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

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.

Download Full-text

Overcoming limitations to environmental DNA studies: A coastal temperate reference sequence database for multiple chloroplast gene regions generated in a single assay.

10.22541/au.163252330.05592688/v1 ◽

2021 ◽

Author(s):

Nicole Foster ◽

Kor-jent Dijk ◽

Ed Biffin ◽

Jennifer Young ◽

Vicki Thomson ◽

...

Keyword(s):

Dna Sequences ◽

Dna Barcode ◽

Environmental Dna ◽

Reference Sequence ◽

Reference Database ◽

Chloroplast Gene ◽

Coastal Plants ◽

Reference Databases ◽

Targeted Capture ◽

Comprehensive Reference

A proliferation in environmental DNA (eDNA) research has increased the reliance on reference sequence databases to assign unknown DNA sequences to known taxa. Without comprehensive reference databases, DNA extracted from environmental samples cannot be correctly assigned to taxa, limiting the use of this genetic information to identify organisms in unknown sample mixtures. For animals, standard metabarcoding practices involve amplification of the mitochondrial Cytochrome-c oxidase subunit 1 (CO1) region, which is a universally amplifyable region across majority of animal taxa. This region, however, does not work well as a DNA barcode for plants and fungi, and there is no similar universal single barcode locus that has the same species resolution. Therefore, generating reference sequences has been more difficult and several loci have been suggested to be used in parallel to get to species identification. For this reason, we developed a multi-gene targeted capture approach to generate reference DNA sequences for plant taxa across 20 target chloroplast gene regions in a single assay. We successfully compiled a reference database for 93 temperate coastal plants including seagrasses, mangroves, and saltmarshes/samphire’s. We demonstrate the importance of a comprehensive reference database to prevent species going undetected in eDNA studies. We also investigate how using multiple chloroplast gene regions impacts the ability to discriminate between taxa.

Download Full-text

Bacteria are everywhere, even in your COI data: Τhe art of getting to know the unknown unknowns and shine light on the dark matter!

ARPHA Conference Abstracts ◽

10.3897/aca.4.e64966 ◽

2021 ◽

Vol 4 ◽

Author(s):

Haris Zafeiropoulos ◽

Laura Gargan ◽

Christina Pavloudi ◽

Evangelos Pafilis ◽

Jens Carlsson

Keyword(s):

Dark Matter ◽

Phylogenetic Tree ◽

Sequence Data ◽

Marker Gene ◽

Environmental Dna ◽

Tree Of Life ◽

Taxonomic Assignment ◽

Species Identity ◽

Consensus Sequences ◽

Coi Sequences

Environmental DNA (eDNA) metabarcoding has been commonly used in recent years (Jeunen et al. 2019) for the identification of the species composition of environmental samples. By making use of genetic markers anchored in conserved gene regions, universally present acrooss the species of large taxonomy groups, eDNA metabarcoding exploits both extra- and intra-cellular DNA fragments for biodiversity assessment. However, there is not a truly “universal” marker gene that is capable of amplifying all species across different taxa (Kress et al. 2015). The mitochondrial cytochrome C oxidase subunit I gene (COI) has many of the desirable properties of a “universal" marker and has been widely used for assessing species identity in Eukaryotes, especially metazoans (Andjar et al. 2018). However, a great number of COI Operational Taxonomic Units (OTUs) or/and Amplicon Sequence Variants (ASVs) retrieved from such studies do not match reference sequences and are often referred to as “dark matter” (Deagle et al. 2014). The aim of this study was to discover the origins and identities of these COI dark matter sequences. We built a reference phylogenetic tree that included as many COI-sequence-related information across the tree of life as possible. An overview of the steps followed is presented in Fig. 1a. Briefly, the Midori reference 2 database was used to retrieve eukaryotes sequences (183,330 species). In addition, the API of the BOLD database was used as source for the corresponding Bacteria (559 genera) and Archaea (41 genera) sequences. Consensus sequences at the family level were constructed from each of these three initial COI datasets. The COI-oriented reference phylogenetic tree of life was then built by using 1,240 consensus sequences with more than 80% of those coming from eukaryotic taxa. Phylogeny-based taxonomic assignment was then used to place query sequences. The a) total number of sequences, b) sequences assigned to Eukaryotes and c) unassigned subsets of OTUs, from marine and freshwater samples, retrieved during in-house metabarcoding experiments, were placed in the reference tree (Fig. 1b). It is clear that a large proportion of sequences targeting the COI region of Eukaryotes actually represents bacterial branches in the phylogenetic tree (Fig. 1b). We conclude that COI metabarcoding studies targeting Eukaryotes may come with a great bias derived from amplification and sequencing of bacterial taxa, depending on the primer pair used. However, for the time being, publicly available bacterial COI sequences are far too few to represent the bacterial variability; thus, a reliable taxonomic identification of them is not possible. We suggest that bacterial COI sequences should be included in the reference databases used for the taxonomy assignment of OTUs/ASVs in COI-based eukaryote metabarcoding studies to allow for bacterial sequences that were amplified to be excluded enabling researchers to exclude non-target sequences. Further, the approach presented here allows researchers to better understand the unknown unknowns and shed light on the dark matter of their metabarcoding sequence data.

Download Full-text

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.

Download Full-text

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

Download Full-text

A workflow for accurate metabarcoding using nanopore MinION sequencing

10.1101/2020.05.21.108852 ◽

2020 ◽

Cited By ~ 2

Author(s):

Bilgenur Baloğlu ◽

Zhewei Chen ◽

Vasco Elbrecht ◽

Thomas Braukmann ◽

Shanna MacDonald ◽

...

Keyword(s):

High Throughput Sequencing ◽

Sequence Data ◽

Rolling Circle Amplification ◽

Error Rates ◽

Read Length ◽

Taxonomic Assignment ◽

Major Drawback ◽

Rolling Circle ◽

Sequencing Platform ◽

Sequencing Platforms

AbstractMetabarcoding has become a common approach to the rapid identification of the species composition in a mixed sample. The majority of studies use established short-read high-throughput sequencing platforms. The Oxford Nanopore MinION™, a portable sequencing platform, represents a low-cost alternative allowing researchers to generate sequence data in the field. However, a major drawback is the high raw read error rate that can range from 10% to 22%.To test if the MinION™ represents a viable alternative to other sequencing platforms we used rolling circle amplification (RCA) to generate full-length consensus DNA barcodes (658bp of cytochrome oxidase I - COI) for a bulk mock sample of 50 aquatic invertebrate species. By applying two different laboratory protocols, we generated two MinION™ runs that were used to build consensus sequences. We also developed a novel Python pipeline, ASHURE, for processing, consensus building, clustering, and taxonomic assignment of the resulting reads.We were able to show that it is possible to reduce error rates to a median accuracy of up to 99.3% for long RCA fragments (>45 barcodes). Our pipeline successfully identified all 50 species in the mock community and exhibited comparable sensitivity and accuracy to MiSeq. The use of RCA was integral for increasing consensus accuracy, but it was also the most time-consuming step during the laboratory workflow and most RCA reads were skewed towards a shorter read length range with a median RCA fragment length of up to 1262bp. Our study demonstrates that Nanopore sequencing can be used for metabarcoding but we recommend the exploration of other isothermal amplification procedures to improve consensus length.

Download Full-text

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.

Download Full-text

Evaluation of a high-throughput, cost-effective Illumina library preparation kit

Scientific Reports ◽

10.1038/s41598-021-94911-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Eric S. Tvedte ◽

Jane Michalski ◽

Shaoji Cheng ◽

Rayanna S. Patkus ◽

Luke J. Tallon ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

De Novo ◽

Sequence Data ◽

Poor Performance ◽

Degraded Dna ◽

Library Preparation ◽

Sequencing Data ◽

Taxonomic Assignment ◽

Illumina Library

AbstractLibrary preparation for high-throughput sequencing applications is a critical step in producing representative, unbiased sequencing data. The iGenomX Riptide High Throughput Rapid Library Prep Kit purports to provide high-quality sequencing data with lower costs compared to other Illumina library kits. To test these claims, we compared sequence data quality of Riptide libraries to libraries constructed with KAPA Hyper and NEBNext Ultra. Across several single-source genome samples, mapping performance and de novo assembly of Riptide libraries were similar to conventional libraries prepared with the same DNA. Poor performance of some libraries resulted in low sequencing depth. In particular, degraded DNA samples may be challenging to sequence with Riptide. There was little cross-well plate contamination with the overwhelming majority of reads belong to the proper source genomes. The sequencing of metagenome samples using different Riptide primer sets resulted in variable taxonomic assignment of reads. Increased adoption of the Riptide kit will decrease library preparation costs. However, this method might not be suitable for degraded DNA.

Download Full-text

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

10.7287/peerj.preprints.3133v1 ◽

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

Download Full-text

BBI: an R package for the computation of Benthic Biotic Indices from composition data

Metabarcoding and Metagenomics ◽

10.3897/mbmg.2.25649 ◽

2018 ◽

Vol 2 ◽

pp. e25649 ◽

Cited By ~ 5

Author(s):

Tristan Cordier ◽

Jan Pawlowski

Keyword(s):

Deep Sea ◽

High Throughput Sequencing ◽

Environmental Dna ◽

R Package ◽

Biotic Indices ◽

Oil Drilling ◽

Composition Data ◽

Ecological Quality Status ◽

Custom Script ◽

Quality Status

The monitoring of impacts of anthropic activities in marine environments, such as aquaculture, oil-drilling platforms or deep-sea mining, relies on Benthic Biotic Indices (BBI). Several indices have been formalised to reduce the multivariate composition data into a single continuous value that is ascribed to a discrete ecological quality status. Such composition data is traditionally obtained from macrofaunal inventories, which is time-consuming and expertise-demanding. Important efforts are ongoing towards using High-Throughput Sequencing of environmental DNA (eDNA metabarcoding) to replace or complement morpho-taxonomic surveys for routine biomonitoring. The computation of BBI from such composition data is usually being undertaken by practitioners with excel spreadsheets or through custom script. Hence, the updating of reference morpho-taxonomic tables and cross studies comparison could be hampered. Here we introduce the R package BBI for the computation of BBI from composition data, either obtained from traditional morpho-taxonomic inventories or from metabarcoding data. Its aim is to provide an open-source, transparent and centralised method to compute BBI for routine biomonitoring.

Download Full-text

Standards for Methods Utilizing Environmental DNA for Detection of Fish Species

Genes ◽

10.3390/genes11030296 ◽

2020 ◽

Vol 11 (3) ◽

pp. 296 ◽

Cited By ~ 5

Author(s):

Lu Shu ◽

Arne Ludwig ◽

Zuogang Peng

Keyword(s):

Fish Species ◽

High Throughput Sequencing ◽

Quantitative Polymerase Chain Reaction ◽

Environmental Dna ◽

Aquatic Organism ◽

Negative Control ◽

Community Diversity ◽

Gene Markers ◽

Mitochondrial Cytochrome B ◽

Fish Detection

Environmental DNA (eDNA) techniques are gaining attention as cost-effective, non-invasive strategies for acquiring information on fish and other aquatic organisms from water samples. Currently, eDNA approaches are used to detect specific fish species and determine fish community diversity. Various protocols used with eDNA methods for aquatic organism detection have been reported in different eDNA studies, but there are no general recommendations for fish detection. Herein, we reviewed 168 papers to supplement and highlight the key criteria for each step of eDNA technology in fish detection and provide general suggestions for eliminating detection errors. Although there is no unified recommendation for the application of diverse eDNA in detecting fish species, in most cases, 1 or 2 L surface water collection and eDNA capture on 0.7-μm glass fiber filters followed by extraction with a DNeasy Blood and Tissue Kit or PowerWater DNA Isolation Kit are useful for obtaining high-quality eDNA. Subsequently, species-specific quantitative polymerase chain reaction (qPCR) assays based on mitochondrial cytochrome b gene markers or eDNA metabarcoding based on both 12S and 16S rRNA markers via high-throughput sequencing can effectively detect target DNA or estimate species richness. Furthermore, detection errors can be minimized by mitigating contamination, negative control, PCR replication, and using multiple genetic markers. Our aim is to provide a useful strategy for fish eDNA technology that can be applied by researchers, advisors, and managers.

Download Full-text