scholarly journals Multiomic Strategies Reveal Diversity and Important Functional Aspects of Human Gut Microbiome

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ravi Ranjan ◽  
Asha Rani ◽  
Patricia W. Finn ◽  
David L. Perkins
Keyword(s):  
Sequence Data ◽  
Bacterial Species ◽  
Abundant Species ◽  
Shotgun Sequencing ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Illumina Platform ◽  
Shotgun Metagenomics ◽  
Functional Aspects ◽  
Detectable Bias

It is well accepted that dysbiosis of microbiota is associated with disease; however, the biological mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data to complement the interpretation of metagenomics (bacterial abundance). The purpose of this study was twofold, first to evaluate the bacterial diversity and differential gene expression of gut microbiota using complementary shotgun metagenomics (MG) and metatranscriptomics (MT) from same fecal sample. Second, to compare sequence data using different Illumina platforms and with different sequencing parameters as new sequencers are introduced, and to determine if the data are comparable on different platforms. In this study, we perform ultradeep metatranscriptomic shotgun sequencing for a sample that we previously analyzed with metagenomics shotgun sequencing. We performed sequencing analysis using different Illumina platforms, with different sequencing and analysis parameters. Our results suggest that use of different Illumina platform did not lead to detectable bias in the sequencing data. The analysis of the sample using MG and MT approach shows that some species genes are highly represented in the MT than in the MG, indicating that some species are highly metabolically active. Our analysis also shows that ~52% of the genes in the metagenome are in the metatranscriptome and therefore are robustly expressed. The functions of the low and rare abundance bacterial species remain poorly understood. Our observations indicate that among the low abundant species analyzed in this study some were found to be more metabolically active compared to others, and can contribute distinct profiles of biological functions that may modulate the host-microbiota and bacteria-bacteria interactions.

scholarly journals Evaluating bacterial and functional diversity of human gut microbiota by complementary metagenomics and metatranscriptomics

2018 ◽  
Author(s):  
Ravi Ranjan ◽  
Asha Rani ◽  
Patricia W. Finn ◽  
David L. Perkins
Keyword(s):  
Gut Microbiota ◽  
Sequence Data ◽  
Bacterial Species ◽  
Abundant Species ◽  
Shotgun Sequencing ◽  
Sequencing Data ◽  
Illumina Platform ◽  
Shotgun Metagenomics ◽  
Differential Gene ◽  
Detectable Bias

ABSTRACTIt is well accepted that dysbiosis of microbiota is associated with disease; however, the biological mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data to complement the interpretation of metagenomics (bacterial abundance). The purpose of the study was twofold, first to evaluate the bacterial diversity and differential gene expression of gut microbiota using complementary shotgun metagenomics (MG) and metatranscriptomics (MT) from same fecal sample. Second, to compare sequence data using different Illumina platforms and with different sequencing parameters as new sequencers are introduced and determine if the data are comparable on different platforms. In this study, we perform ultra-deep metatranscriptomic shotgun sequencing for a sample that we previously analyzed with metagenomics shotgun sequencing. We validated the sequencing and analysis methods using different Illumina platform, and with different sequencing and analysis parameters. Our results suggest that use of different Illumina platform did not lead to detectable bias in the sequencing data. The analysis of the sample using MG and MT approach shows that some species genes are more highly represented in the MT than in the MG, indicating that some species are highly metabolically active. Our analysis also shows that ~52% of the genes in the metagenome are in the metatranscriptome, and therefore are robustly expressed. The functions of the low and rare abundance bacterial species remain poorly understood. Our observations indicate that among the low abundant species analyzed in this study some were found to be more metabolically active compared to others and can contribute distinct profiles of biological functions that may modulate the host-microbiota and bacteria-bacteria interactions.

scholarly journals Inferring species compositions of complex fungal communities from long- and short-read sequence data

2021 ◽  
Author(s):  
Yiheng Hu ◽  
Laszlo Irinyi ◽  
Minh Thuy Vi Hoang ◽  
Tavish Eenjes ◽  
Abigail Graetz ◽  
...  
Keyword(s):  
Community Composition ◽  
Pathogen Detection ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Composition Analysis ◽  
Sequencing Data ◽  
Species Classification ◽  
Shotgun Metagenomics ◽  
Query Coverage

Background: The kingdom fungi is crucial for life on earth and is highly diverse. Yet fungi are challenging to characterize. They can be difficult to culture and may be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size and are still underrepresented in whole genome sequence databases. Overall their description and analysis lags far behind other microbes such as bacteria. At the same time, classification of species via high throughput sequencing without prior purification is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. However, standardized procedures for characterizing unknown fungi from complex sequencing data have not yet been established. Results: We compared different metagenomics sequencing and analysis strategies for the identification of fungal species. Using two fungal mock communities of 44 phylogenetically diverse species, we compared species classification and community composition analysis pipelines using shotgun metagenomics and amplicon sequencing data generated from both short and long read sequencing technologies. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against a fungi-specific database. During the assessment of classification algorithms, we found that applying cut-offs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without significant data loss. Conclusion: Overall, our study expands the toolkit for identifying fungi by improving sequence-based fungal classification, and provides a practical guide for the design of metagenomics analyses.

scholarly journals Metaproteomics Analysis Reveals the Adaptation Process for the Chicken Gut Microbiota

2013 ◽  
Vol 80 (2) ◽  
pp. 478-485 ◽  
Author(s):  
Yue Tang ◽  
Anthony Underwood ◽  
Adriana Gielbert ◽  
Martin J. Woodward ◽  
Liljana Petrovska
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
High Throughput Sequencing ◽  
Bacterial Species ◽  
Abundant Species ◽  
Fecal Microbiota ◽  
Rrna Gene ◽  
Sequencing Analysis ◽  
Content Type ◽  
Chicken Gut Microbiota

ABSTRACTThe animal gastrointestinal tract houses a large microbial community, the gut microbiota, that confers many benefits to its host, such as protection from pathogens and provision of essential metabolites. Metagenomic approaches have defined the chicken fecal microbiota in other studies, but here, we wished to assess the correlation between the metagenome and the bacterial proteome in order to better understand the healthy chicken gut microbiota. Here, we performed high-throughput sequencing of 16S rRNA gene amplicons and metaproteomics analysis of fecal samples to determine microbial gut composition and protein expression. 16 rRNA gene sequencing analysis identifiedClostridiales,Bacteroidaceae, andLactobacillaceaespecies as the most abundant species in the gut. For metaproteomics analysis, peptides were generated by using the Fasp method and subsequently fractionated by strong anion exchanges. Metaproteomics analysis identified 3,673 proteins. Among the most frequently identified proteins, 380 proteins belonged toLactobacillusspp., 155 belonged toClostridiumspp., and 66 belonged toStreptococcusspp. The most frequently identified proteins were heat shock chaperones, including 349 GroEL proteins, from many bacterial species, whereas the most abundant enzymes were pyruvate kinases, as judged by the number of peptides identified per protein (spectral counting). Gene ontology and KEGG pathway analyses revealed the functions and locations of the identified proteins. The findings of both metaproteomics and 16S rRNA sequencing analyses are discussed.

scholarly journals Switching on the light: using metagenomic shotgun sequencing to characterize the intestinal microbiome of Atlantic cod

2019 ◽  
Author(s):  
Even Sannes Riiser ◽  
Thomas H.A. Haverkamp ◽  
Srinidhi Varadharajan ◽  
Ørnulf Borgan ◽  
Kjetill S. Jakobsen ◽  
...  
Keyword(s):  
Gadus Morhua ◽  
Atlantic Cod ◽  
North Atlantic Ocean ◽  
Bacterial Species ◽  
Abundant Species ◽  
Shotgun Sequencing ◽  
Intestinal Microbiome ◽  
Geographical Differences ◽  
Important Species ◽  
The North

AbstractThe biological roles of the intestinal microbiome and how it is impacted by environmental factors are yet to be determined in wild marine fish species. Atlantic cod (Gadus morhua) is an ecologically important species with a wide-spread distribution in the North Atlantic Ocean. 16S rRNA-based amplicon analyses found no geographical differentiation between the intestinal microbiome of Atlantic cod from different locations. Nevertheless, it is unclear if this lack of differentiation results from an insufficient resolution of this method to resolve fine-scaled biological complexity. Here, we take advantage of the increased resolution provided by metagenomic shotgun sequencing to investigate the intestinal microbiome of 19 adult Atlantic cod individuals from two coastal populations in Norway – located 470 km apart. Our results show that the intestinal microbiome is dominated by theVibrionalesorder, consisting of varying abundances ofPhotobacterium, AliivibrioandVibriospecies. Moreover, resolving the species community to unprecedented resolution, we identify two abundant species,P. iliopiscariumandP. kishitanii,which comprise over 50% of the classified reads. Interestingly, genomic data shows that the intestinalP. kishitaniistrains have functionally intactluxgenes, and its high abundance suggests that fish intestines form an important part of its ecological niche. These observations support a hypothesis that bioluminescence plays an ecological role in the marine food web. Despite our improved taxonomical resolution, we identify no geographical differences in bacterial community structure, indicating that the intestinal microbiome of these coastal cod is colonized by a limited number of closely related bacterial species with a broad geographical distribution that are well suited to thrive in this host-associated environment.

scholarly journals MALT: Fast alignment and analysis of metagenomic DNA sequence data applied to the Tyrolean Iceman

2016 ◽  
Author(s):  
Alexander Herbig ◽  
Frank Maixner ◽  
Kirsten I. Bos ◽  
Albert Zink ◽  
Johannes Krause ◽  
...  
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Bacterial Species ◽  
Human Microbiome ◽  
Metagenomic Analysis ◽  
Sequencing Data ◽  
Metagenomic Dna ◽  
Alignment Procedure ◽  
Taxonomic Profile ◽  
Tyrolean Iceman

AbstractModern next generation sequencing technologies produce vast amounts of data in the context of large-scale metagenomic studies, in which complex microbial communities can be reconstructed to an unprecedented level of detail. Most prominent examples are human microbiome studies that correlate the bacterial taxonomic profile with specific physiological conditions or diseases.In order to perform these analyses high-throughput computational tools are needed that are able to process these data within a short time while preserving a high level of sensitivity and specificity.Here we present MALT (MEGAN ALignment Tool) a program for the ultrafast alignment and analysis of metagenomic DNA sequencing data. MALT processes hundreds of millions of sequencing reads within only a few hours. In addition to the alignment procedure MALT implements a taxonomic binning algorithm that is able to specifically assign reads to bacterial species. Its tight integration with the interactive metagenomic analysis software MEGAN allows for visualization and further analyses of results.We demonstrate MALT by its application to the metagenomic analysis of two ancient microbiomes from oral cavity and lung samples of the 5,300-year-old Tyrolean Iceman. Despite the strong environmental background, MALT is able to pick up the weak signal of the original microbiomes and identifies multiple species that are typical representatives of the respective host environment.

scholarly journals MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data

2020 ◽  
Vol 8 (5) ◽  
pp. 684
Author(s):  
Nathanael J. Bangayan ◽  
Baochen Shi ◽  
Jerry Trinh ◽  
Emma Barnard ◽  
Gabriela Kasimatis ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Human Microbiome ◽  
Shotgun Sequencing ◽  
Strain Level ◽  
Multi Locus Sequence Typing ◽  
Metagenomic Data ◽  
Sequencing Analysis ◽  
Metagenomic Sequencing ◽  
Healthy Skin ◽  
Sequencing Data

The microbiome plays an important role in human physiology. The composition of the human microbiome has been described at the phylum, class, genus, and species levels, however, it is largely unknown at the strain level. The importance of strain-level differences in microbial communities has been increasingly recognized in understanding disease associations. Current methods for identifying strain populations often require deep metagenomic sequencing and a comprehensive set of reference genomes. In this study, we developed a method, metagenomic multi-locus sequence typing (MG-MLST), to determine strain-level composition in a microbial community by combining high-throughput sequencing with multi-locus sequence typing (MLST). We used a commensal bacterium, Propionibacterium acnes, as an example to test the ability of MG-MLST in identifying the strain composition. Using simulated communities, MG-MLST accurately predicted the strain populations in all samples. We further validated the method using MLST gene amplicon libraries and metagenomic shotgun sequencing data of clinical skin samples. MG-MLST yielded consistent results of the strain composition to those obtained from nearly full-length 16S rRNA clone libraries and metagenomic shotgun sequencing analysis. When comparing strain-level differences between acne and healthy skin microbiomes, we demonstrated that strains of RT2/6 were highly associated with healthy skin, consistent with previous findings. In summary, MG-MLST provides a quantitative analysis of the strain populations in the microbiome with diversity and richness. It can be applied to microbiome studies to reveal strain-level differences between groups, which are critical in many microorganism-related diseases.

scholarly journals Comparison of Three Commercial Tools for Metagenomic Shotgun Sequencing Analysis

2019 ◽  
Vol 58 (3) ◽  
Author(s):  
Matthew Thoendel ◽  
Patricio Jeraldo ◽  
Kerryl E. Greenwood-Quaintance ◽  
Janet Yao ◽  
Nicholas Chia ◽  
...  
Keyword(s):  
Diagnostic Method ◽  
Shotgun Sequencing ◽  
Metagenomic Analysis ◽  
Data Sets ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Data Set ◽  
Significant Challenge ◽  
Number Of Species

ABSTRACT Metagenomic shotgun sequencing for the identification of pathogens is being increasingly utilized as a diagnostic method. Interpretation of large and complicated data sets is a significant challenge, for which multiple commercial tools have been developed. Three commercial metagenomic shotgun sequencing tools, CosmosID, One Codex, and IDbyDNA, were compared to determine whether they result in similar interpretations of the same sequencing data. We selected 24 diverse samples from a previously characterized data set derived from DNA extracted from biofilms dislodged from the surfaces of resected arthroplasties (sonicate fluid). Sequencing data sets were analyzed using the three commercial tools and compared to culture results and prior metagenomic analysis interpretation. Identical interpretations from all three tools occurred for 6 samples. The total number of species identified included 28 by CosmosID, 59 by One Codex, and 41 by IDbyDNA. All of the tools performed similarly in detecting those microorganisms identified by culture, including polymicrobial mixes. These data show that while all of the tools performed well overall, there were some differences, particularly in their predilection for identifying low-abundance or contaminant organisms as present.

scholarly journals Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing

2015 ◽  
Author(s):  
Minh Duc Cao ◽  
Devika Ganesamoorthy ◽  
Alysha G. Elliott ◽  
Huihui Zhang ◽  
Matthew A. Cooper ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Antibiotic Resistance ◽  
Real Time ◽  
Sequence Data ◽  
Bacterial Species ◽  
Strain Typing ◽  
Streaming Algorithms ◽  
Sequencing Data ◽  
Desktop Computer ◽  
Antibiotic Resistance Profile

AbstractThe recently introduced Oxford Nanopore MinION platform generates DNA sequence data in real-time. This opens immense potential to shorten the sample-to-results time and is likely to lead to enormous benefits in rapid diagnosis of bacterial infection and identification of drug resistance. However, there are very few tools available for streaming analysis of real-time sequencing data. Here, we present a framework for streaming analysis of MinION real-time sequence data, together with probabilistic streaming algorithms for species typing, multi-locus strain typing, gene presence strain-typing and antibiotic resistance profile identification. Using three culture isolate samples as well as a mixed-species sample, we demonstrate that bacterial species and strain information can be obtained within 30 minutes of sequencing and using about 500 reads, initial drug-resistance profiles within two hours, and complete resistance profiles within 10 hours. Multi-locus strain typing required more than 15x coverage to generate confident assignments, whereas gene-presence typing could detect the presence of a known strain with 0.5x coverage. We also show that our pipeline can process over 100 times more data than the current throughput of the MinION on a desktop computer.

scholarly journals Metagenomic Shotgun Sequencing Analysis of Canalicular Concretions in Lacrimal Canaliculitis Cases

2021 ◽  
Vol 43 (2) ◽  
pp. 676-686
Author(s):  
Yukinobu Okajima ◽  
Takashi Suzuki ◽  
Chika Miyazaki ◽  
Satoshi Goto ◽  
Sho Ishikawa ◽  
...  
Keyword(s):  
Pathogen Detection ◽  
Diversity Index ◽  
Anaerobic Bacteria ◽  
Shotgun Sequencing ◽  
Shannon Diversity Index ◽  
Aerobic Bacteria ◽  
Clinical Samples ◽  
Sequencing Analysis ◽  
Shotgun Metagenomics ◽  
Shannon Diversity

Lacrimal canaliculitis is a rare infection of the lacrimal canaliculi with canalicular concretions formed by aggregation of organisms. Metagenomic shotgun sequencing analysis using next-generation sequencing has been used to detect pathogens directly from clinical samples. Using this technology, we report cases of successful pathogen detection of canalicular concretions in lacrimal canaliculitis cases. We investigated patients with primary lacrimal canaliculitis examined in the eye clinics of four hospitals from February 2015 to July 2017. Eighteen canalicular concretion specimens collected from 18 eyes of 17 patients were analyzed by shotgun metagenomics sequencing using the MiSeq platform (Illumina). Taxonomic classification was performed using the GenBank NT database. The canalicular concretion diversity was characterized using the Shannon diversity index. This study included 18 eyes (17 patients, 77.1 ± 6.1 years): 82.4% were women with lacrimal canaliculitis; canalicular concretions were obtained from 12 eyes using lacrimal endoscopy and six eyes using canaliculotomy with curettage. Sequencing analysis detected bacteria in all samples (Shannon diversity index, 0.05–1.47). The following genera of anaerobic bacteria (>1% abundance) were identified: Actinomyces spp. in 15 eyes, Propionibacterium spp., Parvimonas spp. in 11 eyes, Prevotella spp. in 9 eyes, Fusobacterium spp. in 6 eyes, Selenomonas spp. in 5 eyes, Aggregatibacter spp. in 3 eyes, facultative and aerobic bacteria such as Streptococcus spp. in 13 eyes, Campylobacter spp. in 6 eyes, and Haemophilus spp. in 3 eyes. The most common combinations were Actinomyces spp. and Streptococcus spp. and Parvinomonas spp. and Streptococcus spp., found in 10 cases. Pathogens were identified successfully using metagenomic shotgun sequencing analysis in patients with canalicular concretions. Canalicular concretions are polymicrobial with anaerobic and facultative, aerobic bacteria.

scholarly journals BiSulfite Bolt: A bisulfite sequencing analysis platform

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Colin Farrell ◽  
Michael Thompson ◽  
Anela Tosevska ◽  
Adewale Oyetunde ◽  
Matteo Pellegrini
Keyword(s):  
Data Aggregation ◽  
Bisulfite Sequencing ◽  
Low Complexity ◽  
Sequencing Analysis ◽  
Command Line ◽  
Sequencing Data ◽  
Bisulfite Sequencing Data ◽  
Analysis Platform ◽  
Python Package ◽  
Bisulfite Sequencing Analysis

Abstract Background Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. Findings We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. Conclusion BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

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