Metagenomics Using Next-Generation Sequencing

Bragg, Lauren M.; Tyson, Gene W.

doi:10.1007/978-1-62703-712-9_15

Cited by 86 publications

(44 citation statements)

References 113 publications

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“…Curiously, the DGGE-detected phylotypes J (EB2 sample) and A (EB1 sample) could not be detected by pyrosequencing (Figure 5) despite the same pool of DNA having been used for both techniques (Figure 2). Like other PCR-based approaches (von Wintzingerode et al, 1997; Speksnijder et al, 2001), the two culture-independent molecular techniques employed in this study are prone to bias, artifacts, pitfalls, and have limitations, whose discussion and explanation falls beyond the scope of this study (for details on these issues see Mühling et al, 2008; Green et al, 2010; Berry et al, 2011; Scholz et al, 2012; Bragg and Tyson, 2014). It is possible that by removing singletons and small sequences from the pyrosequencing data (Table 2), we may have missed sequences phylogenetically close to the unique DGGE-derived sequences.…”

Section: Discussionmentioning

confidence: 98%

Cyanobacterial Diversity in Microbial Mats from the Hypersaline Lagoon System of Araruama, Brazil: An In-depth Polyphasic Study

et al. 2017

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Microbial mats are complex, micro-scale ecosystems that can be found in a wide range of environments. In the top layer of photosynthetic mats from hypersaline environments, a large diversity of cyanobacteria typically predominates. With the aim of strengthening the knowledge on the cyanobacterial diversity present in the coastal lagoon system of Araruama (state of Rio de Janeiro, Brazil), we have characterized three mat samples by means of a polyphasic approach. We have used morphological and molecular data obtained by culture-dependent and -independent methods. Moreover, we have compared different classification methodologies and discussed the outcomes, challenges, and pitfalls of these methods. Overall, we show that Araruama's lagoons harbor a high cyanobacterial diversity. Thirty-six unique morphospecies could be differentiated, which increases by more than 15% the number of morphospecies and genera already reported for the entire Araruama system. Morphology-based data were compared with the 16S rRNA gene phylogeny derived from isolate sequences and environmental sequences obtained by PCR-DGGE and pyrosequencing. Most of the 48 phylotypes could be associated with the observed morphospecies at the order level. More than one third of the sequences demonstrated to be closely affiliated (best BLAST hit results of ≥99%) with cyanobacteria from ecologically similar habitats. Some sequences had no close relatives in the public databases, including one from an isolate, being placed as “loner” sequences within different orders. This hints at hidden cyanobacterial diversity in the mats of the Araruama system, while reinforcing the relevance of using complementary approaches to study cyanobacterial diversity.

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Section: Discussionmentioning

confidence: 98%

Cyanobacterial Diversity in Microbial Mats from the Hypersaline Lagoon System of Araruama, Brazil: An In-depth Polyphasic Study

et al. 2017

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“…It can be run in a parallel cloud computing environment to obtain extremely high performance on large data sets. Unbiased NGS approaches enable comprehensive pathogen detection in the clinical microbiology laboratory [63] and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. Sequence-based ultra rapid pathogen identification (SURPI™) [53] is a computational pipeline for pathogen identification from complex metagenomic NGS data generated.…”

Section: Open-source Toolsmentioning

confidence: 99%

Cloud Computing for Next-Generation Sequencing Data Analysis

Zhao¹,

Watrous²,

Zhang³

et al. 2017

Cloud Computing - Architecture and Applications

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High-throughput next-generation sequencing (NGS) technologies have evolved rapidly and are reshaping the scope of genomics research. The substantial decrease in the cost of NGS techniques in the past decade has led to its rapid adoption in biological research and drug development. Genomics studies of large populations are producing a huge amount of data, giving rise to computational issues around the storage, transfer, and analysis of the data. Fortunately, cloud computing has recently emerged as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses. Some cloud-based applications and resources have been developed specifically to address the computational challenges of working with very large volumes of data generated by NGS technology. In this chapter, we will review some cloud-based systems and solutions for NGS data analysis, discuss the practical hurdles and limitations in cloud computing, including data transfer and security, and share the lessons we learned from the implementation of Rainbow, a cloud-based tool for large-scale genome sequencing data analysis.

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“…However, while computationally challenging, successful implementation of this approach may provide a more accurate reflection of population dynamics at a given site. A more detailed overview of metagenomics analysis is given in Bragg and Tyson (Bragg and Tyson 2014). These data can provide much better identification at the species and, possibly, even at the strain level compared to the amplicon-based approach due to increased sequence information for each microbe.…”

Section: The Omics Evolutionmentioning

confidence: 99%

Reflection on Molecular Approaches Influencing State-of-the-Art Bioremediation Design: Culturing to Microbial Community Fingerprinting to Omics

Czaplicki

Gunsch

2016

J. Environ. Eng.

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Bioremediation is generally viewed as a cost effective and sustainable technology because it relies on microbes to transform pollutants into benign compounds. Advances in molecular biological analyses allow unprecedented microbial detection and are increasingly incorporated into bioremediation. Throughout history, state-of-the-art techniques have informed bioremediation strategies. However, the insights those techniques provided were not as in depth as those provided by recently developed omics tools. Advances in next generation sequencing (NGS) have now placed metagenomics and metatranscriptomics within reach of environmental engineers. As NGS costs decrease, metagenomics and metatranscriptomics have become increasingly feasible options to rapidly scan sites for specific degradative functions and identify microorganisms important in pollutant degradation. These omic techniques are capable of revolutionizing biological treatment in environmental engineering by allowing highly sensitive characterization of previously uncultured microorganisms. Omics enables the discovery of novel microorganisms for use in bioaugmentation and supports systematic optimization of biostimulation strategies. This review describes the omics journey from roots in biology and medicine to its current status in environmental engineering including potential future directions in commercial application.

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Metagenomics Using Next-Generation Sequencing

Cited by 86 publications

References 113 publications

Cyanobacterial Diversity in Microbial Mats from the Hypersaline Lagoon System of Araruama, Brazil: An In-depth Polyphasic Study

Cyanobacterial Diversity in Microbial Mats from the Hypersaline Lagoon System of Araruama, Brazil: An In-depth Polyphasic Study

Cloud Computing for Next-Generation Sequencing Data Analysis

Reflection on Molecular Approaches Influencing State-of-the-Art Bioremediation Design: Culturing to Microbial Community Fingerprinting to Omics

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