Relevant bibliographies by topics / Microbial genomics - Data processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Microbial genomics - Data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Microbial genomics - Data processing"

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Dieckmann, Marius Alfred, Sebastian Beyvers, Rudel Christian Nkouamedjo-Fankep, et al. "EDGAR3.0: comparative genomics and phylogenomics on a scalable infrastructure." Nucleic Acids Research 49, W1 (2021): W185—W192. http://dx.doi.org/10.1093/nar/gkab341.

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Abstract The EDGAR platform, a web server providing databases of precomputed orthology data for thousands of microbial genomes, is one of the most established tools in the field of comparative genomics and phylogenomics. Based on precomputed gene alignments, EDGAR allows quick identification of the differential gene content, i.e. the pan genome, the core genome, or singleton genes. Furthermore, EDGAR features a wide range of analyses and visualizations like Venn diagrams, synteny plots, phylogenetic trees, as well as Amino Acid Identity (AAI) and Average Nucleotide Identity (ANI) matrices. Dur
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Shen-Gunther, Jane, Qingqing Xia, Hong Cai, and Yufeng Wang. "HPV DeepSeq: An Ultra-Fast Method of NGS Data Analysis and Visualization Using Automated Workflows and a Customized Papillomavirus Database in CLC Genomics Workbench." Pathogens 10, no. 8 (2021): 1026. http://dx.doi.org/10.3390/pathogens10081026.

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Next-generation sequencing (NGS) has actualized the human papillomavirus (HPV) virome profiling for in-depth investigation of viral evolution and pathogenesis. However, viral computational analysis remains a bottleneck due to semantic discrepancies between computational tools and curated reference genomes. To address this, we developed and tested automated workflows for HPV taxonomic profiling and visualization using a customized papillomavirus database in the CLC Microbial Genomics Module. HPV genomes from Papilloma Virus Episteme were customized and incorporated into CLC “ready-to-use” workf
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Murovec, Boštjan, Leon Deutsch, and Blaz Stres. "Computational Framework for High-Quality Production and Large-Scale Evolutionary Analysis of Metagenome Assembled Genomes." Molecular Biology and Evolution 37, no. 2 (2019): 593–98. http://dx.doi.org/10.1093/molbev/msz237.

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Abstract Microbial species play important roles in different environments and the production of high-quality genomes from metagenome data sets represents a major obstacle to understanding their ecological and evolutionary dynamics. Metagenome-Assembled Genomes Orchestra (MAGO) is a computational framework that integrates and simplifies metagenome assembly, binning, bin improvement, bin quality (completeness and contamination), bin annotation, and evolutionary placement of bins via detailed maximum-likelihood phylogeny based on multiple marker genes using different amino acid substitution model
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Rau, Martin H., and Ahmad A. Zeidan. "Constraint-based modeling in microbial food biotechnology." Biochemical Society Transactions 46, no. 2 (2018): 249–60. http://dx.doi.org/10.1042/bst20170268.

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Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is
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Smolikova, Galina, Daria Gorbach, Elena Lukasheva, et al. "Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives." International Journal of Molecular Sciences 21, no. 23 (2020): 9162. http://dx.doi.org/10.3390/ijms21239162.

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For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of ana
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van Belkum, Alex, Marc Struelens, Arjan de Visser, Henri Verbrugh, and Michel Tibayrenc. "Role of Genomic Typing in Taxonomy, Evolutionary Genetics, and Microbial Epidemiology." Clinical Microbiology Reviews 14, no. 3 (2001): 547–60. http://dx.doi.org/10.1128/cmr.14.3.547-560.2001.

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SUMMARY Currently, genetic typing of microorganisms is widely used in several major fields of microbiological research. Taxonomy, research aimed at elucidation of evolutionary dynamics or phylogenetic relationships, population genetics of microorganisms, and microbial epidemiology all rely on genetic typing data for discrimination between genotypes. Apart from being an essential component of these fundamental sciences, microbial typing clearly affects several areas of applied microbiogical research. The epidemiological investigation of outbreaks of infectious diseases and the measurement of ge
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Gangadoo, Sheeana, Piumie Rajapaksha Pathirannahalage, Samuel Cheeseman, et al. "The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling." International Journal of Molecular Sciences 22, no. 4 (2021): 1965. http://dx.doi.org/10.3390/ijms22041965.

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Gastrointestinal (GIT) diseases have risen globally in recent years, and early detection of the host’s gut microbiota, typically through fecal material, has become a crucial component for rapid diagnosis of such diseases. Human fecal material is a complex substance composed of undigested macromolecules and particles, and the processing of such matter is a challenge due to the unstable nature of its products and the complexity of the matrix. The identification of these products can be used as an indication for present and future diseases; however, many researchers focus on one variable or marke
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Shah, Carisa M., Rohan Bareja, and Olivier Elemento. "Pathogen identification in prostate cancer biopsies using transcriptome sequencing." Journal of Clinical Oncology 35, no. 15_suppl (2017): e16545-e16545. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e16545.

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e16545 Background: Tumor biopsies may frequently be associated with microbial species due to proximity with microbial communities or due to contamination during tissue processing. We examined sequencing data from tumor biopsies to explore the tumor-associated microbiome in prostate cancer. Methods: Patients were enrolled in a prospective Precision Medicine study to evaluate genomic alterations based on freshly obtained tissue biopsies. Total RNA was prepared for RNA sequencing using the standard Illumina mRNA sample preparation protocol. Paired-end RNA-sequencing at read lengths of 50 or 51 bp
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Magnitov, Mikhail D., Veronika S. Kuznetsova, Sergey V. Ulianov, Sergey V. Razin, and Alexander V. Tyakht. "Benchmark of software tools for prokaryotic chromosomal interaction domain identification." Bioinformatics 36, no. 17 (2020): 4560–67. http://dx.doi.org/10.1093/bioinformatics/btaa555.

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Abstract Motivation The application of genome-wide chromosome conformation capture (3C) methods to prokaryotes provided insights into the spatial organization of their genomes and identified patterns conserved across the tree of life, such as chromatin compartments and contact domains. Prokaryotic genomes vary in GC content and the density of restriction sites along the chromosome, suggesting that these properties should be considered when planning experiments and choosing appropriate software for data processing. Diverse algorithms are available for the analysis of eukaryotic chromatin contac
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Ilyina, L. A., V. A. Filippova, E. A. Yildirim, et al. "APPLICATION OF NGS FOR EVALUATING THE SYMBIOTIC MICROFLORA OF THE REINDEER RUMEN IN THE RUSSIAN ARCTIC." International bulletin of Veterinary Medicine 2 (2020): 127–31. http://dx.doi.org/10.17238/issn2072-2419.2020.2.127.

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The method of next generation sequencing (NGS) allows deeply analyze the composi-tion of the microorganisms of the rumen of ruminants. Sampling of the contents of the rumen was carried out in the winter-spring period during the slaughter of reindeer in the territory of the Murmansk region - in the agricultural complex “Tundra” of the Lovozero district. 12 samples of rumen con-tent were taken. Samples of rumen contents intended for molecular genetic studies were frozen immediately after selection at -20 ° C and then placed for long-term storage in a freezer. Total DNA was extracted from the sam
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Dissertations / Theses on the topic "Microbial genomics - Data processing"

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Ye, Lin, and 叶林. "Exploring microbial community structures and functions of activated sludge by high-throughput sequencing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48079649.

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To investigate the diversities and abundances of nitrifiers and to apply the highthroughput sequencing technologies to analyze the overall microbial community structures and functions in the wastewater treatment bioreactors were the major objectives of this study. Specifically, this study was conducted: (1) to investigate the diversities and abundances of AOA, AOB and NOB in bioreactors, (2) to explore the bacterial communities in bioreactors using 454 pyrosequencing, and (3) to analyze the metagenomes of activated sludge using Illumina sequencing. A lab-scale nitrification bioreactor
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Yang, Bin, and 杨彬. "A novel framework for binning environmental genomic fragments." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45789344.

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Oelofse, Andries Johannes. "Development of a MAIME-compliant microarray data management system for functional genomics data integration." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-08222007-135249.

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Ao, Sio-iong, and 區小勇. "Data mining algorithms for genomic analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38319822.

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Landfors, Mattias. "Normalization and analysis of high-dimensional genomics data." Doctoral thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-53486.

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In the middle of the 1990’s the microarray technology was introduced. The technology allowed for genome wide analysis of gene expression in one experiment. Since its introduction similar high through-put methods have been developed in other fields of molecular biology. These high through-put methods provide measurements for hundred up to millions of variables in a single experiment and a rigorous data analysis is necessary in order to answer the underlying biological questions. Further complications arise in data analysis as technological variation is introduced in the data, due to the complex
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Karanam, Suresh Kumar. "Automation of comparative genomic promoter analysis of DNA microarray datasets." Thesis, Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04062004-164658/unrestricted/karanam%5Fsuresh%5Fk%5F200312%5Fms.pdf.

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Cai, J. James, and 蔡莖. "Understanding the pathogenic fungus Penicillium marneffei: a computational genomics perspective." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36595135.

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Hankeln, Wolfgang Matthias [Verfasser]. "Data integration in microbial genomics Contextualizing sequence data in aid of biological knowledge / Wolfgang Matthias Hankeln. Max Planck Institute for Marine Microbiology." Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2012. http://d-nb.info/1035209020/34.

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Zhu, Xinjie, and 朱信杰. "START : a parallel signal track analytical research tool for flexible and efficient analysis of genomic data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2015. http://hdl.handle.net/10722/211136.

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Signal Track Analytical Research Tool (START), is a parallel system for analyzing large-scale genomic data. Currently, genomic data analyses are usually performed by using custom scripts developed by individual research groups, and/or by the integrated use of multiple existing tools (such as BEDTools and Galaxy). The goals of START are 1) to provide a single tool that supports a wide spectrum of genomic data analyses that are commonly done by analysts; and 2) to greatly simplify these analysis tasks by means of a simple declarative language (STQL) with which users only need to specify what the
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Driscoll, Timothy. "Host-Microbe Relations: A Phylogenomics-Driven Bioinformatic Approach to the Characterization of Microbial DNA from Heterogeneous Sequence Data." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/50921.

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Plants and animals are characterized by intimate, enduring, often indispensable, and always complex associations with microbes. Therefore, it should come as no surprise that when the genome of a eukaryote is sequenced, a medley of bacterial sequences are produced as well. These sequences can be highly informative about the interactions between the eukaryote and its bacterial cohorts; unfortunately, they often comprise a vanishingly small constituent within a heterogeneous mixture of microbial and host sequences. Genomic analyses typically avoid the bacterial sequences in order to obtain a geno
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Books on the topic "Microbial genomics - Data processing"

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Metagenomics: Current innovations and future trends. Caister Academic Press, 2011.

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Metagenomics: Theory, methods and applications. Caister Academic, 2010.

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Ussery, David W. Computing for comparative microbial genomics: Bioinformatics for microbiologists. Springer, 2009.

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Ussery, David W. Computing for comparative microbial genomics: Bioinformatics for microbiologists. Springer, 2009.

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Ao, Sio-Iong. Data mining and applications in genomics. Springer, 2008.

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Data mining and applications in genomics. Springer, 2008.

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Dzuida, Darius M. Data mining for genomics and proteomics: Analysis of gene and protein expression data. Wiley, 2010.

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Data mining for genomics and proteomics: Analysis of gene and protein expression data. Wiley, 2010.

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Horvath, Steve. Weighted Network Analysis: Applications in Genomics and Systems Biology. Springer Science+Business Media, LLC, 2011.

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Foundations of comparative genomics. Elsevier Academic Press, 2006.

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Book chapters on the topic "Microbial genomics - Data processing"

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Xu, Ying. "Microarray Gene Expression Data Analysis." In Microbial Functional Genomics. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471647527.ch7.

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Emran, Nurul A., Suzanne Embury, Paolo Missier, Mohd Noor Mat Isa, and Azah Kamilah Muda. "Measuring Data Completeness for Microbial Genomics Database." In Intelligent Information and Database Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36546-1_20.

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Pedersen, Edvard, Nils Peder Willassen, and Lars Ailo Bongo. "Transparent Incremental Updates for Genomics Data Analysis Pipelines." In Euro-Par 2013: Parallel Processing Workshops. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54420-0_31.

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Ruan, Yan-chun, and Ning-hai Liu. "Application of Data Processing Analysis in Microbial Identification of Fermented Food." In 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1726-3_29.

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Becker, Matthias, Umesh Worlikar, Shobhit Agrawal, et al. "Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50743-5_17.

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Chen, Huaming, Jiangning Song, Jun Shen, and Lei Wang. "Big Data in Genomics." In Big Data Management and Processing. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315154008-18.

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Sajantila, Antti, and Bruce Budowle. "Microbial Forensics." In Silent Witness. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190909444.003.0007.

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Forensic DNA analysis has been used predominantly for comparison, either directly or indirectly, of crime scene evidence and known reference samples from human suspects in a variety of situations, such as analyzing a biospecimen(s) from a crime scene, identifying unidentified cadavers (or other human remains) in a postmortem setting, or kinship testing. The field of forensic genetics has recently expanded from its original focus on human samples to more holistic methods of characterization of the source(s) of biological samples. This progression has been motivated in part by technological advancements, from targeted PCR-based methods to higher throughput DNA sequencing methods, with concomitant bioinformatics to support the increased data output. One of the new areas in forensic genetics facilitating the expansion of forensic genomics is the field of microbial forensics. Microbial forensics involves bioterrorism, biocrime, human identification, determining postmortem interval, human geolocation, and body fluid identification.
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Goyal, Deepika, Shiv Swaroop, and Janmejay Pandey. "Harnessing the Genetic Diversity and Metabolic Potential of Extremophilic Microorganisms through the Integration of Metagenomics and Single-Cell Genomics." In Extremophilic Microbes and Metabolites - Diversity, Bioprespecting and Biotechnological Applications [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.82639.

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Microorganisms thriving under extreme environments have proven to be an invaluable resource for metabolic products and processes. While studies carried out on microbial characterization of extremophilic environments during golden era of microbiology adapted a ‘reductionist approach’ and focused on isolation, purification and characterization of individual microbial isolates; the recent studies have implemented a holistic approach using both culture-dependent and culture-independent approaches for characterization of total microbial diversity of the extreme environments. Findings from these studies have unmistakably indicated that microbial diversity within extreme environments is much higher than anticipated. Consequently, unraveling the taxonomic and metabolic characteristics of microbial diversity in extreme environments has emerged as an imposing challenge in the field of microbiology and microbial biotechnology. To a great extent, this challenge has been addressed with inception and advancement of next-generation sequencing and computing methods for NGS data analyses. However, further it has been realized that in order to maximize the exploitation of genetic and metabolic diversity of extremophilic microbial diversity, the metagenomic approaches must be combined synergistically with single-cell genomics. A synergistic approach is expected to provide comprehensions into the biology of extremophilic microorganism, including their metabolic potential, molecular mechanisms of adaptations, unique genomic features including codon reassignments etc.
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Desai, Prerak. "Gene Expression in Microbial Systems for Growth and Metabolism." In Handbook of Research on Systems Biology Applications in Medicine. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-076-9.ch016.

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The use of systems biology to study complex biological questions is gaining ground due to the ever-increasing amount of genetic tools and genome sequences available. As such, systems biology concepts and approaches are increasingly underpinning our concept of microbial physiology. Three tools for use in functional genomics are gene expression, proteomics, and metabolomics. However, these tools produce such large data sets that we sometimes become paralyzed trying to merge the data and link it to form a consistent biological interpretation. Use of functional groupings has relieved some of the issues in merging data for biological meaning. Statistical analysis and visualization of these multi-dimension data sets are needed to aid the microbiologist, which brings additional methods that are often not familiar. Progress is being made to bring these diverse data types together to understand fundamental metabolic processes and pathways. These efforts are paying tremendous dividends in our understanding of how microbes live, grow, survive, and metabolize nutrients. These insights allow metabolic engineering to progress and allow scientists to further define the mechanisms of metabolism.
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Segall, Richard S., and Jeffrey S. Cook. "Overview of Big-Data-Intensive Storage and Its Technologies." In Advances in Data Mining and Database Management. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3142-5.ch002.

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This chapter deals with a detailed discussion on the storage systems for data-intensive computing using Big Data. The chapter begins with a brief introduction about data-intensive computing and types of parallel processing approaches. It also highlights the points that display how data-intensive computing systems differ from other forms of computing. A discussion on the importance of Big Data computing is put forth. The current and future challenges of storage in genomics are discussed in detail. Also, storage and data management strategies are given. The chapter's focus is then on the software challenges for storage. Storage use cases are provided like DataDirect Networks, SDSC, etc. The list of storage tools and their details are provided. A small section discusses the sensor data storage system. Then a table is provided that shows the top 10 cloud storage systems for data-intensive computing using Big Data in the world. Top 500 Big Data storage servers statistics are also displayed effectively by the images from Top500 website.
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Conference papers on the topic "Microbial genomics - Data processing"

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Zeng, Erliang, Wei Zhang, Scott Emrich, Dan Liu, Josh Livermore, and Stuart Jones. "A computational framework for integrative analysis of large microbial genomics data." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359837.

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Agapito, Giuseppe, Pietro Hiram Guzzi, and Mario Cannataro. "Parallel processing of genomics data." In NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965364.

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Becker, Matthias, Hartmut Schultze, Thomas Ulas, et al. "Accelerated Genomics Data Processing using Memory-Driven Computing." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983296.

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Marshall, S., and Le Yu. "Modeling gene expression data using probabalistic Boolean networks (PSNS)." In IET Seminar on Signal Processing for Genomics. IEE, 2006. http://dx.doi.org/10.1049/ic:20060368.

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Ahmad, Tanveer, Nauman Ahmed, Johan Peltenburg, and Zaid Al-Ars. "ArrowSAM: In-Memory Genomics Data Processing Using Apache Arrow." In 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS). IEEE, 2020. http://dx.doi.org/10.1109/iccais48893.2020.9096725.

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Zhesi He, R. Ruddle, and L. Caves. "Strategies and tools for multivariate biology: interactive analysis of high dimensional postgenomic data." In IET Seminar on Signal Processing for Genomics. IEE, 2006. http://dx.doi.org/10.1049/ic:20060375.

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Nabavi, Sheida, and Andrew H. Beck. "Earth mover's distance for differential analysis of heterogeneous genomics data." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418340.

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Femminella, Mauro, Gianluca Reali, Dario Valocchi, and Emilia Nunzi. "The ARES Project: Network Architecture for Delivering and Processing Genomics Data." In 2014 IEEE 3rd Symposium on Network Cloud Computing and Applications (NCCA). IEEE, 2014. http://dx.doi.org/10.1109/ncca.2014.12.

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Jennings, Elizabeth M., Jeffrey S. Morris, Raymond J. Carroll, Ganiraju C. Manyam, and Veerabhadran Baladandayuthapani. "Hierarchical Bayesian methods for integration of various types of genomics data." In 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2012. http://dx.doi.org/10.1109/gensips.2012.6507713.

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Olivares, Rolando J., Arvind Rao, Ganesh Rao, Jeffrey S. Morris, and Veerabhadran Baladandayuthapani. "Integrative analysis of multi-modal correlated imaging-genomics data in glioblastoma." In 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2013. http://dx.doi.org/10.1109/gensips.2013.6735914.

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