scholarly journals Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12198
Author(s):  
Nicholas D. Youngblut ◽  
Ruth E. Ley
Keyword(s):  
Functional Diversity ◽  
Genomic Data ◽  
Metagenomic Data ◽  
Gene Sequences ◽  
Read Mapping ◽  
Microbial Genomes ◽  
Individual Gene ◽  
Database Construction ◽  
Microbiome Diversity ◽  
Reference Databases

Mapping metagenome reads to reference databases is the standard approach for assessing microbial taxonomic and functional diversity from metagenomic data. However, public reference databases often lack recently generated genomic data such as metagenome-assembled genomes (MAGs), which can limit the sensitivity of read-mapping approaches. We previously developed the Struo pipeline in order to provide a straight-forward method for constructing custom databases; however, the pipeline does not scale well enough to cope with the ever-increasing number of publicly available microbial genomes. Moreover, the pipeline does not allow for efficient database updating as new data are generated. To address these issues, we developed Struo2, which is >3.5 fold faster than Struo at database generation and can also efficiently update existing databases. We also provide custom Kraken2, Bracken, and HUMAnN3 databases that can be easily updated with new genomes and/or individual gene sequences. Efficient database updating, coupled with our pre-generated databases, enables “assembly-enhanced” profiling, which increases database comprehensiveness via inclusion of native genomic content. Inclusion of newly generated genomic content can greatly increase database comprehensiveness, especially for understudied biomes, which will enable more accurate assessments of microbiome diversity.

scholarly journals Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets

2021 ◽  
Author(s):  
Nicholas D. Youngblut ◽  
Ruth E. Ley
Keyword(s):  
Large Scale ◽  
Genomic Data ◽  
Metagenomic Data ◽  
List Type ◽  
Read Mapping ◽  
Microbial Genomes ◽  
Individual Gene ◽  
Link Type ◽  
Database Construction ◽  
Reference Databases

AbstractMapping metagenome reads to reference databases is the standard approach for assessing microbial taxonomic and functional diversity from metagenomic data. However, public reference databases often lack recently generated genomic data such as metagenome-assembled genomes (MAGs), which can limit the sensitivity of read-mapping approaches. We previously developed the Struo pipeline in order to provide a straight-forward method for constructing custom databases; however, the pipeline does not scale well with the ever-increasing number of publicly available microbial genomes. Moreover, the pipeline does not allow for efficient database updating as new data are generated. To address these issues, we developed Struo2, which is >3.5-fold faster than Struo at database generation and can also efficiently update existing databases. We also provide custom Kraken2, Bracken, and HUMAnN3 databases that can be easily updated with new genomes and/or individual gene sequences. Struo2 enables feasible database generation for continually increasing large-scale genomic datasets.AvailabilityStruo2: https://github.com/leylabmpi/Struo2Pre-built databases: http://ftp.tue.mpg.de/ebio/projects/struo2/Utility tools: https://github.com/nick-youngblut/gtdb_to_taxdump

scholarly journals metaXplor: an interactive viral and microbial metagenomic data manager

GigaScience ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Guilhem Sempéré ◽  
Adrien Pétel ◽  
Magsen Abbé ◽  
Pierre Lefeuvre ◽  
Philippe Roumagnac ◽  
...  
Keyword(s):  
Heterogeneous Data ◽  
Metagenomic Data ◽  
Online Data ◽  
Data Repositories ◽  
Ongoing Research ◽  
Efficient Management ◽  
Public Data ◽  
Reference Databases ◽  
Interactive Data ◽  
User Friendly

Abstract Background Efficiently managing large, heterogeneous data in a structured yet flexible way is a challenge to research laboratories working with genomic data. Specifically regarding both shotgun- and metabarcoding-based metagenomics, while online reference databases and user-friendly tools exist for running various types of analyses (e.g., Qiime, Mothur, Megan, IMG/VR, Anvi'o, Qiita, MetaVir), scientists lack comprehensive software for easily building scalable, searchable, online data repositories on which they can rely during their ongoing research. Results metaXplor is a scalable, distributable, fully web-interfaced application for managing, sharing, and exploring metagenomic data. Being based on a flexible NoSQL data model, it has few constraints regarding dataset contents and thus proves useful for handling outputs from both shotgun and metabarcoding techniques. By supporting incremental data feeding and providing means to combine filters on all imported fields, it allows for exhaustive content browsing, as well as rapid narrowing to find specific records. The application also features various interactive data visualization tools, ways to query contents by BLASTing external sequences, and an integrated pipeline to enrich assignments with phylogenetic placements. The project home page provides the URL of a live instance allowing users to test the system on public data. Conclusion metaXplor allows efficient management and exploration of metagenomic data. Its availability as a set of Docker containers, making it easy to deploy on academic servers, on the cloud, or even on personal computers, will facilitate its adoption.

scholarly journals PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11845
Author(s):  
Maxime Borry ◽  
Alexander Hübner ◽  
Adam B. Rohrlach ◽  
Christina Warinner
Keyword(s):  
Dna Damage ◽  
Functional Diversity ◽  
Ancient Dna ◽  
Damage Identification ◽  
De Novo ◽  
Metagenomic Data ◽  
Cytosine Deamination ◽  
High Data ◽  
Manual Inspection ◽  
Gene Catalogue

DNA de novo assembly can be used to reconstruct longer stretches of DNA (contigs), including genes and even genomes, from short DNA sequencing reads. Applying this technique to metagenomic data derived from archaeological remains, such as paleofeces and dental calculus, we can investigate past microbiome functional diversity that may be absent or underrepresented in the modern microbiome gene catalogue. However, compared to modern samples, ancient samples are often burdened with environmental contamination, resulting in metagenomic datasets that represent mixtures of ancient and modern DNA. The ability to rapidly and reliably establish the authenticity and integrity of ancient samples is essential for ancient DNA studies, and the ability to distinguish between ancient and modern sequences is particularly important for ancient microbiome studies. Characteristic patterns of ancient DNA damage, namely DNA fragmentation and cytosine deamination (observed as C-to-T transitions) are typically used to authenticate ancient samples and sequences, but existing tools for inspecting and filtering aDNA damage either compute it at the read level, which leads to high data loss and lower quality when used in combination with de novo assembly, or require manual inspection, which is impractical for ancient assemblies that typically contain tens to hundreds of thousands of contigs. To address these challenges, we designed PyDamage, a robust, automated approach for aDNA damage estimation and authentication of de novo assembled aDNA. PyDamage uses a likelihood ratio based approach to discriminate between truly ancient contigs and contigs originating from modern contamination. We test PyDamage on both on simulated aDNA data and archaeological paleofeces, and we demonstrate its ability to reliably and automatically identify contigs bearing DNA damage characteristic of aDNA. Coupled with aDNA de novo assembly, Pydamage opens up new doors to explore functional diversity in ancient metagenomic datasets.

scholarly journals CAMITAX: Taxon labels for microbial genomes

2019 ◽  
Author(s):  
Andreas Bremges ◽  
Adrian Fritz ◽  
Alice C. McHardy
Keyword(s):  
Single Cells ◽  
Ensemble Classification ◽  
Rrna Gene ◽  
Genome Sequences ◽  
Microbial Genomes ◽  
Phylogenetic Placement ◽  
Gene Homology ◽  
Reference Databases ◽  
Taxonomic Assignments ◽  
Genome Distance

The number of microbial genome sequences is growing exponentially, also thanks to recent advances in recovering complete or near-complete genomes from metagenomes and single cells. Assigning reliable taxon labels to genomes is key and often a prerequisite for downstream analyses. We introduce CAMITAX, a scalable and reproducible workflow for the taxonomic labelling of microbial genomes recovered from isolates, single cells, and metagenomes. CAMI-TAX combines genome distance-, 16S rRNA gene-, and gene homology-based taxonomic assignments with phylogenetic placement. It uses Nextflow to orchestrate reference databases and software containers, and thus combines ease of installation and use with computational re-producibility. We evaluated the method on several hundred metagenome-assembled genomes with high-quality taxonomic annotations from the TARA Oceans project, and show that the ensemble classification method in CAMITAX improved on all individual methods across tested ranks. While we initially developed CAMITAX to aid the Critical Assessment of Metagenome Interpretation (CAMI) initiative, it evolved into a comprehensive software to reliably assign taxon labels to microbial genomes. CAMITAX is available under the Apache License 2.0 at: https://github.com/CAMI-challenge/CAMITAX

scholarly journals IMG-ABC: A Knowledge Base To Fuel Discovery of Biosynthetic Gene Clusters and Novel Secondary Metabolites

mBio ◽  
2015 ◽  
Vol 6 (4) ◽  
Author(s):  
Michalis Hadjithomas ◽  
I-Min Amy Chen ◽  
Ken Chu ◽  
Anna Ratner ◽  
Krishna Palaniappan ◽  
...  
Keyword(s):  
Secondary Metabolites ◽  
Secondary Metabolism ◽  
Genomic Data ◽  
Gene Clusters ◽  
Metagenomic Data ◽  
Integrated Analysis ◽  
Analysis Tool ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Analysis Tools

ABSTRACTIn the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of “big” genomic data for discovering small molecules. IMG-ABC relies on IMG's comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve as the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC's focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time inAlphaproteobacteria. IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules.IMPORTANCEIMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG's extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to expand, with the goal of becoming an essential component of any bioinformatic exploration of the secondary metabolism world.

scholarly journals Combining NCBI and BOLD databases for OTU assignment in metabarcoding and metagenomic data: The BOLD_NCBI _Merger

2017 ◽  
Author(s):  
Jan-Niklas Macher ◽  
Till-Hendrik Macher ◽  
Florian Leese
Keyword(s):  
Sequence Data ◽  
Metagenomic Data ◽  
Biodiversity Assessment ◽  
Ecological Studies ◽  
Reliability Of Results ◽  
Taxonomic Assignment ◽  
Taxonomic Information ◽  
Reference Databases ◽  
Taxonomic Groups ◽  
Combine Sequence

Metabarcoding and metagenomic approaches are becoming routine techniques in biodiversity assessment and ecological studies. The assignment of taxonomic information to sequences is challenging, as many reference libraries are lacking information on certain taxonomic groups and can contain erroneous sequences. Combining different reference databases is therefore a promising approach for maximizing taxonomic coverage and reliability of results. This tutorial shows how to use the “BOLD_NCBI_Merger” script to combine sequence data obtained from the National Center for Biotechnology Information (NCBI) GenBank and the Barcode of Life Database (BOLD) and prepare it for taxonomic assignment with the software MEGAN.

scholarly journals From single nuclei to whole genome assemblies

2019 ◽  
Author(s):  
Merce Montoliu-Nerin ◽  
Marisol Sánchez-García ◽  
Claudia Bergin ◽  
Manfred Grabherr ◽  
Barbara Ellis ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Genomic Data ◽  
Life Cycles ◽  
Genomic Research ◽  
Metagenomic Data ◽  
Model Organisms ◽  
Genomic Study ◽  
And Function ◽  
Genome Assemblies

SummaryA large proportion of Earth's biodiversity constitutes organisms that cannot be cultured, have cryptic life-cycles and/or live submerged within their substrates1–4. Genomic data are key to unravel both their identity and function5. The development of metagenomic methods6,7 and the advent of single cell sequencing8–10 have revolutionized the study of life and function of cryptic organisms by upending the need for large and pure biological material, and allowing generation of genomic data from complex or limited environmental samples. Genome assemblies from metagenomic data have so far been restricted to organisms with small genomes, such as bacteria11, archaea12 and certain eukaryotes13. On the other hand, single cell technologies have allowed the targeting of unicellular organisms, attaining a better resolution than metagenomics8,9,14–16, moreover, it has allowed the genomic study of cells from complex organisms one cell at a time17,18. However, single cell genomics are not easily applied to multicellular organisms formed by consortia of diverse taxa, and the generation of specific workflows for sequencing and data analysis is needed to expand genomic research to the entire tree of life, including sponges19, lichens3,20, intracellular parasites21,22, and plant endophytes23,24. Among the most important plant endophytes are the obligate mutualistic symbionts, arbuscular mycorrhizal (AM) fungi, that pose an additional challenge with their multinucleate coenocytic mycelia25. Here, the development of a novel single nuclei sequencing and assembly workflow is reported. This workflow allows, for the first time, the generation of reference genome assemblies from large scale, unbiased sorted, and sequenced AM fungal nuclei circumventing tedious, and often impossible, culturing efforts. This method opens infinite possibilities for studies of evolution and adaptation in these important plant symbionts and demonstrates that reference genomes can be generated from complex non-model organisms by isolating only a handful of their nuclei.

scholarly journals HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data

2020 ◽  
Author(s):  
Evangelos A. Dimopoulos ◽  
Alberto Carmagnini ◽  
Irina M. Velsko ◽  
Christina Warinner ◽  
Greger Larson ◽  
...  
Keyword(s):  
Species Identification ◽  
High Throughput Sequencing ◽  
Bayesian Framework ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Taxonomic Assignment ◽  
Species Classification ◽  
High Throughput Sequencing Data ◽  
Reference Databases ◽  
Specific Species

Identification of specific species in metagenomic samples is critical for several key applications, yet many tools available require large computational power and are often prone to false positive identifications. Here we describe High-AccuracY and Scalable Taxonomic Assignment of MetagenomiC data (HAYSTAC), which can estimate the probability that a specific taxon is present in a metagenome. HAYSTAC provides a user-friendly tool to construct databases, based on publicly available genomes, that are used for competitive reads mapping. It then uses a novel Bayesian framework to infer the abundance and statistical support for each species identification, and provide per-read species classification. Unlike other methods, HAYSTAC is specifically designed to efficiently handle both ancient and modern DNA data, as well as incomplete reference databases, making it possible to run highly accurate hypothesis-driven analyses (i.e., assessing the presence of a specific species) on variably sized reference databases while dramatically improving processing speeds. We tested the performance and accuracy of HAYSTAC using simulated Illumina libraries, both with and without ancient DNA damage, and compared the results to other currently available methods (i.e., Kraken2/Braken, MALT/HOPS, and Sigma). HAYSTAC identified fewer false positives than both Kraken2/Braken and MALT in all simulations, and fewer than Sigma in simulations of ancient data. It uses less memory than Kraken2/Braken as well as MALT both during database construction and sample analysis. Lastly, we used HAYSTAC to search for specific pathogens in two published ancient metagenomic datasets, demonstrating how it can be applied to empirical datasets. HAYSTAC is available from https://github.com/antonisdim/HAYSTAC

scholarly journals Precision Food Safety: a Paradigm Shift in Detection and Control of Foodborne Pathogens

mSystems ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Jasna Kovac
Keyword(s):  
Food Safety ◽  
Foodborne Pathogens ◽  
Functional Characterization ◽  
Foodborne Pathogen ◽  
Genomic Data ◽  
Metagenomic Data ◽  
Whole Genome ◽  
Pathogenic Potential ◽  
Outbreak Investigations ◽  
Ongoing Surveillance

ABSTRACT The implementation of whole-genome sequencing in food safety has revolutionized foodborne pathogen tracking and outbreak investigations. The vast amounts of genomic data that are being produced through ongoing surveillance efforts continue advancing our understanding of pathogen diversity and genome biology. Produced genomic data are also supporting the use of metagenomics and metatranscriptomics for detection and functional characterization of microbiological hazards in foods and food processing environments. In addition to that, many studies have shown that metabolic and pathogenic potential, antimicrobial resistance, and other phenotypes relevant to food safety can be predicted from whole-genome sequences, omitting the need for multiple laboratory tests. Nevertheless, further work in the area of functional inference is necessary to enable accurate interpretation of functional information inferred from genomic and metagenomic data, as well as real-time detection and tracking of high-risk pathogen subtypes and microbiomes.

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