scholarly journals Hybridisation capture allows DNA damage analysis of ancient marine eukaryotes

2020 ◽  
Author(s):  
L. Armbrecht ◽  
G. Hallegraeff ◽  
C.J.S. Bolch ◽  
C. Woodward ◽  
A. Cooper
Keyword(s):  
Dna Damage ◽  
Relative Abundance ◽  
Ancient Dna ◽  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
Shotgun Sequencing ◽  
Metagenomic Data ◽  
Damage Analysis ◽  
Bioinformatic Tools ◽  
Bioinformatic Tool

AbstractMarine sedimentary ancient DNA (sedaDNA) is increasingly used to study past ocean ecosystems, however, studies have been severely limited by the very low amounts of DNA preserved in the subseafloor, and the lack of bioinformatic tools to authenticate sedaDNA in metagenomic data. We applied a hybridisation capture ‘baits’ technique to target marine eukaryote sedaDNA (specifically, phytoplankton, ‘Phytobaits1’; and harmful algal bloom taxa, ‘HABbaits1’), which resulted in up to 4- and 9-fold increases, respectively, in the relative abundance of eukaryotes compared to shotgun sequencing. We further used the new bioinformatic tool ‘HOPS’ to authenticate the sedaDNA component, establishing a new proxy to assess sedaDNA authenticity, the Ancient: Default (A:D) sequences ratio, here positively correlated with subseafloor depth, and generated the first-ever DNA damage profiles of a key phytoplankton, the ubiquitous coccolithophore Emiliania huxleyi. Our study opens new options for the detailed investigation of marine eukaryotes and their evolution over geological timescales.

scholarly journals Hybridisation capture allows DNA damage analysis of ancient marine eukaryotes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
L. Armbrecht ◽  
G. Hallegraeff ◽  
C. J. S. Bolch ◽  
C. Woodward ◽  
A. Cooper
Keyword(s):  
Dna Damage ◽  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
Marine Phytoplankton ◽  
Metagenomic Data ◽  
Damage Analysis ◽  
Bioinformatic Tools ◽  
Term Change ◽  
Bioinformatic Tool

AbstractMarine sedimentary ancient DNA (sedaDNA) is increasingly used to study past ocean ecosystems, however, studies have been severely limited by the very low amounts of DNA preserved in the subseafloor, and the lack of bioinformatic tools to authenticate sedaDNA in metagenomic data. We applied a hybridisation capture ‘baits’ technique to target marine eukaryote sedaDNA (specifically, phyto- and zooplankton, ‘Planktonbaits1’; and harmful algal bloom taxa, ‘HABbaits1’), which resulted in up to 4- and 9-fold increases, respectively, in the relative abundance of eukaryotes compared to shotgun sequencing. We further used the bioinformatic tool ‘HOPS’ to authenticate the sedaDNA component, establishing a new proxy to assess sedaDNA authenticity, “% eukaryote sedaDNA damage”, that is positively correlated with subseafloor depth. We used this proxy to report the first-ever DNA damage profiles from a marine phytoplankton species, the ubiquitous coccolithophore Emiliania huxleyi. Our approach opens new avenues for the detailed investigation of long-term change and evolution of marine eukaryotes over geological timescales.

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

2021 ◽  
Author(s):  
Maxime Borry ◽  
Alexander Huebner ◽  
Adam B Rohrlach ◽  
Christina G Warinner
Keyword(s):  
Dna Damage ◽  
Functional Diversity ◽  
Ancient Dna ◽  
De Novo Assembly ◽  
Damage Identification ◽  
De Novo ◽  
Metagenomic Data ◽  
Cytosine Deamination ◽  
High Data ◽  
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. However, 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 simulated, and empirical aDNA data from 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 Pathogen Species Identification from Metagenomes in Ancient Remains: The Challenge of Identifying Human Pathogenic Species of Trypanosomatidae via Bioinformatic Tools

Genes ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 418 ◽  
Author(s):  
Denis Sereno ◽  
Franck Dorkeld ◽  
Mohammad Akhoundi ◽  
Pascale Perrin
Keyword(s):  
South America ◽  
Species Identification ◽  
Ancient Dna ◽  
Metagenomic Data ◽  
Metagenomic Sequence ◽  
Bioinformatic Tools ◽  
History Of ◽  
Generation Sequencing ◽  
Ancient Remains ◽  
Memory Efficient

Accurate species identification from ancient DNA samples is a difficult task that would shed light on the evolutionary history of pathogenic microorganisms. The field of palaeomicrobiology has undoubtedly benefited from the advent of untargeted metagenomic approaches that use next-generation sequencing methodologies. Nevertheless, assigning ancient DNA at the species level is a challenging process. Recently, the gut microbiome analysis of three pre-Columbian Andean mummies (Santiago-Rodriguez et al., 2016) has called into question the identification of Leishmania in South America. The accurate assignment would be important because it will provide some key elements that are linked to the evolutionary scenario for visceral leishmaniasis agents in South America. Here, we recovered the metagenomic data filed in the metagenomics RAST server (MG-RAST) to identify the different members of the Trypanosomatidae family that have infected these ancient remains. For this purpose, we used the ultrafast metagenomic sequence classifier, based on an exact alignment of k-mers (Kraken) and Bowtie2, an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. The analyses, which have been conducted on the most exhaustive genomic database possible on Trypanosomatidae, show that species assignments could be biased by a lack of some genomic sequences of Trypanosomatidae species (strains). Nevertheless, our work raises the issue of possible co-infections by multiple members of the Trypanosomatidae family in these three pre-Columbian mummies. In the three mummies, we show the presence of DNA that is reminiscent of a probable co-infection with Leptomonas seymouri, a parasite of insect’s gut, and Lotmaria.

scholarly journals Selection of appropriate metagenome taxonomic classifiers for ancient microbiome research

2018 ◽  
Author(s):  
Irina M. Velsko ◽  
Laurent A. F. Frantz ◽  
Alexander Herbig ◽  
Greger Larson ◽  
Christina Warinner
Keyword(s):  
Dna Damage ◽  
Community Structure ◽  
Microbial Communities ◽  
Relative Abundance ◽  
Ancient Dna ◽  
In Silico ◽  
Taxonomic Classification ◽  
Taxonomic Assignment ◽  
Species Detection ◽  
Taxonomic Profiling

AbstractMetagenomics enables the study of complex microbial communities from myriad sources, including the remains of oral and gut microbiota preserved in archaeological dental calculus and paleofeces, respectively. While accurate taxonomic assignment is essential to this process, DNA damage, characteristic to ancient samples (e.g. reduction in fragment size), may reduce the accuracy of read taxonomic assignment. Using a set of in silico-generated metagenomic datasets we investigated how the addition of ancient DNA (aDNA) damage patterns influences microbial taxonomic assignment by five widely-used profilers: QIIME/UCLUST, MetaPhlAn2, MIDAS, CLARK-S, and MALT (BLAST-X-mode). In silico-generated datasets were designed to mimic dental plaque, consisting of 40, 100, and 200 microbial species/strains, both with and without simulated aDNA damage patterns. Following taxonomic assignment, the profiles were evaluated for species presence/absence, relative abundance, alpha-diversity, beta-diversity, and specific taxonomic assignment biases. Unifrac metrics indicated that both MIDAS and MetaPhlAn2 provided the most accurate community structure reconstruction. QIIME/UCLUST, CLARK-S, and MALT had the highest number of inaccurate taxonomic assignments; however, filtering out species present at <0.1% abundance greatly increased the accuracy of CLARK-S and MALT. All programs except CLARK-S failed to detect some species from the input file that were in their databases. Ancient DNA damage resulted in minimal differences in species detection and relative abundance between simulated ancient and modern datasets for most programs. In conclusion, taxonomic profiling biases are program-specific rather than damage-dependent, and the choice of taxonomic classification program to use should be tailored to the research question.ImportanceAncient biomolecules from oral and gut microbiome samples have been shown to preserve in the archaeological record. Studying ancient microbiome communities using metagenomic techniques offer a unique opportunity to reconstruct the evolutionary trajectories of microbial communities through time. DNA accumulates specific damage over time, which could potentially affect taxonomic classification and our ability to reconstruct community assemblages accurately. It is therefore necessary to assess whether ancient DNA (aDNA) damage patterns affect metagenomic taxonomic profiling. Here, we assessed biases in community structure, diversity, species detection, and relative abundance estimates by five popular metagenomic taxonomic classification programs using in silico-generated datasets with aDNA damage. Age-related damage patterns had minimal impact on the taxonomic profiles produced by each program, and biases were intrinsic to each program. Therefore, an appropriate classification program should be chosen that minimizes the biases related to the questions being addressed.

scholarly journals BENTHIC HARMFUL DINOFLAGELLATE ASSEMBLAGES IN A FRINGING REEF OF SAMPADI ISLAND, SARAWAK, MALAYSIA

2015 ◽  
Vol 38 (2) ◽  
pp. 77-87 ◽  
Author(s):  
Toh-Hii Tan ◽  
Po-Teen Lim ◽  
Aazani Mujahid ◽  
Gires Usup ◽  
Chui-Pin Leaw
Keyword(s):  
Coral Reef ◽  
Relative Abundance ◽  
Quantitative Method ◽  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
Total Cell ◽  
Artificial Substrate ◽  
Primary Concern ◽  
Cell Densities ◽  
Harmful Dinoflagellate

A study on the presence and relative abundance of benthic harmful algal bloom (BHAB) forming dinoflagellate species was carried out in the coral reefs of Sampadi Island, Sarawak, Malaysia. The study involved deployment of fiberglass screens as an artificial substrate for the benthic epiphytic microalgae. The screens were placed for 24 h above the seafloor along a 100 m transect at 10 m intervals. BHAB species attached to the screens were identified and cell abundances were enumerated under a light microscope. The BHAB community at the study site was dominated by Prorocentrum spp. and Coolia spp. Other BHAB species collected included Amphidinium spp., Gambierdiscus spp. and Ostreopsis spp. Total cell densities collected on the screens ranged from 5 to 100 cells per 100 cm2. The two BHAB groups of primary concern, Gambierdiscus spp. and Ostreopsis spp. were detected at relatively low abundances of 0.6–4.2% and 1.8–16% respectively. This study has shown that potentially toxic BHAB species were present in the coral reef and the artificial substrate approach could provide a convenient quantitative method for the collection of clean samples for identification and enumeration purposes.

scholarly journals Pathogen Species Identification from Metagenomes in Ancient Remains: the Challenge of Identifying Human Pathogenic Species of Trypanosomatidae Via Bioinformatic Tools

2018 ◽  
Author(s):  
Denis Sereno ◽  
Franck Dorkeld ◽  
Mohammad Akhoundi ◽  
Pascale Perrin
Keyword(s):  
Species Identification ◽  
Ancient Dna ◽  
Metagenomic Data ◽  
Efficient Tool ◽  
Metagenomic Sequence ◽  
Bioinformatic Tools ◽  
History Of ◽  
Generation Sequencing ◽  
Ancient Remains ◽  
Memory Efficient

Proper species identification from ancient DNA samples is a difficult task that sheds light on the evolutionary history of pathogenic microorganisms. The field of palaeomicrobiology has undoubtedly benefited from the advent of untargeted metagenomic approaches that use next-generation sequencing methodologies. Nevertheless, assigning ancient DNA at the species level is a challenging process. Recently, the gut microbiome analysis of three pre-Columbian Andean mummies [1](Santiago-Rodriguez et al. 2016) has called into question the identification of Leishmania in South America. Here, the metagenomic data filed in MG-RAST (Metagenomics RAST server) were used for a further attempt to identify members of the Trypanosomatidae family infecting these ancient remains. For this purpose, we used two metagenomic analysis tools. In the first step, data were analysed using the ultrafast metagenomic sequence classifier, based on exact alignment of k-mers (Kraken). In the second step, we used Bowtie2, an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. We then compared the output results. These approaches highlight some interesting findings on potential infections by human pathogenic trypanosomatids in these three pre-Columbian mummies.

scholarly journals Selection of Appropriate Metagenome Taxonomic Classifiers for Ancient Microbiome Research

mSystems ◽  
2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Irina M. Velsko ◽  
Laurent A. F. Frantz ◽  
Alexander Herbig ◽  
Greger Larson ◽  
Christina Warinner
Keyword(s):  
Dna Damage ◽  
Community Structure ◽  
Microbial Communities ◽  
Relative Abundance ◽  
Ancient Dna ◽  
In Silico ◽  
Taxonomic Classification ◽  
Data Sets ◽  
Taxonomic Assignment ◽  
Taxonomic Profiling

ABSTRACT Metagenomics enables the study of complex microbial communities from myriad sources, including the remains of oral and gut microbiota preserved in archaeological dental calculus and paleofeces, respectively. While accurate taxonomic assignment is essential to this process, DNA damage characteristic of ancient samples (e.g., reduction in fragment size and cytosine deamination) may reduce the accuracy of read taxonomic assignment. Using a set of in silico-generated metagenomic data sets, we investigated how the addition of ancient DNA (aDNA) damage patterns influences microbial taxonomic assignment by five widely used profilers: QIIME/UCLUST, MetaPhlAn2, MIDAS, CLARK-S, and MALT. In silico-generated data sets were designed to mimic dental plaque, consisting of 40, 100, and 200 microbial species/strains, both with and without simulated aDNA damage patterns. Following taxonomic assignment, the profiles were evaluated for species presence/absence, relative abundance, alpha diversity, beta diversity, and specific taxonomic assignment biases. Unifrac metrics indicated that both MIDAS and MetaPhlAn2 reconstructed the most accurate community structure. QIIME/UCLUST, CLARK-S, and MALT had the highest number of inaccurate taxonomic assignments; false-positive rates were highest by CLARK-S and QIIME/UCLUST. Filtering out species present at <0.1% abundance greatly increased the accuracy of CLARK-S and MALT. All programs except CLARK-S failed to detect some species from the input file that were in their databases. The addition of ancient DNA damage resulted in minimal differences in species detection and relative abundance between simulated ancient and modern data sets for most programs. Overall, taxonomic profiling biases are program specific rather than damage dependent, and the choice of taxonomic classification program should be tailored to specific research questions. IMPORTANCE Ancient biomolecules from oral and gut microbiome samples have been shown to be preserved in the archaeological record. Studying ancient microbiome communities using metagenomic techniques offers a unique opportunity to reconstruct the evolutionary trajectories of microbial communities through time. DNA accumulates specific damage over time, which could potentially affect taxonomic classification and our ability to accurately reconstruct community assemblages. It is therefore necessary to assess whether ancient DNA (aDNA) damage patterns affect metagenomic taxonomic profiling. Here, we assessed biases in community structure, diversity, species detection, and relative abundance estimates by five popular metagenomic taxonomic classification programs using in silico-generated data sets with and without aDNA damage. Damage patterns had minimal impact on the taxonomic profiles produced by each program, while false-positive rates and biases were intrinsic to each program. Therefore, the most appropriate classification program is one that minimizes the biases related to the questions being addressed.

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