scholarly journals Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kazutoshi Yoshitake ◽  
Gaku Kimura ◽  
Tomoko Sakami ◽  
Tsuyoshi Watanabe ◽  
Yukiko Taniuchi ◽  
...  
Keyword(s):  
Time Series ◽  
Microbial Communities ◽  
Pacific Coast ◽  
Three Dimensional ◽  
Amplicon Sequencing ◽  
Metagenomic Data ◽  
Shotgun Metagenomics ◽  
Functional Features ◽  
Pacific Coast Of Japan ◽  
Marine Microbial Communities

AbstractAlthough numerous metagenome, amplicon sequencing-based studies have been conducted to date to characterize marine microbial communities, relatively few have employed full metagenome shotgun sequencing to obtain a broader picture of the functional features of these marine microbial communities. Moreover, most of these studies only performed sporadic sampling, which is insufficient to understand an ecosystem comprehensively. In this study, we regularly conducted seawater sampling along the northeastern Pacific coast of Japan between March 2012 and May 2016. We collected 213 seawater samples and prepared size-based fractions to generate 454 subsets of samples for shotgun metagenome sequencing and analysis. We also determined the sequences of 16S rRNA (n = 111) and 18S rRNA (n = 47) gene amplicons from smaller sample subsets. We thereafter developed the Ocean Monitoring Database for time-series metagenomic data (http://marine-meta.healthscience.sci.waseda.ac.jp/omd/), which provides a three-dimensional bird’s-eye view of the data. This database includes results of digital DNA chip analysis, a novel method for estimating ocean characteristics such as water temperature from metagenomic data. Furthermore, we developed a novel classification method that includes more information about viruses than that acquired using BLAST. We further report the discovery of a large number of previously overlooked (TAG)n repeat sequences in the genomes of marine microbes. We predict that the availability of this time-series database will lead to major discoveries in marine microbiome research.

scholarly journals Microbial Communities of Cladonia Lichens and Their Biosynthetic Gene Clusters Potentially Encoding Natural Products

2021 ◽  
Vol 9 (7) ◽  
pp. 1347
Author(s):  
Tânia Keiko Shishido ◽  
Matti Wahlsten ◽  
Pia Laine ◽  
Jouko Rikkinen ◽  
Taina Lundell ◽  
...  
Keyword(s):  
Natural Products ◽  
Microbial Communities ◽  
High Throughput Sequencing ◽  
Indigenous Communities ◽  
Gene Clusters ◽  
Amplicon Sequencing ◽  
Metagenomic Data ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Shotgun Metagenomics

Lichens have been widely used in traditional medicine, especially by indigenous communities worldwide. However, their slow growth and difficulties in the isolation of lichen symbionts and associated microbes have hindered the pharmaceutical utilisation of lichen-produced compounds. Advances in high-throughput sequencing techniques now permit detailed investigations of the complex microbial communities formed by fungi, green algae, cyanobacteria, and other bacteria within the lichen thalli. Here, we used amplicon sequencing, shotgun metagenomics, and in silico metabolomics together with compound extractions to study reindeer lichens collected from Southern Finland. Our aim was to evaluate the potential of Cladonia species as sources of novel natural products. We compared the predicted biosynthetic pathways of lichen compounds from isolated genome-sequenced lichen fungi and our environmental samples. Potential biosynthetic genes could then be further used to produce secondary metabolites in more tractable hosts. Furthermore, we detected multiple compounds by metabolite analyses, which revealed connections between the identified biosynthetic gene clusters and their products. Taken together, our results contribute to metagenomic data studies from complex lichen-symbiotic communities and provide valuable new information for use in further biochemical and pharmacological studies.

scholarly journals Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

2019 ◽  
Vol 3 ◽  
Author(s):  
Shruthi Magesh ◽  
Viktor Jonsson ◽  
Johan Bengtsson-Palme
Keyword(s):  
Microbial Communities ◽  
Point Mutations ◽  
Software Tool ◽  
Metagenomic Data ◽  
Data Sets ◽  
Resistance Mutations ◽  
Shotgun Metagenomics ◽  
Key Factor ◽  
Detection Of Mutations ◽  
And Function

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame).

scholarly journals Quantifying point-mutations in shotgun metagenomic data

2018 ◽  
Author(s):  
Shruthi Magesh ◽  
Viktor Jonsson ◽  
Johan Bengtsson-Palme
Keyword(s):  
Microbial Communities ◽  
Point Mutations ◽  
Software Tool ◽  
Metagenomic Data ◽  
Data Sets ◽  
Resistance Mutations ◽  
Shotgun Metagenomics ◽  
Key Factor ◽  
Detection Of Mutations ◽  
And Function

AbstractMetagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available from http://microbiology.se/software/mumame

Taxonomic and functional insights into rock varnish microbiome using shotgun metagenomics

2019 ◽  
Vol 95 (12) ◽  
Author(s):  
Alfonso Esposito ◽  
Luigimaria Borruso ◽  
Jayne E Rattray ◽  
Lorenzo Brusetti ◽  
Engy Ahmed
Keyword(s):  
Microbial Communities ◽  
Ecosystem Dynamics ◽  
Survival Strategies ◽  
Rock Surface ◽  
Shotgun Metagenomics ◽  
Complete Genomes ◽  
Rock Varnish ◽  
Functional Features ◽  
Microbial Habitat ◽  
Mn Oxides

ABSTRACT Rock varnish is a microbial habitat, characterised by thin (5–500 μm) and shiny coatings of iron (Fe) and manganese (Mn) oxides associated with clay minerals. This structure is well studied by geologists, and recently there have been reports about the taxonomical composition of its microbiome. In this study, we investigated the rock varnish microbiome using shotgun metagenomics together with analyses of elemental composition, lipid and small molecule biomarkers, and rock surface analyses to explore the biogeography of microbial communities and their functional features. We report taxa and encoded functions represented in metagenomes retrieved from varnish or non-varnish samples, additionally, eight nearly complete genomes have been reconstructed spanning four phyla (Acidobacteria, Actinobacteria, Chloroflexi and TM7). The functional and taxonomic analyses presented in this study provide new insights into the ecosystem dynamics and survival strategies of microbial communities inhabiting varnish and non-varnish rock surfaces.

scholarly journals mixMC: a multivariate statistical framework to gain insight into Microbial Communities

2016 ◽  
Author(s):  
Kim-Anh Le Cao ◽  
Mary-Ellen Costello ◽  
Vanessa Anne Lakis ◽  
Francois Bartolo ◽  
Xin-Yi Chua ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Biomarker Discovery ◽  
Amplicon Sequencing ◽  
Added Value ◽  
Statistical Tools ◽  
Multivariate Statistical ◽  
Shotgun Metagenomics ◽  
16S Rrna Amplicon Sequencing ◽  
Microbial Biomarkers ◽  
Culture Independent

Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.

Enhanced CO2 concentrations change the structure of Antarctic marine microbial communities

2016 ◽  
Vol 552 ◽  
pp. 93-113 ◽  
Author(s):  
AT Davidson ◽  
J McKinlay ◽  
K Westwood ◽  
PG Thomson ◽  
R van den Enden ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Co2 Concentrations ◽  
Antarctic Marine ◽  
Marine Microbial Communities

scholarly journals Quantitative image analysis of microbial communities with BiofilmQ

2021 ◽  
Author(s):  
Raimo Hartmann ◽  
Hannah Jeckel ◽  
Eric Jelli ◽  
Praveen K. Singh ◽  
Sanika Vaidya ◽  
...  
Keyword(s):  
Image Analysis ◽  
Microbial Communities ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Software Tool ◽  
Image Cytometry ◽  
Quantitative Image Analysis ◽  
Space And Time ◽  
Quantitative Image ◽  
Three Dimensional Space

AbstractBiofilms are microbial communities that represent a highly abundant form of microbial life on Earth. Inside biofilms, phenotypic and genotypic variations occur in three-dimensional space and time; microscopy and quantitative image analysis are therefore crucial for elucidating their functions. Here, we present BiofilmQ—a comprehensive image cytometry software tool for the automated and high-throughput quantification, analysis and visualization of numerous biofilm-internal and whole-biofilm properties in three-dimensional space and time.

scholarly journals Reduced metagenome sequencing for strain-resolution taxonomic profiles

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Lars Snipen ◽  
Inga-Leena Angell ◽  
Torbjørn Rognes ◽  
Knut Rudi
Keyword(s):  
Microbial Community ◽  
Microbial Community Composition ◽  
Amplicon Sequencing ◽  
Good Alternative ◽  
Shotgun Sequencing ◽  
Least Square ◽  
Full Potential ◽  
Shotgun Metagenomics ◽  
Metagenome Sequencing ◽  
Reference Genomes

Abstract Background Studies of shifts in microbial community composition has many applications. For studies at species or subspecies levels, the 16S amplicon sequencing lacks resolution and is often replaced by full shotgun sequencing. Due to higher costs, this restricts the number of samples sequenced. As an alternative to a full shotgun sequencing we have investigated the use of Reduced Metagenome Sequencing (RMS) to estimate the composition of a microbial community. This involves the use of double-digested restriction-associated DNA sequencing, which means only a smaller fraction of the genomes are sequenced. The read sets obtained by this approach have properties different from both amplicon and shotgun data, and analysis pipelines for both can either not be used at all or not explore the full potential of RMS data. Results We suggest a procedure for analyzing such data, based on fragment clustering and the use of a constrained ordinary least square de-convolution for estimating the relative abundance of all community members. Mock community datasets show the potential to clearly separate strains even when the 16S is 100% identical, and genome-wide differences is < 0.02, indicating RMS has a very high resolution. From a simulation study, we compare RMS to shotgun sequencing and show that we get improved abundance estimates when the community has many very closely related genomes. From a real dataset of infant guts, we show that RMS is capable of detecting a strain diversity gradient for Escherichia coli across time. Conclusion We find that RMS is a good alternative to either metabarcoding or shotgun sequencing when it comes to resolving microbial communities at the strain level. Like shotgun metagenomics, it requires a good database of reference genomes and is well suited for studies of the human gut or other communities where many reference genomes exist. A data analysis pipeline is offered, as an R package at https://github.com/larssnip/microRMS.

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