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

10.7287/peerj.preprints.3133 ◽

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.

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

10.1101/2020.12.16.419085 ◽

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

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An efficient and robust laboratory workflow and tetrapod database for larger scale eDNA studies

10.1101/345082 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jan Axtner ◽

Alex Crampton-Platt ◽

Lisa A. Hörig ◽

Azlan Mohamed ◽

Charles C.Y. Xu ◽

...

Keyword(s):

Pcr Amplification ◽

Environmental Dna ◽

Taxonomic Assignment ◽

Bioinformatic Pipeline ◽

Assignment Method ◽

Reference Databases ◽

Reference Sequences ◽

Taxonomic Groups ◽

Annotation Errors ◽

Technical Replication

AbstractBackgroundThe use of environmental DNA, ‘eDNA,’ for species detection via metabarcoding is growing rapidly. We present a co-designed lab workflow and bioinformatic pipeline to mitigate the two most important risks of eDNA: sample contamination and taxonomic mis-assignment. These risks arise from the need for PCR amplification to detect the trace amounts of DNA combined with the necessity of using short target regions due to DNA degradation.FindingsOur high-throughput workflow minimises these risks via a four-step strategy: (1) technical replication with two PCR replicates and two extraction replicates; (2) using multi-markers (12S, 16S, CytB); (3) a ‘twin-tagging,’ two-step PCR protocol;(4) use of the probabilistic taxonomic assignment method PROTAX, which can account for incomplete reference databases.As annotation errors in the reference sequences can result in taxonomic mis-assignment, we supply a protocol for curating sequence datasets. For some taxonomic groups and some markers, curation resulted in over 50% of sequences being deleted from public reference databases, due to (1) limited overlap between our target amplicon and reference sequences; (2) mislabelling of reference sequences; (3) redundancy.Finally, we provide a bioinformatic pipeline to process amplicons and conduct PROTAX assignment and tested it on an ‘invertebrate derived DNA’ (iDNA) dataset from 1532 leeches from Sabah, Malaysia. Twin-tagging allowed us to detect and exclude sequences with non-matching tags. The smallest DNA fragment (16S) amplified most frequently for all samples, but was less powerful for discriminating at species rank. Using a stringent and lax acceptance criteria we found 162 (stringent) and 190 (lax) vertebrate detections of 95 (stringent) and 109 (lax) leech samples.ConclusionsOur metabarcoding workflow should help research groups increase the robustness of their results and therefore facilitate wider usage of e/iDNA, which is turning into a valuable source of ecological and conservation information on tetrapods.

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Quantifying uncertainty of taxonomic placement in DNA barcoding and metabarcoding

10.1101/070573 ◽

2016 ◽

Cited By ~ 3

Author(s):

Panu Somervuo ◽

Douglas Yu ◽

Charles Xu ◽

Yinqiu Ji ◽

Jenni Hultman ◽

...

Keyword(s):

Dna Barcoding ◽

Dna Sequences ◽

Sequence Data ◽

Environmental Data ◽

Reference Sequence ◽

Future Research ◽

List Type ◽

Reference Databases ◽

Sequence Databases ◽

Taxonomic Groups

AbstractA crucial step in the use of DNA markers for biodiversity surveys is the assignment of Linnaean taxonomies (species, genus, etc.) to sequence reads. This allows the use of all the information known based on the taxonomic names. Taxonomic placement of DNA barcoding sequences is inherently probabilistic because DNA sequences contain errors, because there is natural variation among sequences within a species, and because reference databases are incomplete and can have false annotations. However, most existing bioinformatics methods for taxonomic placement either exclude uncertainty, or quantify it using metrics other than probability.In this paper we evaluate the performance of a recently proposed probabilistic taxonomic placement method PROTAX by applying it to both annotated reference sequence data as well as unknown environmental data. Our four case studies include contrasting taxonomic groups (fungi, bacteria, mammals, and insects), variation in the length and quality of the barcoding sequences (from individually Sanger-sequenced sequences to short Illumina reads), variation in the structures and sizes of the taxonomies (from 800 to 130 000 species), and variation in the completeness of the reference databases (representing 15% to 100% of the species).Our results demonstrate that PROTAX yields essentially unbiased assessment of probabilities of taxonomic placement, and thus that its quantification of species identification uncertainty is reliable. As expected, the accuracy of taxonomic placement increases with increasing coverage of taxonomic and reference sequence databases, and with increasing ratio of genetic variation among taxonomic levels over within taxonomic levels.Our results show that reliable species-level identification from environmental samples is still challenging, and thus neglecting identification uncertainty can lead to spurious inference. A key aim for future research is the completion and pruning of taxonomic and reference sequence databases, and making these two types of data compatible.

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Assessing accuracy and completeness of GenBank for eDNA metabarcoding: towards a reliable marine fish reference database

ARPHA Conference Abstracts ◽

10.3897/aca.4.e64671 ◽

2021 ◽

Vol 4 ◽

Author(s):

Cristina Claver ◽

Oriol Canals ◽

Naiara Rodriguez-Ezpeleta

Keyword(s):

Ribosomal Rna ◽

Gap Analysis ◽

Environmental Dna ◽

Reference Database ◽

Fish Diversity ◽

Cyt B ◽

Taxonomic Assignment ◽

Reference Databases ◽

Taxonomic Groups ◽

High Level

Environmental DNA (eDNA) metabarcoding, the process of sequencing DNA collected from the environment for producing biodiversity inventories, is increasingly being applied to assess fish diversity and distribution in marine environments. Yet, the successful application of this technique deeply relies on accurate and complete reference databases used for taxonomic assignment. The most used markers for fish eDNA metabarcoding studies are the cytochrome C oxidase subunit 1 (COI), 16S ribosomal RNA (16S), the 12S ribosomal RNA (12S) and cytochrome b (cyt b) genes, whose sequences are usually retrieved from GenBank, the largest DNA sequence database that represents a worldwide public resource for genetic studies. Thus, the completeness and accuracy of GenBank is critical to derive reliable estimations from fish eDNA metabarcoding data. Here, we have i) compiled the checklist of European marine fishes, ii) performed a gap analysis of the four genes and, within COI and 12S, also of the most used barcodes for fish, and iii) developed a workflow to detect potentially incorrect records in GenBank. We found that from the 1965 species in the checklist (1761 Actinopterygii, 189 Elasmobranchii, 9 Holocephali, 4 Petromyzonti and 2 Myxini), about 70% have sequences for COI, whereas less have sequences for 12S, 16S and cyt b (45-55%). Among the species for which COI ad 12S sequences are available, about 60% and 40% have sequences covering the most used barcodes respectively. The analysis of pairwise distances between sequences revealed pairs belonging to the same species with significantly low similarity and pairs belonging to different high level taxonomic groups (class, order) with significantly large similarity. In light of this further confirmation of presence of a substantial number of incorrect records in GenBank, we propose a method for identifying and removing spurious sequences to create reliable and accurate reference databases for eDNA metabarcoding.

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Anacapa Toolkit: an environmental DNA toolkit for processing multilocus metabarcode datasets

10.1101/488627 ◽

2018 ◽

Cited By ~ 1

Author(s):

Emily E. Curd ◽

Zack Gold ◽

Gaurav S Kandlikar ◽

Jesse Gomer ◽

Max Ogden ◽

...

Keyword(s):

High Throughput Sequencing ◽

Sequence Data ◽

Environmental Dna ◽

Taxonomic Diversity ◽

R Package ◽

Community Diversity ◽

Taxonomic Assignment ◽

Lowest Common Ancestor ◽

Reference Databases ◽

Comprehensive Reference

Abstract1. Environmental DNA (eDNA) metabarcoding is a promising method to monitor species and community diversity that is rapid, affordable, and non-invasive. Longstanding needs of the eDNA community are modular informatics tools, comprehensive and customizable reference databases, flexibility across high-throughput sequencing platforms, fast multilocus metabarcode processing, and accurate taxonomic assignment. As bioinformatics tools continue to improve, addressing each of these demands within a single bioinformatics toolkit is becoming a reality.2. We present the modular metabarcode sequence toolkit Anacapa (https://github.com/limey-bean/Anacapa/), which addresses the above needs, allowing users to build comprehensive reference databases and assign taxonomy to raw multilocus metabarcode sequence data A novel aspect of Anacapa is our database building module, Creating Reference libraries Using eXisting tools (CRUX), which generates comprehensive reference databases for specific user-defined metabarcode loci. The Quality Control and Dereplication module sorts and processes multiple metabarcode loci and processes merged, unmerged and unpaired reads maximizing recovered diversity. Followed by amplicon sequence variants (ASVs) detection using DADA2. The Anacapa Classifier module aligns these ASVs to CRUX-generated reference databases using Bowtie2. Taxonomy is assigned to ASVs with confidence scores using a Bayesian Lowest Common Ancestor (BLCA) method. The Anacapa Toolkit also includes an R package, ranacapa, for automated results exploration through standard biodiversity statistical analysis.3. We performed a series of benchmarking tests to verify that the Anacapa Toolkit generates comprehensive reference databases that capture wide taxonomic diversity and that it can assign high-quality taxonomy to both MiSeq-length and Hi-Seq length sequence data. We demonstrate the value of the Anacapa Toolkit to assigning taxonomy to eDNA sequences from seawater samples from southern California including capability of this tool kit to process multilocus metabarcoding data.4. The Anacapa Toolkit broadens the exploration of eDNA and assists in biodiversity assessment and management by generating metabarcode specific databases, processing multilocus data, retaining all read types, and expanding non-traditional eDNA targets. Anacapa software and source code are open and available in a virtual container to ease installation.

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DNA metabarcoding of littoral hard-bottom communities: high diversity and database gaps revealed by two molecular markers

PeerJ ◽

10.7717/peerj.4705 ◽

2018 ◽

Vol 6 ◽

pp. e4705 ◽

Cited By ~ 51

Author(s):

Owen S. Wangensteen ◽

Creu Palacín ◽

Magdalena Guardiola ◽

Xavier Turon

Keyword(s):

Molecular Markers ◽

Taxonomic Resolution ◽

Universal Primers ◽

Biodiversity Assessment ◽

Taxonomic Assignment ◽

Bottom Communities ◽

Dna Metabarcoding ◽

Reference Databases ◽

Hard Bottom Communities ◽

High Diversity

Biodiversity assessment of marine hard-bottom communities is hindered by the high diversity and size-ranges of the organisms present. We developed a DNA metabarcoding protocol for biodiversity characterization of structurally complex natural marine hard-bottom communities. We used two molecular markers: the “Leray fragment” of mitochondrialcytochrome c oxidase(COI), for which a novel primer set was developed, and the V7 region of the nuclear small subunit ribosomal RNA (18S). Eight different shallow marine littoral communities from two National Parks in Spain (one in the Atlantic Ocean and another in the Mediterranean Sea) were studied. Samples were sieved into three size fractions from where DNA was extracted separately. Bayesian clustering was used for delimiting molecular operational taxonomic units (MOTUs) and custom reference databases were constructed for taxonomic assignment. Despite applying stringent filters, we found high values for MOTU richness (2,510 and 9,679 MOTUs with 18S and COI, respectively), suggesting that these communities host a large amount of yet undescribed eukaryotic biodiversity. Significant gaps are still found in sequence reference databases, which currently prevent the complete taxonomic assignment of the detected sequences. In our dataset, 85% of 18S MOTUs and 64% of COI MOTUs could be identified to phylum or lower taxonomic level. Nevertheless, those unassigned were mostly rare MOTUs with low numbers of reads, and assigned MOTUs comprised over 90% of the total sequence reads. The identification rate might be significantly improved in the future, as reference databases are further completed. Our results show that marine metabarcoding, currently applied mostly to plankton or sediments, can be adapted to structurally complex hard bottom samples. Thus, eukaryotic metabarcoding emerges as a robust, fast, objective and affordable method to comprehensively characterize the diversity of marine benthic communities dominated by macroscopic seaweeds and colonial or modular sessile metazoans. The 18S marker lacks species-level resolution and thus cannot be recommended to assess the detailed taxonomic composition of these communities. Our new universal primers for COI can potentially be used for biodiversity assessment with high taxonomic resolution in a wide array of marine, terrestrial or freshwater eukaryotic communities.

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metaXplor: an interactive viral and microbial metagenomic data manager

GigaScience ◽

10.1093/gigascience/giab001 ◽

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.

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MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data

F1000Research ◽

10.12688/f1000research.18866.2 ◽

2019 ◽

Vol 8 ◽

pp. 726

Author(s):

Mike W.C. Thang ◽

Xin-Yi Chua ◽

Gareth Price ◽

Dominique Gorse ◽

Matt A. Field

Keyword(s):

Microbial Communities ◽

Sequence Data ◽

Metagenomic Data ◽

Marker Genes ◽

Metagenomic Sequencing ◽

Differential Analysis ◽

Biomedical Sciences ◽

Metagenomic Sequence ◽

Differential Abundance ◽

Differential Abundance Analysis

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences. While software for detailing the composition of microbial communities using 16S rRNA marker genes is relatively mature, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs. Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics. MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

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Accurate Reconstruction of Microbial Strains from Metagenomic Sequencing Using Representative Reference Genomes

10.1101/215707 ◽

2017 ◽

Cited By ~ 2

Author(s):

Zhemin Zhou ◽

Nina Luhmann ◽

Nabil-Fareed Alikhan ◽

Christopher Quince ◽

Mark Achtman

Keyword(s):

Evaluation Studies ◽

Species Level ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Reference Databases ◽

Microbial Strains ◽

Taxonomic Assignments ◽

Taxonomic Groups ◽

Reference Genomes ◽

Recent Evaluation

AbstractExploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with ≤ 0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones. SPARSE and all evaluation scripts are available at https://github.com/zheminzhou/SPARSE.

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