scholarly journals Microbiome Search Engine 2: a Platform for Taxonomic and Functional Search of Global Microbiomes on the Whole-Microbiome Level

mSystems ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Gongchao Jing ◽  
Lu Liu ◽  
Zengbin Wang ◽  
Yufeng Zhang ◽  
Li Qian ◽  
...  
Keyword(s):  
Big Data ◽  
User Interface ◽  
Search Engine ◽  
Functional Similarity ◽  
Metagenomic Data ◽  
Data Sets ◽  
Data Space ◽  
Link Type ◽  
Database Platform ◽  
Microbiome Data

ABSTRACT Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (<0.5 s per query) searches against the entire database for best-matched microbiomes using overall taxonomic or functional profiles, and (iii) a Web-based graphical user interface for user-friendly searching, data browsing, and tutoring. MSE 2 is freely accessible via http://mse.ac.cn. For standalone searches of customized microbiome databases, the kernel of the MSE 2 search engine is provided at GitHub (https://github.com/qibebt-bioinfo/meta-storms). IMPORTANCE A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Key improvements include database extension, data compatibility, a search engine kernel, and a user interface. The new ability to search the microbiome space via functional similarity greatly expands the scope of search-based mining of the microbiome big data.

scholarly journals Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature

2020 ◽  
Author(s):  
Abdullatif Köksal ◽  
Hilal Dönmez ◽  
Rıza Özçelik ◽  
Elif Ozkirimli ◽  
Arzucan Özgür
Keyword(s):  
User Interface ◽  
Search Engine ◽  
Current Literature ◽  
Search Engines ◽  
General Purpose ◽  
Inverted Index ◽  
Related Protein ◽  
Domain Specific ◽  
Link Type

AbstractCoronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/.

scholarly journals An Extensive Meta-Metagenomic Search Identifies SARS-CoV-2-Homologous Sequences in Pangolin Lung Viromes

mSphere ◽  
2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Lamia Wahba ◽  
Nimit Jain ◽  
Andrew Z. Fire ◽  
Massa J. Shoura ◽  
Karen L. Artiles ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
High Speed ◽  
High Throughput Sequencing ◽  
Biological Significance ◽  
Metagenomic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Data Set ◽  
Link Type ◽  
Recent Emergence

ABSTRACT In numerous instances, tracking the biological significance of a nucleic acid sequence can be augmented through the identification of environmental niches in which the sequence of interest is present. Many metagenomic data sets are now available, with deep sequencing of samples from diverse biological niches. While any individual metagenomic data set can be readily queried using web-based tools, meta-searches through all such data sets are less accessible. In this brief communication, we demonstrate such a meta-metagenomic approach, examining close matches to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in all high-throughput sequencing data sets in the NCBI Sequence Read Archive accessible with the “virome” keyword. In addition to the homology to bat coronaviruses observed in descriptions of the SARS-CoV-2 sequence (F. Wu, S. Zhao, B. Yu, Y. M. Chen, et al., Nature 579:265–269, 2020, https://doi.org/10.1038/s41586-020-2008-3; P. Zhou, X. L. Yang, X. G. Wang, B. Hu, et al., Nature 579:270–273, 2020, https://doi.org/10.1038/s41586-020-2012-7), we note a strong homology to numerous sequence reads in metavirome data sets generated from the lungs of deceased pangolins reported by Liu et al. (P. Liu, W. Chen, and J. P. Chen, Viruses 11:979, 2019, https://doi.org/10.3390/v11110979). While analysis of these reads indicates the presence of a similar viral sequence in pangolin lung, the similarity is not sufficient to either confirm or rule out a role for pangolins as an intermediate host in the recent emergence of SARS-CoV-2. In addition to the implications for SARS-CoV-2 emergence, this study illustrates the utility and limitations of meta-metagenomic search tools in effective and rapid characterization of potentially significant nucleic acid sequences. IMPORTANCE Meta-metagenomic searches allow for high-speed, low-cost identification of potentially significant biological niches for sequences of interest.

scholarly journals Analyzing Large Microbiome Datasets Using Machine Learning and Big Data

2021 ◽  
Vol 1 (3) ◽  
pp. 138-165
Author(s):  
Thomas Krause ◽  
Jyotsna Talreja Wassan ◽  
Paul Mc Kevitt ◽  
Haiying Wang ◽  
Huiru Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Metagenomic Data ◽  
Data Sets ◽  
Raw Data ◽  
Public And Private ◽  
Rumen Microbiome

Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw data, which is a challenge for data processing but also an opportunity for advanced machine learning methods like deep learning that require large datasets. However, in contrast to classical machine learning algorithms, the use of deep learning in metagenomics is still an exception. Regardless of the algorithms used, they are usually not applied to raw data but require several preprocessing steps. Performing this preprocessing and the actual analysis in an automated, reproducible, and scalable way is another challenge. This and other challenges can be addressed by adjusting known big data methods and architectures to the needs of microbiome analysis and DNA sequence processing. A conceptual architecture for the use of machine learning and big data on metagenomic data sets was recently presented and initially validated to analyze the rumen microbiome. The same architecture can be used for clinical purposes as is discussed in this paper.

scholarly journals MegaGO: a fast yet powerful approach to assess functional similarity across meta-omics data sets

2020 ◽  
Author(s):  
Pieter Verschaffelt ◽  
Tim Van Den Bossche ◽  
Wassim Gabriel ◽  
Michał Burdukiewicz ◽  
Alessio Soggiu ◽  
...  
Keyword(s):  
Web Application ◽  
Functional Similarity ◽  
Data Sets ◽  
Link Type ◽  
Large Sets ◽  
Powerful Approach ◽  
Command Line Tool ◽  
Complete Set ◽  
User Friendly ◽  
Go Terms

AbstractThe study of microbiomes has gained in importance over the past few years, and has led to the fields of metagenomics, metatranscriptomics and metaproteomics. While initially focused on the study of biodiversity within these communities the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute functional similarity between two data sets. MegaGO is highly performant: each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license, and is available at https://github.com/MEGA-GO/.

scholarly journals Complex Big Data Analysis Based on Multi-granularity Generalized Functions

2018 ◽  
Vol 14 (04) ◽  
pp. 43
Author(s):  
Zhang Xueya ◽  
Jianwei Zhang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Model ◽  
Input Data ◽  
Big Data Analysis ◽  
Generalized Functions ◽  
Data Sets ◽  
Test Results ◽  
Data Space ◽  
Noise Data

<p>A new method for the big data analysis - multi-granularity generalized functions data model (referred to as MGGF for short) is put forward. This method adopts the dynamic adaptive multi-granularity clustering technique, transforms the grid like "Hard partitioning" to the input data space by the generalized functions data model (referred to as GFDM for short) into the multi-granularity partitioning, and identifies the multi-granularity pattern class in the input data space. By defining the type of the mapping relationship between the multi-granularity model class and the decision-making category ftype:Ci→y, and the concept of the Degree of Fulfillment (referred to as DoF (x)) of the input data to the classification rules of the various pattern classes, the corresponding MGGF model is established. Experimental test results of different data sets show that, compared with the GFDM method, the method proposed in this paper has better data summarization ability, stronger noise data processing ability and higher searching efficiency.</p>

scholarly journals q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data

mSystems ◽  
2018 ◽  
Vol 3 (6) ◽  
Author(s):  
Nicholas A. Bokulich ◽  
Matthew R. Dillon ◽  
Yilong Zhang ◽  
Jai Ram Rideout ◽  
Evan Bolyen ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Population Heterogeneity ◽  
Data Sets ◽  
Cross Sectional ◽  
Paired Sample ◽  
Link Type ◽  
Paired Samples ◽  
Longitudinal Sampling ◽  
Microbiome Data ◽  
Analysis Platform

ABSTRACTStudies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre-post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including interactive plotting, linear mixed-effects models, paired differences and distances, microbial interdependence testing, first differencing, longitudinal feature selection, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open-source software released under a 3-clause Berkeley Software Distribution (BSD) license and is freely available, including for commercial use.IMPORTANCELongitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity. We describe q2-longitudinal, a software plugin for longitudinal analysis of microbiome data sets in QIIME 2. The availability of longitudinal statistics and visualizations in the QIIME 2 framework will make the analysis of longitudinal data more accessible to microbiome researchers.

scholarly journals Tiara: Deep learning-based classification system for eukaryotic sequences

2021 ◽  
Author(s):  
Michał Karlicki ◽  
Stanisław Antonowicz ◽  
Anna Karnkowska
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
Metagenomic Data ◽  
Data Sets ◽  
Test Dataset ◽  
Eukaryotic Diversity ◽  
Essential Step ◽  
Organellar Genomes ◽  
Link Type

AbstractMotivationWith a large number of metagenomic datasets becoming available, the eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step towards the better understanding of eukaryotic diversity.ResultsWe developed Tiara, a deep-learning-based approach for identification of eukaryotic sequences in the metagenomic data sets. Its two-step classification process enables the classification of nuclear and organellar eukaryotic fractions and subsequently divides organellar sequences to plastidial and mitochondrial. Using test dataset, we have shown that Tiara performs similarly to EukRep for prokaryotes classification and outperformed it for eukaryotes classification with lower calculation time. Tiara is also the only available tool correctly classifying organellar sequences.Availability and implementationTiara is implemented in python 3.8, available at https://github.com/ibe-uw/tiara and tested on Unix-based systems. It is released under an open-source MIT license and documentation is available at https://ibe-uw.github.io/tiara. Version 1.0.1 of Tiara has been used for all benchmarks.

scholarly journals A Novel Sparse Compositional Technique Reveals Microbial Perturbations

mSystems ◽  
2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Cameron Martino ◽  
James T. Morton ◽  
Clarisse A. Marotz ◽  
Luke R. Thompson ◽  
Anupriya Tripathi ◽  
...  
Keyword(s):  
Beta Diversity ◽  
Matrix Completion ◽  
Salient Feature ◽  
Discriminatory Power ◽  
Data Sets ◽  
Link Type ◽  
Compositional Technique ◽  
High Discriminatory Power ◽  
Microbiome Data ◽  
Log Ratio

ABSTRACTThe central aims of many host or environmental microbiome studies are to elucidate factors associated with microbial community compositions and to relate microbial features to outcomes. However, these aims are often complicated by difficulties stemming from high-dimensionality, non-normality, sparsity, and the compositional nature of microbiome data sets. A key tool in microbiome analysis is beta diversity, defined by the distances between microbial samples. Many different distance metrics have been proposed, all with varying discriminatory power on data with differing characteristics. Here, we propose a compositional beta diversity metric rooted in a centered log-ratio transformation and matrix completion called robust Aitchison PCA. We demonstrate the benefits of compositional transformations upstream of beta diversity calculations through simulations. Additionally, we demonstrate improved effect size, classification accuracy, and robustness to sequencing depth over the current methods on several decreased sample subsets of real microbiome data sets. Finally, we highlight the ability of this new beta diversity metric to retain the feature loadings linked to sample ordinations revealing salient intercommunity niche feature importance.IMPORTANCEBy accounting for the sparse compositional nature of microbiome data sets, robust Aitchison PCA can yield high discriminatory power and salient feature ranking between microbial niches. The software to perform this analysis is available under an open-source license and can be obtained athttps://github.com/biocore/DEICODE; additionally, a QIIME 2 plugin is provided to perform this analysis athttps://library.qiime2.org/plugins/q2-deicode.

scholarly journals DOE JGI Metagenome Workflow

mSystems ◽  
2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Alicia Clum ◽  
Marcel Huntemann ◽  
Brian Bushnell ◽  
Brian Foster ◽  
Bryce Foster ◽  
...  
Keyword(s):  
Nucleic Acids ◽  
Data Processing ◽  
Microbial Communities ◽  
Taxonomic Composition ◽  
Metagenomic Data ◽  
Data Sets ◽  
Microbial Genomes ◽  
Link Type ◽  
Analysis System ◽  
Rich Data

ABSTRACT The DOE Joint Genome Institute (JGI) Metagenome Workflow performs metagenome data processing, including assembly; structural, functional, and taxonomic annotation; and binning of metagenomic data sets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) (I.-M. A. Chen, K. Chu, K. Palaniappan, A. Ratner, et al., Nucleic Acids Res, 49:D751–D763, 2021, https://doi.org/10.1093/nar/gkaa939) comparative analysis system and provided for download via the JGI data portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here, we describe the different tools, databases, and parameters used at different steps of the workflow to help with the interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a workflow description language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in the Genomes OnLine Database (GOLD) (S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, et al., Nucleic Acids Res, 49:D723–D733, 2021, https://doi.org/10.1093/nar/gkaa983). IMPORTANCE The DOE JGI Metagenome Workflow is designed for processing metagenomic data sets starting from Illumina fastq files. It performs data preprocessing, error correction, assembly, structural and functional annotation, and binning. The results of processing are provided in several standard formats, such as fasta and gff, and can be used for subsequent integration into the Integrated Microbial Genomes and Microbiomes (IMG/M) system where they can be compared to a comprehensive set of publicly available metagenomes. As of 30 July 2020, 7,155 JGI metagenomes have been processed by the DOE JGI Metagenome Workflow. Here, we present a metagenome workflow developed at the JGI that generates rich data in standard formats and has been optimized for downstream analyses ranging from assessment of the functional and taxonomic composition of microbial communities to genome-resolved metagenomics and the identification and characterization of novel taxa. This workflow is currently being used to analyze thousands of metagenomic data sets in a consistent and standardized manner.

Sign in / Sign up

Export Citation Format

Share Document