Microbiome Maps: Hilbert Curve Visualizations of Metagenomic Profiles

AbstractMotivationAbundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent is complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number in the thousands. We present a technique for succinct visualization of abundance profiles using a space-filling curve that transforms a profile into an interpretable 2D image.ResultsJasper is a tool for visualizing profiles from metagenomic whole-genome sequencing and 16S, and orders taxa along a space-filling Hilbert curve. The result is a “Microbiome Map”, where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order the taxa in one of two ways, and depending on the ordering, the microbiome maps can highlight “hot spots” of microbes that are either dominant in taxonomic clades or to the biological conditions under study.We use Jasper to visualize samples from the Human Microbiome Project and from a Chronic Kidney Disease study, and discuss a variety of ways in which the microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can be converted into animated movies that bring to life the dynamicity of microbiomes.AvailabilityJasper will be available as free software from the Mac App Store and biorg.cs.fiu.edu/jasperSupplementary informationSupplementary materials are available at biorg.cs.fiu.edu/jasper

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phylogenize: correcting for phylogeny reveals genes associated with microbial distributions

Bioinformatics ◽

10.1093/bioinformatics/btz722 ◽

2019 ◽

Vol 36 (4) ◽

pp. 1289-1290

Author(s):

Patrick H Bradley ◽

Katherine S Pollard

Keyword(s):

Community Composition ◽

Human Microbiome ◽

Human Microbiome Project ◽

Shotgun Sequencing ◽

Supplementary Information ◽

Phylogenetic Comparative Methods ◽

Supplementary Data ◽

Sequencing Data ◽

Phylogenetic Regression ◽

Project Data

Abstract Summary Phylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization. Availability and implementation phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize. Supplementary information Supplementary data are available at Bioinformatics online.

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The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals

International journal of electrical and computer engineering systems ◽

10.32985/ijeces.12.1.3 ◽

2021 ◽

Vol 12 (1) ◽

pp. 23-31

Author(s):

Panagiotis Tsinganos ◽

Bruno Cornelis ◽

Jan Cornelis ◽

Bart Jansen ◽

Athanassios Skodras

Keyword(s):

Computer Vision ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Network Architectures ◽

Hilbert Curve ◽

Space Filling ◽

Space Filling Curve ◽

Space Filling Curves ◽

Semg Signals

Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.

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Approximation and Analytical Studies of Inter-clustering Performances of Space-Filling Curves

Discrete Mathematics & Theoretical Computer Science ◽

10.46298/dmtcs.3338 ◽

2003 ◽

Vol DMTCS Proceedings vol. AC,... (Proceedings) ◽

Author(s):

Ho-Kwok Dai ◽

Hung-Chi Su

Keyword(s):

Random Walk ◽

Dimensional Space ◽

Hilbert Curve ◽

Space Filling ◽

Space Filling Curve ◽

Space Filling Curves ◽

Order Curve ◽

Cluster Distance ◽

The Mean ◽

Curve Family

International audience A discrete space-filling curve provides a linear traversal/indexing of a multi-dimensional grid space.This paper presents an application of random walk to the study of inter-clustering of space-filling curves and an analytical study on the inter-clustering performances of 2-dimensional Hilbert and z-order curve families.Two underlying measures are employed: the mean inter-cluster distance over all inter-cluster gaps and the mean total inter-cluster distance over all subgrids.We show how approximating the mean inter-cluster distance statistics of continuous multi-dimensional space-filling curves fits into the formalism of random walk, and derive the exact formulas for the two statistics for both curve families.The excellent agreement in the approximate and true mean inter-cluster distance statistics suggests that the random walk may furnish an effective model to develop approximations to clustering and locality statistics for space-filling curves.Based upon the analytical results, the asymptotic comparisons indicate that z-order curve family performs better than Hilbert curve family with respect to both statistics.

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Mouse Gut Microbiome-Encoded β-Glucuronidases Identified Using Metagenome Analysis Guided by Protein Structure

mSystems ◽

10.1128/msystems.00452-19 ◽

2019 ◽

Vol 4 (4) ◽

Cited By ~ 5

Author(s):

Benjamin C. Creekmore ◽

Josh H. Gray ◽

William G. Walton ◽

Kristen A. Biernat ◽

Michael S. Little ◽

...

Keyword(s):

Protein Structure ◽

Active Site ◽

Human Microbiome ◽

Drug Efficacy ◽

Human Microbiome Project ◽

Structural Features ◽

Model Organisms ◽

Mouse Strains ◽

Sequencing Data ◽

Metagenome Analysis

ABSTRACT Gut microbial β-glucuronidase (GUS) enzymes play important roles in drug efficacy and toxicity, intestinal carcinogenesis, and mammalian-microbial symbiosis. Recently, the first catalog of human gut GUS proteins was provided for the Human Microbiome Project stool sample database and revealed 279 unique GUS enzymes organized into six categories based on active-site structural features. Because mice represent a model biomedical research organism, here we provide an analogous catalog of mouse intestinal microbial GUS proteins—a mouse gut GUSome. Using metagenome analysis guided by protein structure, we examined 2.5 million unique proteins from a comprehensive mouse gut metagenome created from several mouse strains, providers, housing conditions, and diets. We identified 444 unique GUS proteins and organized them into six categories based on active-site features, similarly to the human GUSome analysis. GUS enzymes were encoded by the major gut microbial phyla, including Firmicutes (60%) and Bacteroidetes (21%), and there were nearly 20% for which taxonomy could not be assigned. No differences in gut microbial gus gene composition were observed for mice based on sex. However, mice exhibited gus differences based on active-site features associated with provider, location, strain, and diet. Furthermore, diet yielded the largest differences in gus composition. Biochemical analysis of two low-fat-associated GUS enzymes revealed that they are variable with respect to their efficacy of processing both sulfated and nonsulfated heparan nonasaccharides containing terminal glucuronides. IMPORTANCE Mice are commonly employed as model organisms of mammalian disease; as such, our understanding of the compositions of their gut microbiomes is critical to appreciating how the mouse and human gastrointestinal tracts mirror one another. GUS enzymes, with importance in normal physiology and disease, are an attractive set of proteins to use for such analyses. Here we show that while the specific GUS enzymes differ at the sequence level, a core GUSome functionality appears conserved between mouse and human gastrointestinal bacteria. Mouse strain, provider, housing location, and diet exhibit distinct GUSomes and gus gene compositions, but sex seems not to affect the GUSome. These data provide a basis for understanding the gut microbial GUS enzymes present in commonly used laboratory mice. Further, they demonstrate the utility of metagenome analysis guided by protein structure to provide specific sets of functionally related proteins from whole-genome metagenome sequencing data.

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Metabolic network-guided binning of metagenomic sequence fragments

Bioinformatics ◽

10.1093/bioinformatics/btv671 ◽

2015 ◽

Vol 32 (6) ◽

pp. 867-874 ◽

Cited By ~ 4

Author(s):

Matthew B. Biggs ◽

Jason A. Papin

Keyword(s):

Metabolic Network ◽

Dna Sequences ◽

Metabolic Networks ◽

Human Microbiome ◽

Environmental Dna ◽

Human Microbiome Project ◽

Supplementary Information ◽

Metagenomic Sequence ◽

Connectivity Score ◽

Genome Scale

Abstract Motivation: Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health. Results: We present the SOrting by NEtwork Completion (SONEC) approach for assigning reactions to incomplete metabolic networks based on a metabolite connectivity score. We successfully demonstrate proof of concept in a set of 100 genome-scale metabolic network reconstructions, and delineate the variables that impact reaction assignment accuracy. We further demonstrate the integration of SONEC with existing approaches (such as cross-sample scaffold abundance profile clustering) on a set of 94 metagenomic samples from the Human Microbiome Project. We show that not only does SONEC aid in reconstructing species-level genomes, but it also improves functional predictions made with the resulting metabolic networks. Availability and implementation: The datasets and code presented in this work are available at: https://bitbucket.org/mattbiggs/sorting_by_network_completion/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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A resistome roadmap: from the human body to pristine environments

10.1101/2021.10.08.463752 ◽

2021 ◽

Author(s):

Lucia Maestre-Carballa ◽

Manuel Martínez-García ◽

Vicente Navarro-López

Keyword(s):

Oral Cavity ◽

Resistance Genes ◽

Human Body ◽

Human Microbiome ◽

Antibiotic Resistance Genes ◽

Human Microbiome Project ◽

Individual Variability ◽

Body Parts ◽

Sequencing Data ◽

The One

A comprehensive characterization of the human body resistome (sets of antibiotic resistance genes (ARGs)) is yet to be done and paramount for addressing the antibiotic microbial resistance threat. Here, we study the resistome of 771 samples from five major body parts (skin, nares, vagina, gut and oral cavity) of healthy subjects from the Human Microbiome Project and addressed the potential dispersion of ARGs in pristine environments. A total of 28,731 ARGs belonging to 344 different ARG types were found in the HMP proteome dataset (n=9.1x107 proteins analyzed). Our study reveals a distinct resistome profile (ARG type and abundance) between body sites and high inter-individual variability. Nares had the highest ARG load (≈5.4 genes/genome) followed by the oral cavity, while the gut showed one of the highest ARG richness (shared with nares) but the lowest abundance (≈1.3 genes/genome). Fluroquinolone resistance genes were the most abundant in the human body, followed by macrolide-lincosamide-streptogramin (MLS) or tetracycline. Most of the ARGs belonged to common bacterial commensals and multidrug resistance trait was predominant in the nares and vagina. Our data also provide hope, since the spread of common ARG from the human body to pristine environments (n=271 samples; 77 Gb of sequencing data and 2.1x108 proteins analyzed) thus far remains very unlikely (only one case found in an autochthonous bacterium from a pristine environment). These findings broaden our understanding of ARG in the context of the human microbiome and the One-Health Initiative of WHO uniting human host-microbes and environments as a whole.

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Tag-systems for the Hilbert curve

Discrete Mathematics & Theoretical Computer Science ◽

10.46298/dmtcs.416 ◽

2007 ◽

Vol Vol. 9 no. 2 ◽

Author(s):

Patrice Séébold

Keyword(s):

Hilbert Space ◽

Hilbert Curve ◽

Space Filling ◽

Space Filling Curve ◽

Finite Approximations ◽

Infinite Word ◽

International Audience ◽

Filling Curve

International audience Hilbert words correspond to finite approximations of the Hilbert space filling curve. The Hilbert infinite word H is obtained as the limit of these words. It gives a description of the Hilbert (infinite) curve. We give a uniform tag-system to generate automatically H and, by showing that it is almost cube-free, we prove that it cannot be obtained by simply iterating a morphism.

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On the Behaviour of p-Adic Scaled Space Filling Curve Indices for High-Dimensional Data

The Computer Journal ◽

10.1093/comjnl/bxaa036 ◽

2020 ◽

Author(s):

Patrick Erik Bradley ◽

Markus Wilhelm Jahn

Keyword(s):

Gray Code ◽

High Dimensional ◽

Affine Transformations ◽

Hilbert Curve ◽

Space Filling ◽

Real World Data ◽

Space Filling Curve ◽

Indexing Method ◽

Filling Curve ◽

Hilbert Curves

Abstract Space filling curves are widely used in computer science. In particular, Hilbert curves and their generalizations to higher dimension are used as an indexing method because of their nice locality properties. This article generalizes this concept to the systematic construction of $p$-adic versions of Hilbert curves based on special affine transformations of the $p$-adic Gray code and develops a scaled indexing method for data taken from high-dimensional spaces based on these new curves, which with increasing dimension is shown to be less space consuming than the optimal standard static Hilbert curve index. A measure is derived, which allows to assess the local sparsity of a dataset, and is tested on some real-world data.

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A metagenomic strategy for harnessing the chemical repertoire of the human microbiome

Science ◽

10.1126/science.aax9176 ◽

2019 ◽

Vol 366 (6471) ◽

pp. eaax9176 ◽

Cited By ~ 17

Author(s):

Yuki Sugimoto ◽

Francine R. Camacho ◽

Shuo Wang ◽

Pranatchareeya Chankhamjon ◽

Arman Odabas ◽

...

Keyword(s):

Small Molecules ◽

Host Defense ◽

Computational Algorithm ◽

Human Microbiome ◽

Antibacterial Activities ◽

Biologically Active ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Niche Competition ◽

Made In

Extensive progress has been made in determining the effects of the microbiome on human physiology and disease, but the underlying molecules and mechanisms governing these effects remain largely unexplored. Here, we combine a new computational algorithm with synthetic biology to access biologically active small molecules encoded directly in human microbiome–derived metagenomic sequencing data. We discover that members of a clinically used class of molecules are widely encoded in the human microbiome and that they exert potent antibacterial activities against neighboring microbes, implying a possible role in niche competition and host defense. Our approach paves the way toward a systematic unveiling of the chemical repertoire encoded by the human microbiome and provides a generalizable platform for discovering molecular mediators of microbiome-host and microbiome-microbiome interactions.

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