scholarly journals Measuring and Mitigating PCR Bias in Microbiome Data

2019 ◽  
Author(s):  
Justin D. Silverman ◽  
Rachael J. Bloom ◽  
Sharon Jiang ◽  
Heather K. Durand ◽  
Sayan Mukherjee ◽  
...  
Keyword(s):  
Microbial Communities ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Linear Models ◽  
Pcr Amplification ◽  
Computational Techniques ◽  
Common Source ◽  
Sequencing Data ◽  
Pcr Bias ◽  
Microbiome Data

AbstractPCR amplification plays a central role in the measurement of mixed microbial communities via high-throughput sequencing. Yet PCR is also known to be a common source of bias in microbiome data. Here we present a paired modeling and experimental approach to characterize and mitigate PCR bias in microbiome studies. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR bias under real-world conditions. Our results suggest that PCR can bias estimates of microbial relative abundances by a factor of 2-4 but that this bias can be mitigated using simple Bayesian multinomial logistic-normal linear models.Author summaryHigh-throughput sequencing is often used to profile host-associated microbial communities. Many processing steps are required to transform a community of bacteria into a pool of DNA suitable for sequencing. One important step is amplification where, to create enough DNA for sequencing, DNA from many different bacteria are repeatedly copied using a technique called Polymerase Chain Reaction (PCR). However, PCR is known to introduce bias as DNA from some bacteria are more efficiently copied than others. Here we introduce an experimental procedure that allows this bias to be measured and computational techniques that allow this bias to be mitigated in sequencing data.

scholarly journals System Biology Modeling with Compositional Microbiome Data Reveals Personalized Gut Microbial Dynamics and Keystone Species

2018 ◽  
Author(s):  
Chenhao Li ◽  
Lisa Tucker-Kellogg ◽  
Niranjan Nagarajan
Keyword(s):  
Microbial Communities ◽  
High Throughput Sequencing ◽  
Community Dynamics ◽  
Keystone Species ◽  
Microbial Interactions ◽  
Natural Environments ◽  
Sequencing Data ◽  
Predator Prey ◽  
Public Datasets ◽  
Microbiome Data

AbstractA growing body of literature points to the important roles that different microbial communities play in diverse natural environments and the human body. The dynamics of these communities is driven by a range of microbial interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood, making it hard to predict the response of the community to different perturbations. With the increasing availability of high-throughput sequencing based community composition data, it is now conceivable to directly learn models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is however affected by several experimental limitations, particularly the compositional nature of sequencing data. We present a new computational approach (BEEM) that addresses this key limitation in the inference of generalised Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference in an expectation maximization like algorithm (BEEM). Surprisingly, BEEM outperforms state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEM’s application to previously inaccessible public datasets (due to the lack of biomass data) allowed us for the first time to analyse microbial communities in the human gut on a per individual basis, revealing personalised dynamics and keystone species.

scholarly journals DNA extraction and sequencing of the Mawangdui ancient cadaver protected by formalin

2020 ◽  
Author(s):  
Hua-Lin Huang ◽  
Shikui Yin ◽  
Huifang Zhao ◽  
Chao Tian ◽  
Jufang Huang ◽  
...  
Keyword(s):  
Dna Extraction ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Pcr Amplification ◽  
Magnetic Bead ◽  
Sequencing Data ◽  
Sequencing Library ◽  
Bead Method ◽  
Formalin Fixed ◽  
Better Than

AbstractMawangdui ancient Cadaver is the first wet corpse found in the world, which is famous for being immortal for over two thousands of years. After being unearthed, the female corpse was immersed in the formalin protective solution for more than 40 years. We used magnetic bead method and formalin fixed paraffing (FFPE) method to extract the DNA of the female corpse, respectively. PCR amplification, sanger sequencing, library building, high throughput sequencing (testing) and data processing were carried out on the DNA samples, and about 0.5% of the whole genome coverage sequencing data was obtained. Comparing the results of DNA trough two extraction and sequencing methods. We found that the FFPE and high throughput sequencing methods is better than others for DNA extraction of the ancient samples which were preserved in formalin, providing a guidance for dealing with formalin preserved ancient samples in the future.

scholarly journals Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data

2016 ◽  
Vol 62 (8) ◽  
pp. 692-703 ◽  
Author(s):  
Gregory B. Gloor ◽  
Gregor Reid
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Compositional Data ◽  
Critical Role ◽  
Compositional Data Analysis ◽  
Data Sets ◽  
Clear Understanding ◽  
Sequencing Data ◽  
Microbiome Data

A workshop held at the 2015 annual meeting of the Canadian Society of Microbiologists highlighted compositional data analysis methods and the importance of exploratory data analysis for the analysis of microbiome data sets generated by high-throughput DNA sequencing. A summary of the content of that workshop, a review of new methods of analysis, and information on the importance of careful analyses are presented herein. The workshop focussed on explaining the rationale behind the use of compositional data analysis, and a demonstration of these methods for the examination of 2 microbiome data sets. A clear understanding of bioinformatics methodologies and the type of data being analyzed is essential, given the growing number of studies uncovering the critical role of the microbiome in health and disease and the need to understand alterations to its composition and function following intervention with fecal transplant, probiotics, diet, and pharmaceutical agents.

scholarly journals NetCoMi: network construction and comparison for microbiome data in R

2020 ◽  
Author(s):  
Stefanie Peschel ◽  
Christian L Müller ◽  
Erika von Mutius ◽  
Anne-Laure Boulesteix ◽  
Martin Depner
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Network Construction ◽  
Microbial Association ◽  
High Throughput Sequencing Data ◽  
Microbial Associations ◽  
Microbiome Data

Abstract Motivation Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data. Results Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi’s wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children’s rooms between samples from two study centers (Ulm and Munich). Availability R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi. Contact Tel:+49 89 3187 43258; [email protected] Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

DETECTION OF ALFACORONAVIRUSES, BETACORONAVIRUSES AND ASTROVIRUSES IN BAT FECAL SAMPLES FROM MOSCOW REGION

2020 ◽  
Author(s):  
E.V. Korneenko ◽  
◽  
А.E. Samoilov ◽  
I.V. Artyushin ◽  
M.V. Safonova ◽  
...  
Keyword(s):  
High Throughput ◽  
Viral Genome ◽  
High Throughput Sequencing ◽  
Pcr Amplification ◽  
Moscow Region ◽  
Fecal Samples ◽  
Genus Level ◽  
Zoonotic Viruses ◽  
Reservoir Hosts ◽  
Blastn Search

In our study we analyzed viral RNA in bat fecal samples from Moscow region (Zvenigorod district) collected in 2015. To detect various virus families and genera in bat fecal samples we used PCR amplification of viral genome fragments, followed by high-throughput sequencing. Blastn search of unassembled reads revealed the presence of viruses from families Astroviridae, Coronaviridae and Herpesviridae. Assembly using SPAdes 3.14 yields contigs of length 460–530 b.p. which correspond to genome fragments of Coronaviridae and Astroviridae. The taxonomy of coronaviruses has been determined to the genus level. We also showed that one bat can be a reservoir of several virus genuses. Thus, the bats in the Moscow region were confirmed as reservoir hosts for potentially zoonotic viruses.

scholarly journals BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution

2014 ◽  
Vol 30 (9) ◽  
pp. 1214-1219 ◽  
Author(s):  
C. Ye ◽  
C. Hsiao ◽  
H. Corrada Bravo
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Blind Deconvolution ◽  
Sequencing Data ◽  
Base Calling ◽  
High Throughput Sequencing Data

scholarly journals Extremely Halophilic Biohydrogen Producing Microbial Communities from High-Salinity Soil and Salt Evaporation Pond

Fuels ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 241-252
Author(s):  
Dyah Asri Handayani Taroepratjeka ◽  
Tsuyoshi Imai ◽  
Prapaipid Chairattanamanokorn ◽  
Alissara Reungsang
Keyword(s):  
Microbial Communities ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
High Salinity ◽  
Amplicon Sequencing ◽  
Spatial Proximity ◽  
Lignocellulosic Waste ◽  
Evaporation Pond ◽  
Operational Taxonomic Units ◽  
Determining Factor

Extreme halophiles offer the advantage to save on the costs of sterilization and water for biohydrogen production from lignocellulosic waste after the pretreatment process with their ability to withstand extreme salt concentrations. This study identifies the dominant hydrogen-producing genera and species among the acclimatized, extremely halotolerant microbial communities taken from two salt-damaged soil locations in Khon Kaen and one location from the salt evaporation pond in Samut Sakhon, Thailand. The microbial communities’ V3–V4 regions of 16srRNA were analyzed using high-throughput amplicon sequencing. A total of 345 operational taxonomic units were obtained and the high-throughput sequencing confirmed that Firmicutes was the dominant phyla of the three communities. Halanaerobium fermentans and Halanaerobacter lacunarum were the dominant hydrogen-producing species of the communities. Spatial proximity was not found to be a determining factor for similarities between these extremely halophilic microbial communities. Through the study of the microbial communities, strategies can be developed to increase biohydrogen molar yield.

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