scholarly journals HiTaC: Hierarchical Taxonomic Classification of Fungal ITS Sequences

2020 ◽  
Author(s):  
Fábio M. Miranda ◽  
Vasco A. C. Azevedo ◽  
Bernhard Y. Renard ◽  
Vitor C. Piro ◽  
Rommel T. J. Ramos
Keyword(s):  
Human Microbiome ◽  
Taxonomic Classification ◽  
Supplementary Information ◽  
Symbiotic Associations ◽  
Machine Learning Model ◽  
Causative Agents ◽  
Health And Disease ◽  
Human Infections ◽  
Taxonomic Tree ◽  
Fungal Its

AbstractMotivationFungi are key elements in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human microbiome and can be causative agents of human infections. An accurate and robust method for fungal ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight of the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them consider the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.ResultsIn this work, we developed a robust, hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and is able to handle imbalanced datasets. We show that our hierarchical model, HiTaC, outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher accuracy and sensitivity across different taxonomic ranks.AvailabilityHiTaC is an open-source software, with documentation and source code available at https://gitlab.com/dacs-hpi/[email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals Health and disease imprinted in the time variability of the human microbiome

2015 ◽  
Author(s):  
Jose Manuel Marti ◽  
Daniel M Martinez ◽  
Manuel Pena ◽  
Cesar Gracia ◽  
Amparo Latorre ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Scaling Law ◽  
Human Microbiome ◽  
Time Variability ◽  
Clinical Use ◽  
Gut Development ◽  
Research Activity ◽  
Health And Disease ◽  
Fluctuation Scaling ◽  
Universal Law

Human microbiota plays an important role in determining changes from health to disease. Increasing research activity is dedicated to understand its diversity and variability. We analyse 16S rRNA and whole genome sequencing (WGS) data from the gut microbiota of 97 individuals monitored in time. Temporal fluctuations in the microbiome reveal significant differences due to factors that affect the microbiota such as dietary changes, antibiotic intake, early gut development or disease. Here we show that a fluctuation scaling law describes the temporal variability of the system and that a noise-induced phase transition is central in the route to disease. The universal law distinguishes healthy from sick microbiota and quantitatively characterizes the path in the phase space, which opens up its potential clinical use and, more generally, other technological applications where microbiota plays an important role.

scholarly journals Preface to the human microbiome in urologic health and disease

2017 ◽  
Vol 5 ◽  
pp. 27-27 ◽  
Author(s):  
Daniel A. Shoskes ◽  
Jill A. Macoska
Keyword(s):  
Human Microbiome ◽  
Health And Disease

scholarly journals Microbial trend analysis for common dynamic trend, group comparison and classification in longitudinal microbiome study

2020 ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J. Blaser ◽  
Huilin Li
Keyword(s):  
Trend Analysis ◽  
Critical Role ◽  
Human Microbiome ◽  
Real Data ◽  
Microbial Dynamics ◽  
Individual Subject ◽  
Microbial Profiling ◽  
Health And Disease ◽  
Microbiome Data

AbstractMotivationThe human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time.ResultsWe propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects on the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different in groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice.ConclusionsThe proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.

The Genetics and Genomics of Asthma

2018 ◽  
Vol 19 (1) ◽  
pp. 223-246 ◽  
Author(s):  
Saffron A.G. Willis-Owen ◽  
William O.C. Cookson ◽  
Miriam F. Moffatt
Keyword(s):  
Sequence Variation ◽  
Human Microbiome ◽  
Technological Advances ◽  
Genome Wide ◽  
A Genome ◽  
Wide Range ◽  
Health And Disease ◽  
Wide Scale ◽  
Genetics And Genomics ◽  
The Individual

Asthma is a common, clinically heterogeneous disease with strong evidence of heritability. Progress in defining the genetic underpinnings of asthma, however, has been slow and hampered by issues of inconsistency. Recent advances in the tools available for analysis—assaying transcription, sequence variation, and epigenetic marks on a genome-wide scale—have substantially altered this landscape. Applications of such approaches are consistent with heterogeneity at the level of causation and specify patterns of commonality with a wide range of alternative disease traits. Looking beyond the individual as the unit of study, advances in technology have also fostered comprehensive analysis of the human microbiome and its varied roles in health and disease. In this article, we consider the implications of these technological advances for our current understanding of the genetics and genomics of asthma.

scholarly journals DiscoRhythm: an easy-to-use web application and R package for discovering rhythmicity

2019 ◽  
Author(s):  
Matthew Carlucci ◽  
Algimantas Kriščiūnas ◽  
Haohan Li ◽  
Povilas Gibas ◽  
Karolis Koncevičius ◽  
...  
Keyword(s):  
Web Application ◽  
Statistical Significance ◽  
R Package ◽  
Biological Data ◽  
Supplementary Information ◽  
Statistical Knowledge ◽  
Health And Disease ◽  
Phase Amplitude ◽  
Almost All ◽  
User Friendly

Abstract Motivation Biological rhythmicity is fundamental to almost all organisms on Earth and plays a key role in health and disease. Identification of oscillating signals could lead to novel biological insights, yet its investigation is impeded by the extensive computational and statistical knowledge required to perform such analysis. Results To address this issue, we present DiscoRhythm (Discovering Rhythmicity), a user-friendly application for characterizing rhythmicity in temporal biological data. DiscoRhythm is available as a web application or an R/Bioconductor package for estimating phase, amplitude, and statistical significance using four popular approaches to rhythm detection (Cosinor, JTK Cycle, ARSER, and Lomb-Scargle). We optimized these algorithms for speed, improving their execution times up to 30-fold to enable rapid analysis of -omic-scale datasets in real-time. Informative visualizations, interactive modules for quality control, dimensionality reduction, periodicity profiling, and incorporation of experimental replicates make DiscoRhythm a thorough toolkit for analyzing rhythmicity. Availability and Implementation The DiscoRhythm R package is available on Bioconductor (https://bioconductor.org/packages/DiscoRhythm), with source code available on GitHub (https://github.com/matthewcarlucci/DiscoRhythm) under a GPL-3 license. The web application is securely deployed over HTTPS (https://disco.camh.ca) and is freely available for use worldwide. Local instances of the DiscoRhythm web application can be created using the R package or by deploying the publicly available Docker container (https://hub.docker.com/r/mcarlucci/discorhythm). Supplementary information Supplementary data are available at Bioinformatics online.

PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA–lncRNA interaction prediction

2020 ◽  
Vol 36 (10) ◽  
pp. 2986-2992 ◽  
Author(s):  
Qiang Kang ◽  
Jun Meng ◽  
Jun Cui ◽  
Yushi Luan ◽  
Ming Chen
Keyword(s):  
Biological Activities ◽  
Learning Model ◽  
Supplementary Information ◽  
Fuzzy Decision ◽  
Interaction Prediction ◽  
Machine Learning Model ◽  
Plant Mirna ◽  
Mechanism Of Interaction ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Motivation The studies have indicated that not only microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) play important roles in biological activities, but also their interactions affect the biological process. A growing number of studies focus on the miRNA–lncRNA interactions, while few of them are proposed for plant. The prediction of interactions is significant for understanding the mechanism of interaction between miRNA and lncRNA in plant. Results This article proposes a new method for fulfilling plant miRNA–lncRNA interaction prediction (PmliPred). The deep learning model and shallow machine learning model are trained using raw sequence and manually extracted features, respectively. Then they are hybridized based on fuzzy decision for prediction. PmliPred shows better performance and generalization ability compared with the existing methods. Several new miRNA–lncRNA interactions in Solanum lycopersicum are successfully identified using quantitative real time–polymerase chain reaction from the candidates predicted by PmliPred, which further verifies its effectiveness. Availability and implementation The source code of PmliPred is freely available at http://bis.zju.edu.cn/PmliPred/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Revealing the Bacterial Butyrate Synthesis Pathways by Analyzing (Meta)genomic Data

mBio ◽  
2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Marius Vital ◽  
Adina Chuang Howe ◽  
James M. Tiedje
Keyword(s):  
Gut Microbiota ◽  
Critical Role ◽  
Human Microbiome ◽  
Genomic Analysis ◽  
16S Rrna Gene Sequencing ◽  
Emerging Diseases ◽  
Rrna Gene ◽  
Data Set ◽  
Genome Database ◽  
Health And Disease

ABSTRACTButyrate-producing bacteria have recently gained attention, since they are important for a healthy colon and when altered contribute to emerging diseases, such as ulcerative colitis and type II diabetes. This guild is polyphyletic and cannot be accurately detected by 16S rRNA gene sequencing. Consequently, approaches targeting the terminal genes of the main butyrate-producing pathway have been developed. However, since additional pathways exist and alternative, newly recognized enzymes catalyzing the terminal reaction have been described, previous investigations are often incomplete. We undertook a broad analysis of butyrate-producing pathways and individual genes by screening 3,184 sequenced bacterial genomes from the Integrated Microbial Genome database. Genomes of 225 bacteria with a potential to produce butyrate were identified, including many previously unknown candidates. The majority of candidates belong to distinct families within theFirmicutes, but members of nine other phyla, especially fromActinobacteria,Bacteroidetes,Fusobacteria,Proteobacteria,Spirochaetes, andThermotogae, were also identified as potential butyrate producers. The established gene catalogue (3,055 entries) was used to screen for butyrate synthesis pathways in 15 metagenomes derived from stool samples of healthy individuals provided by the HMP (Human Microbiome Project) consortium. A high percentage of total genomes exhibited a butyrate-producing pathway (mean, 19.1%; range, 3.2% to 39.4%), where the acetyl-coenzyme A (CoA) pathway was the most prevalent (mean, 79.7% of all pathways), followed by the lysine pathway (mean, 11.2%). Diversity analysis for the acetyl-CoA pathway showed that the same few firmicute groups associated with severalLachnospiraceaeandRuminococcaceaewere dominating in most individuals, whereas the other pathways were associated primarily withBacteroidetes.IMPORTANCEMicrobiome research has revealed new, important roles of our gut microbiota for maintaining health, but an understanding of effects of specific microbial functions on the host is in its infancy, partly because in-depth functional microbial analyses are rare and publicly available databases are often incomplete/misannotated. In this study, we focused on production of butyrate, the main energy source for colonocytes, which plays a critical role in health and disease. We have provided a complete database of genes from major known butyrate-producing pathways, using in-depth genomic analysis of publicly available genomes, filling an important gap to accurately assess the butyrate-producing potential of complex microbial communities from “-omics”-derived data. Furthermore, a reference data set containing the abundance and diversity of butyrate synthesis pathways from the healthy gut microbiota was established through a metagenomics-based assessment. This study will help in understanding the role of butyrate producers in health and disease and may assist the development of treatments for functional dysbiosis.

Human Microbiome: Implications on Health and Disease

2017 ◽  
pp. 153-168 ◽  
Author(s):  
Princy Hira ◽  
Utkarsh Sood ◽  
Vipin Gupta ◽  
Namita Nayyar ◽  
Nitish Kumar Mahato ◽  
...  
Keyword(s):  
Human Microbiome ◽  
Health And Disease

scholarly journals Breast Cancer and Its Relationship with the Microbiota

2018 ◽  
Vol 15 (8) ◽  
pp. 1747 ◽  
Author(s):  
Mariana Fernández ◽  
Iris Reina-Pérez ◽  
Juan Astorga ◽  
Andrea Rodríguez-Carrillo ◽  
Julio Plaza-Díaz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dynamic Balance ◽  
Human Microbiome ◽  
Serum Levels ◽  
Endogenous Hormones ◽  
Human Beings ◽  
Microbial Composition ◽  
Metabolizing Enzymes ◽  
Health And Disease ◽  
Bacterial Genes

The microorganisms that live symbiotically in human beings are increasingly recognized as important players in health and disease. The largest collection of these microorganisms is found in the gastrointestinal tract. Microbial composition reflects both genetic and lifestyle variables of the host. This microbiota is in a dynamic balance with the host, exerting local and distant effects. Microbial perturbation (dysbiosis) could contribute to the risk of developing health problems. Various bacterial genes capable of producing estrogen-metabolizing enzymes have been identified. Accordingly, gut microbiota is capable of modulating estrogen serum levels. Conversely, estrogen-like compounds may promote the proliferation of certain species of bacteria. Therefore, a crosstalk between microbiota and both endogenous hormones and estrogen-like compounds might synergize to provide protection from disease but also to increase the risk of developing hormone-related diseases. Recent research suggests that the microbiota of women with breast cancer differs from that of healthy women, indicating that certain bacteria may be associated with cancer development and with different responses to therapy. In this review, we discuss recent knowledge about the microbiome and breast cancer, identifying specific characteristics of the human microbiome that may serve to develop novel approaches for risk assessment, prevention and treatment for this disease.

scholarly journals IsoformSwitchAnalyzeR: analysis of changes in genome-wide patterns of alternative splicing and its functional consequences

2019 ◽  
Vol 35 (21) ◽  
pp. 4469-4471 ◽  
Author(s):  
Kristoffer Vitting-Seerup ◽  
Albin Sandelin
Keyword(s):  
Alternative Splicing ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Rna Seq ◽  
Genome Wide ◽  
Functional Consequences ◽  
Cancer Genome Atlas ◽  
Health And Disease ◽  
Splicing Patterns

Abstract Summary Alternative splicing is an important mechanism involved in health and disease. Recent work highlights the importance of investigating genome-wide changes in splicing patterns and the subsequent functional consequences. Current computational methods only support such analysis on a gene-by-gene basis. Therefore, we extended IsoformSwitchAnalyzeR R library to enable analysis of genome-wide changes in specific types of alternative splicing and predicted functional consequences of the resulting isoform switches. As a case study, we analyzed RNA-seq data from The Cancer Genome Atlas and found systematic changes in alternative splicing and the consequences of the associated isoform switches. Availability and implementation Windows, Linux and Mac OS: http://bioconductor.org/packages/IsoformSwitchAnalyzeR. Supplementary information Supplementary data are available at Bioinformatics online.

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