scholarly journals Balances: a new perspective for microbiome analysis

2017 ◽  
Author(s):  
J. Rivera-Pinto ◽  
J.J. Egozcue ◽  
V. Pawlowsky–Glahn ◽  
R. Paredes ◽  
M. Noguera-Julian ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Microbiome Composition ◽  
Microbiome Analysis ◽  
Microbial Species ◽  
Sequencing Technologies ◽  
Alternative Approach ◽  
Microbiome Research ◽  
New Perspective ◽  
And Function ◽  
Microbiome Data

ABSTRACTHigh-throughput sequencing technologies have revolutionized microbiome research by allowing the relative quantification of microbiome composition and function in different environments. One of the main goals in microbiome analysis is the identification of microbial species that are differentially abundant among groups of samples, or whose abundance is associated with a variable of interest. Most available methods for microbiome abundance testing perform univariate tests for each microbial species or taxa separately, ignoring the compositional nature of microbiome data.We propose an alternative approach for microbiome abundance testing that consists on the identification of two groups of taxa whose relative abundance, or balance, is associated with the response variable of interest. This approach is appealing, since it has direct translation to the biological concept of ecological balance between species in an ecosystem. In this work, we present selbal, a greedy stepwise algorithm for balance selection. We illustrate the algorithm with 16s abundance data from an HIV-microbiome study and a Crohn-microbiome study.ImportanceA more meaningful approach for microbiome abundance testing is presented. Instead of testing each taxon separately we propose to explore abundance balances among groups of taxa. This approach acknowledges the compositional nature of microbiome data.

scholarly journals Balances: a New Perspective for Microbiome Analysis

mSystems ◽  
2018 ◽  
Vol 3 (4) ◽  
Author(s):  
J. Rivera-Pinto ◽  
J. J. Egozcue ◽  
V. Pawlowsky-Glahn ◽  
R. Paredes ◽  
M. Noguera-Julian ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Compositional Data ◽  
Real Space ◽  
Compositional Data Analysis ◽  
Geometric Means ◽  
Microbiome Composition ◽  
Sequencing Technologies ◽  
Microbiome Research ◽  
New Perspective ◽  
And Function

ABSTRACTHigh-throughput sequencing technologies have revolutionized microbiome research by allowing the relative quantification of microbiome composition and function in different environments. In this work we focus on the identification of microbial signatures, groups of microbial taxa that are predictive of a phenotype of interest. We do this by acknowledging the compositional nature of the microbiome and the fact that it carries relative information. Thus, instead of defining a microbial signature as a linear combination in real space corresponding to the abundances of a group of taxa, we consider microbial signatures given by the geometric means of data from two groups of taxa whose relative abundances, or balance, are associated with the response variable of interest. In this work we presentselbal, a greedy stepwise algorithm for selection of balances or microbial signatures that preserves the principles of compositional data analysis. We illustrate the algorithm with 16S rRNA abundance data from a Crohn’s microbiome study and an HIV microbiome study.IMPORTANCEWe propose a new algorithm for the identification of microbial signatures. These microbial signatures can be used for diagnosis, prognosis, or prediction of therapeutic response based on an individual’s specific microbiota.

scholarly journals Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale

2022 ◽  
Vol 1 ◽  
Author(s):  
Bin Hu ◽  
Shane Canon ◽  
Emiley A. Eloe-Fadrosh ◽  
Anubhav ◽  
Michal Babinski ◽  
...  
Keyword(s):  
Open Access ◽  
Omics Data ◽  
Metabolomic Data ◽  
Or Groups ◽  
Microbiome Research ◽  
Descriptive Approach ◽  
Dynamics And Function ◽  
And Function ◽  
Microbiome Data ◽  
Bioinformatics Workflows

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

scholarly journals Assessment of Bioleaching Microbial Community Structure and Function Based on Next-Generation Sequencing Technologies

Minerals ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 596 ◽  
Author(s):  
Shuang Zhou ◽  
Min Gan ◽  
Jianyu Zhu ◽  
Xinxing Liu ◽  
Guanzhou Qiu
Keyword(s):  
Community Structure ◽  
Next Generation Sequencing ◽  
Microbial Ecology ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Structure And Function ◽  
Next Generation ◽  
Sequencing Technologies ◽  
And Function ◽  
Generation Sequencing

It is widely known that bioleaching microorganisms have to cope with the complex extreme environment in which microbial ecology relating to community structure and function varies across environmental types. However, analyses of microbial ecology of bioleaching bacteria is still a challenge. To address this challenge, numerous technologies have been developed. In recent years, high-throughput sequencing technologies enabling comprehensive sequencing analysis of cellular RNA and DNA within the reach of most laboratories have been added to the toolbox of microbial ecology. The next-generation sequencing technology allowing processing DNA sequences can produce available draft genomic sequences of more bioleaching bacteria, which provides the opportunity to predict models of genetic and metabolic potential of bioleaching bacteria and ultimately deepens our understanding of bioleaching microorganism. High-throughput sequencing that focuses on targeted phylogenetic marker 16S rRNA has been effectively applied to characterize the community diversity in an ore leaching environment. RNA-seq, another application of high-throughput sequencing to profile RNA, can be for both mapping and quantifying transcriptome and has demonstrated a high efficiency in quantifying the changing expression level of each transcript under different conditions. It has been demonstrated as a powerful tool for dissecting the relationship between genotype and phenotype, leading to interpreting functional elements of the genome and revealing molecular mechanisms of adaption. This review aims to describe the high-throughput sequencing approach for bioleaching environmental microorganisms, particularly focusing on its application associated with challenges.

scholarly journals Deep embeddings to comprehend and visualize microbiome protein space

2021 ◽  
Author(s):  
Krzysztof Odrzywolek ◽  
Zuzanna Karwowska ◽  
Jan Majta ◽  
Aleksander Byrski ◽  
Kaja Milanowska-Zabel ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Language Model ◽  
Structure And Function ◽  
Metagenomic Data ◽  
Use Case ◽  
Sequencing Technologies ◽  
Alignment Free ◽  
And Function ◽  
Clinical Potential ◽  
Protein Space

Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that our model (ArdiMiPE) manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies such as ArdiMiPE that contextualize metagenomic data are a promising direction to deeply understand the microbiome.

Predicting Protein Phosphorylation Sites Based on Deep Learning

2020 ◽  
Vol 15 (4) ◽  
pp. 300-308
Author(s):  
Haixia Long ◽  
Zhao Sun ◽  
Manzhi Li ◽  
Hai Yan Fu ◽  
Ming Cai Lin
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Amino Acid ◽  
Protein Phosphorylation ◽  
High Throughput Sequencing ◽  
Short Term Memory ◽  
Basic Research ◽  
Phosphorylation Sites ◽  
Sequencing Technologies ◽  
And Function

Background: Protein phosphorylation is one of the most important Post-translational Modifications (PTMs) occurring at amino acid residues serine (S), threonine (T), and tyrosine (Y). It plays critical roles in protein structure and function predicting. With the development of novel high-throughput sequencing technologies, there are a huge amount of protein sequences being generated and stored in databases. Objective: It is of great importance in both basic research and drug development to quickly and accurately predict which residues of S, T, or Y can be phosphorylated. Methods: In order to solve the problem, a novel hybrid deep learning model with a convolutional neural network and bi-directional long short-term memory recurrent neural network (CNN+BLSTM) is proposed for predicting phosphorylation sites in proteins. The model contains a list of layers that transform the input data into an output class, in which the convolution layer captures higher-level abstraction features of amino acid, while the recurrent layer captures long-term dependencies between amino acids to improve predictions. The joint model learns interactions between higher-level features derived from the protein sequence to predict the phosphorylated sites. Results: We applied our model together with two canonical methods namely iPhos-PseEn and MusiteDeep. A 5-fold cross-validation process indicated that CNN+BLSTM outperforms the two competitors in various evaluation metrics like the area under the receiver operating characteristic and precision-recall curves, the Matthews correlation coefficient, F-measure, accuracy, and so on. Conclusion: CNN+BLSTM is promising in identifying potential protein phosphorylation for further experimental validation.

scholarly journals The role of the gut microbiome in sustainable teleost aquaculture

2020 ◽  
Vol 287 (1926) ◽  
pp. 20200184 ◽  
Author(s):  
William Bernard Perry ◽  
Elle Lindsay ◽  
Christopher James Payne ◽  
Christopher Brodie ◽  
Raminta Kazlauskaite
Keyword(s):  
Artificial Selection ◽  
Gut Microbiome ◽  
High Throughput Sequencing ◽  
Closed Loop Systems ◽  
Sequencing Technologies ◽  
Road Map ◽  
Recirculating Aquaculture ◽  
Recirculating Aquaculture Systems ◽  
Microbiome Research ◽  
Scientific Attention

As the most diverse vertebrate group and a major component of a growing global aquaculture industry, teleosts continue to attract significant scientific attention. The growth in global aquaculture, driven by declines in wild stocks, has provided additional empirical demand, and thus opportunities, to explore teleost diversity. Among key developments is the recent growth in microbiome exploration, facilitated by advances in high-throughput sequencing technologies. Here, we consider studies on teleost gut microbiomes in the context of sustainable aquaculture, which we have discussed in four themes: diet, immunity, artificial selection and closed-loop systems. We demonstrate the influence aquaculture has had on gut microbiome research, while also providing a road map for the main deterministic forces that influence the gut microbiome, with topical applications to aquaculture. Functional significance is considered within an aquaculture context with reference to impacts on nutrition and immunity. Finally, we identify key knowledge gaps, both methodological and conceptual, and propose promising applications of gut microbiome manipulation to aquaculture, and future priorities in microbiome research. These include insect-based feeds, vaccination, mechanism of pro- and prebiotics, artificial selection on the hologenome, in-water bacteriophages in recirculating aquaculture systems (RAS), physiochemical properties of water and dysbiosis as a biomarker.

scholarly journals Beyond MicroRNAs: Emerging Role of Other Non-Coding RNAs in HPV-Driven Cancers

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1246 ◽  
Author(s):  
Mariateresa Casarotto ◽  
Giuseppe Fanetti ◽  
Roberto Guerrieri ◽  
Elisa Palazzari ◽  
Valentina Lupato ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Hpv Infection ◽  
Response To Therapy ◽  
Sequencing Technologies ◽  
Associated Lesions ◽  
Non Coding Rna ◽  
Micro Rnas ◽  
Hpv Oncoproteins ◽  
And Function

Persistent infection with high-risk Human Papilloma Virus (HPV) leads to the development of several tumors, including cervical, oropharyngeal, and anogenital squamous cell carcinoma. In the last years, the use of high-throughput sequencing technologies has revealed a number of non-coding RNA (ncRNAs), distinct from micro RNAs (miRNAs), that are deregulated in HPV-driven cancers, thus suggesting that HPV infection may affect their expression. However, since the knowledge of ncRNAs is still limited, a better understanding of ncRNAs biology, biogenesis, and function may be challenging for improving the diagnosis of HPV infection or progression, and for monitoring the response to therapy of patients affected by HPV-driven tumors. In addition, to establish a ncRNAs expression profile may be instrumental for developing more effective therapeutic strategies for the treatment of HPV-associated lesions and cancers. Therefore, this review will address novel classes of ncRNAs that have recently started to draw increasing attention in HPV-driven tumors, with a particular focus on ncRNAs that have been identified as a direct target of HPV oncoproteins.

scholarly journals EzMAP: Easy Microbiome Analysis Platform

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gnanendra Shanmugam ◽  
Song Hee Lee ◽  
Junhyun Jeon
Keyword(s):  
User Interface ◽  
Easy Access ◽  
Core Microbiome ◽  
Linear Discriminant ◽  
Alpha And Beta Diversity ◽  
Microbiome Analysis ◽  
Sequencing Technologies ◽  
R Programming ◽  
Microbiome Data ◽  
Analysis Platform

Abstract Background The rapid advances in next-generation sequencing technologies have revolutionized the microbiome research by greatly increasing our ability to understand diversity of microbes in a given sample. Over the past decade, several computational pipelines have been developed to efficiently process and annotate these microbiome data. However, most of these pipelines require an implementation of additional tools for downstream analyses as well as advanced programming skills. Results Here we introduce a user-friendly microbiome analysis platform, EzMAP (Easy Microbiome Analysis Platform), which was developed using Java Swings, Java Script and R programming language. EzMAP is a standalone package providing graphical user interface, enabling easy access to all the functionalities of QIIME2 (Quantitative Insights Into Microbial Ecology) as well as streamlined downstream analyses using QIIME2 output as input. This platform is designed to give users the detailed reports and the intermediate output files that are generated progressively. The users are allowed to download the features/OTU table (.biom;.tsv;.xls), representative sequences (.fasta) and phylogenetic tree (.nwk), taxonomy assignment file (optional). For downstream analyses, users are allowed to perform relative abundances (at all taxonomical levels), community comparison (alpha and beta diversity, core microbiome), differential abundances (DESeq2 and linear discriminant analysis) and functional prediction (PICRust, Tax4Fun and FunGuilds). Our case study using a published rice microbiome dataset demonstrates intuitive user interface and great accessibility of the EzMAP. Conclusions This EzMAP allows users to consolidate the microbiome analysis processes from raw sequence processing to downstream analyses specific for individual projects. We believe that this will be an invaluable tool for the beginners in their microbiome data analysis. This platform is freely available at https://github.com/gnanibioinfo/EzMAP and will be continually updated for adoption of changes in methods and approaches.

scholarly journals Comparative Microbiome Analysis of Three Species of Laboratory-Reared Periplaneta Cockroaches

2020 ◽  
Vol 58 (5) ◽  
pp. 537-542
Author(s):  
Seogwon Lee ◽  
Ju Yeong Kim ◽  
Myung-hee Yi ◽  
In-Yong Lee ◽  
Won-Ja Lee ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Periplaneta Americana ◽  
High Throughput Sequencing ◽  
Diversity Analysis ◽  
Male Adult ◽  
Bacterial Composition ◽  
Microbiome Composition ◽  
Operational Taxonomic Units ◽  
Microbiome Analysis ◽  
Multiple Conditions

Cockroaches inhabit various habitats, which will influence their microbiome. Although the microbiome can be influenced by the diet and environmental factors, it can also differ between species. Therefore, we conducted 16S rDNAtargeted high-throughput sequencing to evaluate the overall bacterial composition of the microbiomes of 3 cockroach species, Periplaneta americana, P. japonica, and P. fuliginosa, raised in laboratory for several generations under the same conditions. The experiments were conducted using male adult cockroaches. The number of operational taxonomic units (OTUs) was not significantly different among the 3 species. With regard to the Shannon and Pielou indexes, higher microbiome values were noted in P. americana than in P. japonica and P. fuliginosa. Microbiome composition was also evaluated, with endosymbionts accounting for over half of all OTUs in P. japonica and P. fuliginosa. Beta diversity analysis further showed that P. japonica and P. fuliginosa had similar microbiome composition, which differed from that of P. americana. However, we also identified that P. japonica and P. fuliginosa host distinct OTUs. Thus, although microbiome compositions may vary based on multiple conditions, it is possible to identify distinct microbiome compositions among different Periplaneta cockroach species, even when the individuals are reared under the same conditions.

scholarly journals Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259973
Author(s):  
Maryia Khomich ◽  
Ingrid Måge ◽  
Ida Rud ◽  
Ingunn Berget
Keyword(s):  
Statistical Methods ◽  
High Throughput Sequencing ◽  
Dietary Intervention ◽  
Statistical Significance ◽  
Simulated Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Microbiome Composition ◽  
Intervention Trials ◽  
And Function

The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking and identification of differentially abundant operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. The generic multivariate ANOVA tools have the flexibility needed for analysing multifactorial experiments and provide outputs at both the community and OTU levels; good performance in the simulation studies suggests that these statistical tools are also suitable for microbiome data sets.

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