scholarly journals Understanding the phyllosphere microbiome assemblage in grape species (Vitaceae) with amplicon sequence data structures

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Prashant Singh ◽  
Sylvain Santoni ◽  
Audrey Weber ◽  
Patrice This ◽  
Jean-Pierre Péros
Keyword(s):  
Sequence Data ◽  
Data Sets ◽  
Statistical Estimates ◽  
Β Diversity ◽  
Normalization Methods ◽  
Microbiome Research ◽  
Bacteria And Fungi ◽  
Microbiome Data ◽  
The Impact

Abstract Impacts of plant genotype on microbial assemblage in the phyllosphere (above-ground parts of plants, which predominantly consists of the set of photosynthetic leaves) of Vitis vinifera cultivars have been studied previously but the impact of grape species (under the grape family Vitaceae) was never investigated. Considering the fact, that the phyllosphere microbiome may have profound effects on host plant health and its performance traits, studying the impact of grape species in microbial taxa structuring in the phyllosphere could be of crucial importance. We performed 16S and ITS profiling (for bacteria and fungi respectively) to access genus level characterization of the microflora present in the leaf phyllosphere of five species within this plant family, sampled in two successive years from the repository situated in the Mediterranean. We also performed α and β-diversity analyses with robust statistical estimates to test the impacts of grape species and growing year, over a two-year period. Our results indicated the presence of complex microbial diversity and assemblages in the phyllosphere with a significant effect of both factors (grape species and growing year), the latter effect is being more pronounced. We also compared separate normalization methods for high-throughput microbiome data-sets followed by differential taxa abundance analyses. The results suggested the predominance of a particular normalization method over others. This also indicated the need for more robust normalization methods to study the differential taxa abundance among groups in microbiome research.

scholarly journals Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Verónica Lloréns-Rico ◽  
Sara Vieira-Silva ◽  
Pedro J. Gonçalves ◽  
Gwen Falony ◽  
Jeroen Raes
Keyword(s):  
Microbial Communities ◽  
Quantitative Methods ◽  
Microbial Load ◽  
Metagenomic Sequencing ◽  
Sampling Depth ◽  
Microbiome Research ◽  
Microbiome Data ◽  
The Impact ◽  
Analytical Approaches ◽  
Quantitative Approaches

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

Care to Share?

2015 ◽  
pp. 222-244
Author(s):  
Paolo Santi ◽  
Carlo Ratti
Keyword(s):  
Urban Environment ◽  
Methodological Approach ◽  
Relevant Literature ◽  
Taxi Driver ◽  
Data Sets ◽  
Exciting Field ◽  
Ride Sharing ◽  
Gps Devices ◽  
The Impact

GPS technology has been extensively used to optimize operation of taxi systems since the first appearance of commercial GPS devices. Owing to this, data sets generated by taxi fleets are amongst the first and most representative examples of massive GPS data that have been systematically collected. The analysis of these data sets has recently generated a rich literature aimed at, among other things, identifying optimal taxi driver strategies, predicting taxi demand or location of vacant taxis, etc. This chapter focuses on what is a new, exciting field of investigation of GPS taxi data analysis, namely, evaluating the impact of a shared taxi system on the urban environment. After introducing the notion of (taxi) ride sharing, the chapter presents the relevant literature, describing in greater details a methodological approach called “shareability network” that allows formal characterization of taxi sharing opportunities in an urban environment.

scholarly journals Comparison of Normalization Methods for Construction of Large, Multiplex Amplicon Pools for Next-Generation Sequencing

2010 ◽  
Vol 76 (12) ◽  
pp. 3863-3868 ◽  
Author(s):  
J. Kirk Harris ◽  
Jason W. Sahl ◽  
Todd A. Castoe ◽  
Brandie D. Wagner ◽  
David D. Pollock ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Massively Parallel Sequencing ◽  
Sequence Data ◽  
Cost Savings ◽  
Massively Parallel ◽  
Next Generation ◽  
Normalization Methods ◽  
The Cost ◽  
Generation Sequencing

ABSTRACT Constructing mixtures of tagged or bar-coded DNAs for sequencing is an important requirement for the efficient use of next-generation sequencers in applications where limited sequence data are required per sample. There are many applications in which next-generation sequencing can be used effectively to sequence large mixed samples; an example is the characterization of microbial communities where ≤1,000 sequences per samples are adequate to address research questions. Thus, it is possible to examine hundreds to thousands of samples per run on massively parallel next-generation sequencers. However, the cost savings for efficient utilization of sequence capacity is realized only if the production and management costs associated with construction of multiplex pools are also scalable. One critical step in multiplex pool construction is the normalization process, whereby equimolar amounts of each amplicon are mixed. Here we compare three approaches (spectroscopy, size-restricted spectroscopy, and quantitative binding) for normalization of large, multiplex amplicon pools for performance and efficiency. We found that the quantitative binding approach was superior and represents an efficient scalable process for construction of very large, multiplex pools with hundreds and perhaps thousands of individual amplicons included. We demonstrate the increased sequence diversity identified with higher throughput. Massively parallel sequencing can dramatically accelerate microbial ecology studies by allowing appropriate replication of sequence acquisition to account for temporal and spatial variations. Further, population studies to examine genetic variation, which require even lower levels of sequencing, should be possible where thousands of individual bar-coded amplicons are examined in parallel.

Geochemical characterization of carbonates using RLS-like Raman data and Raman-LIBS technique combination: applications for Mars exploration

2020 ◽  
Author(s):  
Jose Antonio Manrique-Martinez ◽  
Marco Veneranda ◽  
Guillermo Lopez-Reyes ◽  
Aurelio Sanz-Arranz ◽  
Jesus Saiz ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Continuous Wave ◽  
Direct Detection ◽  
Landing Site ◽  
Mode Analysis ◽  
Data Sets ◽  
Time Resolved ◽  
Analytical Technique ◽  
The Impact

<p>Raman Spectroscopy is an analytical technique that will be deployed on Mars in the following years and could be part of other payloads for planetary exploration missions in the future. Its ability for identification of mineral phases and its interest in Mars has been deeply discussed in bibliography [1]. Perseverance rover, to be launched in 2020, and ExoMars rover, to be launched in 2022, will carry three Raman instruments, different in concept and capabilities. SHERLOC (mounted on Perseverance’s arm) is a UV Raman instrument mainly focused in the direct detection of biomarkers, SuperCam (mounted on Perseverance’s mast) is a standoff, multi-technique, instrument that performs Raman and LIBS at distances of several meters from the rover. Finally, RLS, mounted in Rosalind Franklin Rover, in the Pasteur analytical laboratory, is a continuous wave, 532 nm excitation source Raman instrument. While the first one is focused in detection limits of organics, RLS is intended to investigate mineralogy and possible biomarkers, while SuperCam, due to its standoff and time resolved design, is a different concept to que other two Raman instruments, as it is also capable of fusing data from different techniques.</p> <p> </p> <p>Carbonates are minerals of great interest for astrobiology, and, as suggested by CRISM data, the landing site selected for the NASA/Mars 2020 rover mission (Jezero crater) presents a variety of Fe-Ca-Mg carbonate units [2]. For Oxia Planum, Rosalind Franklin’s landing site, although no carbonates have been detected in that area by orbiter data, Earth analogues suggest that small amounts of carbonates might be found in the clay rich area. On Earth, top bench Raman spectrometers can be effectively used to discriminate carbonates and to determine the Mg/Fe concentration ratio of mineral species from dolomite (CaMg(CO<sub>3</sub>)<sub>2</sub>) - ankerite (CaFe(CO<sub>3</sub>)<sub>2</sub>) and magnesite (MgCO<sub>3</sub>) - siderite (FeCO<sub>3</sub>) solid solutions series [3]. The previously mentioned instruments might present limitations derived from the design constrains of space exploration. Resolution, far from ideal, and low intensity of the signal, are two of the main factors that could affect the possible calculations done with data from the three Raman instruments. SuperCam is a special case, as it is able to obtain data from several techniques from the same spot of the sample, and that might help to overcome those difficulties.</p> <p> </p> <p>In this work a complete set of Ca-Mg-Fe carbonates is analysed by different Raman instruments, including automated contact instruments and combined standoff developments. The initial characterization of the samples is done with XRD, as gold standard. Then, a characterization of all those carbonates based only on Raman data sets was done, aiming to evaluate the impact of resolution in the classification power of Raman-based calculations. A detailed vibrational mode analysis was carried out for interpreting the structural modifications induced by cationic substitution. Here, after a detailed interpretation it was found that Raman active internal modes are less sensitive to the carbonate chemistry than the external modes (i.e. the 155cm-1 and 286cm-1 respectively).</p> <p> </p> <p>Same collection of carbonates is analysed using standoff Raman-LIBS combination. In this case we will evaluate how having the complementary information of the elemental composition improves the results obtained by standoff Raman spectroscopy [4], as LIBS is more sensitive to the possible changes in the cations in the samples. Using these data sets, a combination of univariate and multivariate calculations are done to evaluate their classification capacity. As commented before, LIBS can classify better these minerals thanks to its lower detection limit and a better functionality in standoff configuration. However, the effect from other phases, different from carbonates, might disturb the LIBS calculations, reason why having an assessment of all the phases in play by Raman spectroscopy is of great interest, supporting the idea of the power of technique combination.</p> <p>1    F. Rull, S. Maurice, I. Hutchinson et al. Astrobiology, Vol. 17 (2017), No. 6-7</p> <p>2    B.H.N. Horgan, R.B. Anderson, G. Dromart, E.S. Amador, M.S. Rice Icarus, <strong>339 </strong>(2020) 113526.</p> <p>3    P. Kristova, L. Hopkinson, K. Rutt, H. Hunter, G. Cressey, American Mineralogist, <strong>98</strong> (2013) 401-409.</p> <p>4    J.A. Manrique-Martinez et al. Journal of Raman Spectroscopy (2020) 1-16.</p>

scholarly journals A catalog of tens of thousands of viruses from human metagenomes reveals hidden associations with chronic diseases

2021 ◽  
Vol 118 (23) ◽  
pp. e2023202118
Author(s):  
Michael J. Tisza ◽  
Christopher B. Buck
Keyword(s):  
Human Health ◽  
Chronic Diseases ◽  
Sequence Data ◽  
Virus Disease ◽  
Case Control ◽  
Viral Sequence ◽  
Case Control Studies ◽  
Disease Associations ◽  
Microbiome Research ◽  
The Impact

Despite remarkable strides in microbiome research, the viral component of the microbiome has generally presented a more challenging target than the bacteriome. This gap persists, even though many thousands of shotgun sequencing runs from human metagenomic samples exist in public databases, and all of them encompass large amounts of viral sequence data. The lack of a comprehensive database for human-associated viruses has historically stymied efforts to interrogate the impact of the virome on human health. This study probes thousands of datasets to uncover sequences from over 45,000 unique virus taxa, with historically high per-genome completeness. Large publicly available case-control studies are reanalyzed, and over 2,200 strong virus–disease associations are found.

Ultrasonic characterization of anisotropic rocks: Impact of transducers’ size and geometry on data interpretation

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. C99-C105
Author(s):  
Yevhen Kovalyshen ◽  
Joel Sarout ◽  
Jeremie Dautriat ◽  
Bruce Maney ◽  
Maxim Lebedev
Keyword(s):  
Finite Size ◽  
Data Interpretation ◽  
Point Sources ◽  
Data Sets ◽  
Ultrasonic Characterization ◽  
Multiple Data ◽  
Anisotropic Rocks ◽  
Multiple Data Sets ◽  
The Impact

Typical ultrasonic laboratory measurements of rock physical properties are conducted with ultrasonic transducers that are relatively large compared to the rock sample. We have determined the impact of the transducer size on the resulting velocity estimations. To improve data interpretation, we explore two complementary avenues: (1) explicitly account for the finite size of the transducers as part of the data interpretation/correction workflow, rather than assuming point sources, and (2) reducing the effective size of the transducers at the hardware design level. Both approaches as well as their combination have been tested successfully on multiple data sets including artificial homogeneous and isotropic samples as well as natural anisotropic rocks such as shales.

scholarly journals The Impact of Normalization Methods on RNA-Seq Data Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
J. Zyprych-Walczak ◽  
A. Szabelska ◽  
L. Handschuh ◽  
K. Górczak ◽  
K. Klamecka ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Data Sets ◽  
Complex Data ◽  
Rna Seq ◽  
Medical Problems ◽  
Data Set ◽  
Normalization Methods ◽  
Wide Range ◽  
The Impact ◽  
Selection Of

High-throughput sequencing technologies, such as the Illumina Hi-seq, are powerful new tools for investigating a wide range of biological and medical problems. Massive and complex data sets produced by the sequencers create a need for development of statistical and computational methods that can tackle the analysis and management of data. The data normalization is one of the most crucial steps of data processing and this process must be carefully considered as it has a profound effect on the results of the analysis. In this work, we focus on a comprehensive comparison of five normalization methods related to sequencing depth, widely used for transcriptome sequencing (RNA-seq) data, and their impact on the results of gene expression analysis. Based on this study, we suggest a universal workflow that can be applied for the selection of the optimal normalization procedure for any particular data set. The described workflow includes calculation of the bias and variance values for the control genes, sensitivity and specificity of the methods, and classification errors as well as generation of the diagnostic plots. Combining the above information facilitates the selection of the most appropriate normalization method for the studied data sets and determines which methods can be used interchangeably.

scholarly journals PERFect: PERmutation Filtering test for microbiome data

Biostatistics ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 615-631 ◽  
Author(s):  
Ekaterina Smirnova ◽  
Snehalata Huzurbazar ◽  
Farhad Jafari
Keyword(s):  
Permutation Test ◽  
Real Data ◽  
Data Sets ◽  
Therapeutic Approaches ◽  
Experiment Data ◽  
Microbiome Research ◽  
Inflammatory Bowel ◽  
Microbiome Data ◽  
Processing Steps

Summary The human microbiota composition is associated with a number of diseases including obesity, inflammatory bowel disease, and bacterial vaginosis. Thus, microbiome research has the potential to reshape clinical and therapeutic approaches. However, raw microbiome count data require careful pre-processing steps that take into account both the sparsity of counts and the large number of taxa that are being measured. Filtering is defined as removing taxa that are present in a small number of samples and have small counts in the samples where they are observed. Despite progress in the number and quality of filtering approaches, there is no consensus on filtering standards and quality assessment. This can adversely affect downstream analyses and reproducibility of results across platforms and software. We introduce PERFect, a novel permutation filtering approach designed to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering. Methods are assessed on three “mock experiment” data sets, where the true taxa compositions are known, and are applied to two publicly available real microbiome data sets. The method correctly removes contaminant taxa in “mock” data sets, quantifies and visualizes the corresponding filtering loss, providing a uniform data-driven filtering criteria for real microbiome data sets. In real data analyses PERFect tends to remove more taxa than existing approaches; this likely happens because the method is based on an explicit loss function, uses statistically principled testing, and takes into account correlation between taxa. The PERFect software is freely available at https://github.com/katiasmirn/PERFect.

scholarly journals Properties of Normalization Methods Used in the Construction of Aggregate Measures

2012 ◽  
Vol 12 (2) ◽  
pp. 31-45
Author(s):  
Kesra Nermend
Keyword(s):  
Simulated Data ◽  
Data Sets ◽  
Normalization Methods ◽  
The Impact ◽  
Simulated Data Sets

Abstract This article presents a study on the impact of missing objects, untypical objects and the impact of displacing coordinates of the object on the results of normalization in the construction of aggregate measures for ranking socio-economic objects. The study was conducted on simulated data sets generated in order to investigate the properties of normalization methods. This article focuses on the standardization formula responsible for moving the set of objects

scholarly journals Roles of membrane transporters: connecting the dots from sequence to phenotype

2019 ◽  
Vol 124 (2) ◽  
pp. 201-208 ◽  
Author(s):  
Rakesh David ◽  
Caitlin S Byrt ◽  
Stephen D Tyerman ◽  
Matthew Gilliham ◽  
Stefanie Wege
Keyword(s):  
Amino Acid ◽  
Amino Acid Residue ◽  
Membrane Transporters ◽  
Plant Performance ◽  
Single Amino Acid ◽  
Physiological Data ◽  
Data Sets ◽  
Single Amino Acid Residue ◽  
The Impact

Abstract Background Plant membrane transporters are involved in diverse cellular processes underpinning plant physiology, such as nutrient acquisition, hormone movement, resource allocation, exclusion or sequestration of various solutes from cells and tissues, and environmental and developmental signalling. A comprehensive characterization of transporter function is therefore key to understanding and improving plant performance. Scope and Conclusions In this review, we focus on the complexities involved in characterizing transporter function and the impact that this has on current genomic annotations. Specific examples are provided that demonstrate why sequence homology alone cannot be relied upon to annotate and classify transporter function, and to show how even single amino acid residue variations can influence transporter activity and specificity. Misleading nomenclature of transporters is often a source of confusion in transporter characterization, especially for people new to or outside the field. Here, to aid researchers dealing with interpretation of large data sets that include transporter proteins, we provide examples of transporters that have been assigned names that misrepresent their cellular functions. Finally, we discuss the challenges in connecting transporter function at the molecular level with physiological data, and propose a solution through the creation of new databases. Further fundamental in-depth research on specific transport (and other) proteins is still required; without it, significant deficiencies in large-scale data sets and systems biology approaches will persist. Reliable characterization of transporter function requires integration of data at multiple levels, from amino acid residue sequence annotation to more in-depth biochemical, structural and physiological studies.

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