala besar dan pengeluaran data ‘next-generation sequencing’ (NGS) secara besar-besaran telah menyumbang kepada pertumbuhan pesat metagenomik. Walau bagaimanapun, pengeluaran data dalam skala yang besar oleh NGS telah menimbulkan cabaran dalam mengendalikan alat-alat bioinformatika yang sedia ada berkaitan dengan metagenomik. Justeru itu, dalam kajian ini, kami telah menyiasat satu set data metagenomik DNA yang sama dari sampel effluen kilang minyak sawit dengan menggunakan tiga laman web bioinformatik percuma iaitu dari laman web ‘metagenomics RAST server’ (MG-RAST), ‘Integrated Microbial Genomes with Microbiome Samples’ (IMG/M) dan ‘European Bioinformatics Institute’ (EBI) Metagenomics dari segi taksonomi dan analisis fungsi. Kami mendapati bahawa MG-RAST ialah yang paling cepat di antara ketiga-tiga ‘pipeline’, tetapi mengikut keputusan analisa, IMG/M mengeluarkan maklumat philum yang lebih pelbagai bersama peratus identiti yang lebih luas berbanding yang lain untuk pembahagian taksonomi dan IMG/M juga mempunyai bacaan tertinggi dalam hampir semua anotasi fungsional karbohidrat, amino asid, lipid, dan koenzima pengangkutan dan metabolisma malah juga paling tinggi dalam jumlah enzim hidrolase glikosida. Kemudian, untuk mengenal pasti ‘domain’ terpelihara dan keluarga yang terlibat, EBI metagenomics lebih bersesuaian. Ketiga-tiga saluran ‘bioinformatics pipeline’ mempunyai keistimewaan mereka yang tersendiri dan boleh digunakan bersilih ganti dalam masa yang sama berdasarkan pilihan fungsi penggun.

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Chen, Kevin, and Lior Pachter. "Bioinformatics for Whole-Genome Shotgun Sequencing of Microbial Communities." PLoS Computational Biology 1, no. 2 (2005): e24. http://dx.doi.org/10.1371/journal.pcbi.0010024.

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Kerkhoven, R., F. H. J. van Enckevort, J. Boekhorst, D. Molenaar, and R. J. Siezen. "Visualization for genomics: the Microbial Genome Viewer." Bioinformatics 20, no. 11 (2004): 1812–14. http://dx.doi.org/10.1093/bioinformatics/bth159.

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Lim, Kun Ming Kenneth, Chenhao Li, Kern Rei Chng, and Niranjan Nagarajan. "@MInter: automated text-mining of microbial interactions." Bioinformatics 32, no. 19 (2016): 2981–87. http://dx.doi.org/10.1093/bioinformatics/btw357.

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Piccolo, Brian D., Umesh D. Wankhade, Sree V. Chintapalli, Sudeepa Bhattacharyya, Luo Chunqiao, and Kartik Shankar. "Dynamic assessment of microbial ecology (DAME): a web app for interactive analysis and visualization of microbial sequencing data." Bioinformatics 34, no. 6 (2017): 1050–52. http://dx.doi.org/10.1093/bioinformatics/btx686.

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Tavakoli, Sahar, and Shibu Yooseph. "Learning a mixture of microbial networks using minorization–maximization." Bioinformatics 35, no. 14 (2019): i23—i30. http://dx.doi.org/10.1093/bioinformatics/btz370.

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Abstract Motivation The interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is typically constructed from a sample-taxa count matrix that is obtained by sequencing multiple biological samples and identifying taxa counts. From large-scale microbiome studies, it is evident that microbial community compositions and interactions are impacted by environmental and/or host factors. Thus, it is not unreasonable to expect that a sample-taxa matrix generated as part of a large study involving multiple environmental or clinical parameters can be associated with more than one microbial network. However, to our knowledge, microbial network inference methods proposed thus far assume that the sample-taxa matrix is associated with a single network. Results We present a mixture model framework to address the scenario when the sample-taxa matrix is associated with K microbial networks. This count matrix is modeled using a mixture of K Multivariate Poisson Log-Normal distributions and parameters are estimated using a maximum likelihood framework. Our parameter estimation algorithm is based on the minorization–maximization principle combined with gradient ascent and block updates. Synthetic datasets were generated to assess the performance of our approach on absolute count data, compositional data and normalized data. We also addressed the recovery of sparse networks based on an l1-penalty model. Availability and implementation MixMPLN is implemented in R and is freely available at https://github.com/sahatava/MixMPLN. Supplementary information Supplementary data are available at Bioinformatics online.

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Hosoda, Shion, Tsukasa Fukunaga, and Michiaki Hamada. "Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model." Bioinformatics 37, Supplement_1 (2021): i16—i24. http://dx.doi.org/10.1093/bioinformatics/btab287.

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Abstract Motivation Accumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka–Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions. Results In this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota. Availability and implementation The C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato. Supplementary information Supplementary data are available at Bioinformatics online.

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Brittnacher, M. J., C. Fong, H. S. Hayden, M. A. Jacobs, M. Radey, and L. Rohmer. "PGAT: a multistrain analysis resource for microbial genomes." Bioinformatics 27, no. 17 (2011): 2429–30. http://dx.doi.org/10.1093/bioinformatics/btr418.

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MacDonald, Norman J., and Robert G. Beiko. "Efficient learning of microbial genotype–phenotype association rules." Bioinformatics 26, no. 15 (2010): 1834–40. http://dx.doi.org/10.1093/bioinformatics/btq305.

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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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Lacroix, Jean-Michel, and Marc C. Lavoie. "Microcomputer package for statistical analysis of microbial populations." Bioinformatics 3, no. 4 (1987): 309–12. http://dx.doi.org/10.1093/bioinformatics/3.4.309.

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Angly, Florent E., Christopher J. Fields, and Gene W. Tyson. "The Bio-Community Perl toolkit for microbial ecology." Bioinformatics 30, no. 13 (2014): 1926–27. http://dx.doi.org/10.1093/bioinformatics/btu130.

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Harn, Y. C., M. J. Powers, E. A. Shank, and V. Jojic. "Deconvolving molecular signatures of interactions between microbial colonies." Bioinformatics 31, no. 12 (2015): i142—i150. http://dx.doi.org/10.1093/bioinformatics/btv251.

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Chen, Li. "powmic: an R package for power assessment in microbiome case–control studies." Bioinformatics 36, no. 11 (2020): 3563–65. http://dx.doi.org/10.1093/bioinformatics/btaa197.

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Abstract Summary Power analysis is essential to decide the sample size of metagenomic sequencing experiments in a case–control study for identifying differentially abundant (DA) microbes. However, the complexity of microbial data characteristics, such as excessive zeros, over-dispersion, compositionality, intrinsically microbial correlations and variable sequencing depths, makes the power analysis particularly challenging because the analytical form is usually unavailable. Here, we develop a simulation-based power assessment strategy and R package powmic, which considers the complexity of microbial data characteristics. A real data example demonstrates the usage of powmic. Availability and implementation powmic R package and online tutorial are available at https://github.com/lichen-lab/powmic. Contact chen61@iu.edu Supplementary information Supplementary data are available at Bioinformatics online.

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Hiraoka, Satoshi, Ching-chia Yang, and Wataru Iwasaki. "Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond." Microbes and Environments 31, no. 3 (2016): 204–12. http://dx.doi.org/10.1264/jsme2.me16024.

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Starcevic, Antonio, Ena Melvan, Janko Diminic, et al. "Mass spectrometry, clinical proteomics and bioinformatics as microbial crime fighters." Journal of Biotechnology 185 (September 2014): S6. http://dx.doi.org/10.1016/j.jbiotec.2014.07.025.

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Thi Nhung, Doan, and Bui Van Ngoc. "Bioinformatic approaches for analysis of coral-associated bacteria using R programming language." Vietnam Journal of Biotechnology 18, no. 4 (2021): 733–43. http://dx.doi.org/10.15625/1811-4989/18/4/15320.

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Recent advances in metagenomics and bioinformatics allow the robust analysis of the composition and abundance of microbial communities, functional genes, and their metabolic pathways. So far, there has been a variety of computational/statistical tools or software for analyzing microbiome, the common problems that occurred in its implementation are, however, the lack of synchronization and compatibility of output/input data formats between such software. To overcome these challenges, in this study context, we aim to apply the DADA2 pipeline (written in R programming language) instead of using a set of different bioinformatics tools to create our own workflow for microbial community analysis in a continuous and synchronous manner. For the first effort, we tried to investigate the composition and abundance of coral-associated bacteria using their 16S rRNA gene amplicon sequences. The workflow or framework includes the following steps: data processing, sequence clustering, taxonomic assignment, and data visualization. Moreover, we also like to catch readers’ attention to the information about bacterial communities living in the ocean as most marine microorganisms are unculturable, especially residing in coral reefs, namely, bacteria are associated with the coral Acropora tenuis in this case. The outcomes obtained in this study suggest that the DADA2 pipeline written in R programming language is one of the potential bioinformatics approaches in the context of microbiome analysis other than using various software. Besides, our modifications for the workflow execution help researchers to illustrate metagenomic data more easily and systematically, elucidate the composition, abundance, diversity, and relationship between microorganism communities as well as to develop other bioinformatic tools more effectively.

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Ruan, Q., J. A. Steele, M. S. Schwalbach, J. A. Fuhrman, and F. Sun. "A dynamic programming algorithm for binning microbial community profiles." Bioinformatics 22, no. 12 (2006): 1508–14. http://dx.doi.org/10.1093/bioinformatics/btl114.

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Tamura, Makio, and Patrik D'haeseleer. "Microbial genotype–phenotype mapping by class association rule mining." Bioinformatics 24, no. 13 (2008): 1523–29. http://dx.doi.org/10.1093/bioinformatics/btn210.

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García-Jiménez, Beatriz, José Luis García, and Juan Nogales. "FLYCOP: metabolic modeling-based analysis and engineering microbial communities." Bioinformatics 34, no. 17 (2018): i954—i963. http://dx.doi.org/10.1093/bioinformatics/bty561.

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Leach, Alex L. B., James P. J. Chong, and Kelly R. Redeker. "SSuMMo: rapid analysis, comparison and visualization of microbial communities." Bioinformatics 28, no. 5 (2012): 679–86. http://dx.doi.org/10.1093/bioinformatics/bts017.

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Langille, M. G. I., M. R. Laird, W. W. L. Hsiao, T. A. Chiu, J. A. Eisen, and F. S. L. Brinkman. "MicrobeDB: a locally maintainable database of microbial genomic sequences." Bioinformatics 28, no. 14 (2012): 1947–48. http://dx.doi.org/10.1093/bioinformatics/bts273.

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Copeland, Wade K., Vandhana Krishnan, Daniel Beck, et al. "mcaGUI: microbial community analysis R-Graphical User Interface (GUI)." Bioinformatics 28, no. 16 (2012): 2198–99. http://dx.doi.org/10.1093/bioinformatics/bts338.

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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 (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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K.Bansal, A. "An automated comparative analysis of 17 complete microbial genomes." Bioinformatics 15, no. 11 (1999): 900–908. http://dx.doi.org/10.1093/bioinformatics/15.11.900.

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Ma, Terry, Di Xiao, and Xin Xing. "MetaBMF: a scalable binning algorithm for large-scale reference-free metagenomic studies." Bioinformatics 36, no. 2 (2019): 356–63. http://dx.doi.org/10.1093/bioinformatics/btz577.

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Abstract Motivation Metagenomics studies microbial genomes in an ecosystem such as the gastrointestinal tract of a human. Identification of novel microbial species and quantification of their distributional variations among different samples that are sequenced using next-generation-sequencing technology hold the key to the success of most metagenomic studies. To achieve these goals, we propose a simple yet powerful metagenomic binning method, MetaBMF. The method does not require prior knowledge of reference genomes and produces highly accurate results, even at a strain level. Thus, it can be broadly used to identify disease-related microbial organisms that are not well-studied. Results Mathematically, we count the number of mapped reads on each assembled genomic fragment cross different samples as our input matrix and propose a scalable stratified angle regression algorithm to factorize this count matrix into a product of a binary matrix and a nonnegative matrix. The binary matrix can be used to separate microbial species and the nonnegative matrix quantifies the species distributions in different samples. In simulation and empirical studies, we demonstrate that MetaBMF has a high binning accuracy. It can not only bin DNA fragments accurately at a species level but also at a strain level. As shown in our example, we can accurately identify the Shiga-toxigenic Escherichia coli O104: H4 strain which led to the 2011 German E.coli outbreak. Our efforts in these areas should lead to (i) fundamental advances in metagenomic binning, (ii) development and refinement of technology for the rapid identification and quantification of microbial distributions and (iii) finding of potential probiotics or reliable pathogenic bacterial strains. Availability and implementation The software is available at https://github.com/didi10384/MetaBMF.

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Schmedes, Sarah E., Antti Sajantila, and Bruce Budowle. "Expansion of Microbial Forensics." Journal of Clinical Microbiology 54, no. 8 (2016): 1964–74. http://dx.doi.org/10.1128/jcm.00046-16.

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Microbial forensics has been defined as the discipline of applying scientific methods to the analysis of evidence related to bioterrorism, biocrimes, hoaxes, or the accidental release of a biological agent or toxin for attribution purposes. Over the past 15 years, technology, particularly massively parallel sequencing, and bioinformatics advances now allow the characterization of microorganisms for a variety of human forensic applications, such as human identification, body fluid characterization, postmortem interval estimation, and biocrimes involving tracking of infectious agents. Thus, microbial forensics should be more broadly described as the discipline of applying scientific methods to the analysis of microbial evidence in criminal and civil cases for investigative purposes.

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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 (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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Hu, Gang-Qing, Xiaobin Zheng, Huai-Qiu Zhu, and Zhen-Su She. "Prediction of translation initiation site for microbial genomes with TriTISA." Bioinformatics 25, no. 1 (2008): 123–25. http://dx.doi.org/10.1093/bioinformatics/btn576.

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Carlson, Ross P. "Decomposition of complex microbial behaviors into resource-based stress responses." Bioinformatics 25, no. 1 (2008): 90–97. http://dx.doi.org/10.1093/bioinformatics/btn589.

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Albayrak, Levent, Kamil Khanipov, George Golovko, and Yuriy Fofanov. "Detection of multi-dimensional co-exclusion patterns in microbial communities." Bioinformatics 34, no. 21 (2018): 3695–701. http://dx.doi.org/10.1093/bioinformatics/bty414.

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Lees, John A., Marco Galardini, Stephen D. Bentley, Jeffrey N. Weiser, and Jukka Corander. "pyseer: a comprehensive tool for microbial pangenome-wide association studies." Bioinformatics 34, no. 24 (2018): 4310–12. http://dx.doi.org/10.1093/bioinformatics/bty539.

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Inman, Jason M., Granger G. Sutton, Erin Beck, Lauren M. Brinkac, Thomas H. Clarke, and Derrick E. Fouts. "Large-scale comparative analysis of microbial pan-genomes using PanOCT." Bioinformatics 35, no. 6 (2018): 1049–50. http://dx.doi.org/10.1093/bioinformatics/bty744.

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Bradley, Patrick H., and Katherine S. Pollard. "phylogenize: correcting for phylogeny reveals genes associated with microbial distributions." Bioinformatics 36, no. 4 (2019): 1289–90. http://dx.doi.org/10.1093/bioinformatics/btz722.

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Abstract Summary Phylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization. Availability and implementation phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize. Supplementary information Supplementary data are available at Bioinformatics online.

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Journal articles on the topic 'Microbial bioinformatics'

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