All-Food-Seq (AFS) : a quantifiable screen for species in biological samples by deep DNA sequencing

Publication type:
Journal article
Metadata:
Autoren
  • Fabian Ripp
  • Christopher Felix Krombholz
  • Yongchao Liu
  • Mathias Weber
  • Anne Schäfer
  • Bertil Schmidt
  • Rene Köppel
  • Thomas Hankeln
Sammlungen
  • metadata
ISSN
1471-2164
Zeitschrift
BMC genomics
Schlüsselwörter
  • 570 Biowissenschaften
  • 570 Life sciences
Sprache
eng
Paginierung
Art. 639
Datum der Veröffentlichung
2014
Herausgeber
BioMed central
Herausgeber URL
http://dx.doi.org/10.1186/1471-2164-15-639
Datum der Datenerfassung
2020
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Zugang
Public
Titel
All-Food-Seq (AFS) : a quantifiable screen for species in biological samples by deep DNA sequencing
Ausgabe der Zeitschrift
15

Data source: METADATA.UB

Other metadata sources:
Autoren
  • Fabian Ripp
  • Christopher Felix Krombholz
  • Yongchao Liu
  • Mathias Weber
  • Anne Schaefer
  • Bertil Schmidt
  • Rene Koeppel
  • Thomas Hankeln
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000340639000001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1186/1471-2164-15-639
Externe Identifier
  • Clarivate Analytics Document Solution ID: AN5NT
  • PubMed Identifier: 25081296
ISSN
1471-2164
Zeitschrift
BMC GENOMICS
Schlüsselwörter
  • Illumina
  • Next-generation sequencing
  • Real-time PCR
  • Species identification
  • Metagenomics
Artikelnummer
ARTN 639
Datum der Veröffentlichung
2014
Status
Published
Titel
All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing
Sub types
  • Article
Ausgabe der Zeitschrift
15

Data source: Web of Science (Lite)

Abstract
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>DNA-based methods like PCR efficiently identify and quantify the taxon composition of complex biological materials, but are limited to detecting species targeted by the choice of the primer assay. We show here how untargeted deep sequencing of foodstuff total genomic DNA, followed by bioinformatic analysis of sequence reads, facilitates highly accurate identification of species from all kingdoms of life, at the same time enabling quantitative measurement of the main ingredients and detection of unanticipated food components.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Sequence data simulation and real-case Illumina sequencing of DNA from reference sausages composed of mammalian (pig, cow, horse, sheep) and avian (chicken, turkey) species are able to quantify material correctly at the 1% discrimination level via a read counting approach. An additional metagenomic step facilitates identification of traces from animal, plant and microbial DNA including unexpected species, which is prospectively important for the detection of allergens and pathogens.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>Our data suggest that deep sequencing of total genomic DNA from samples of heterogeneous taxon composition promises to be a valuable screening tool for reference species identification and quantification in biosurveillance applications like food testing, potentially alleviating some of the problems in taxon representation and quantification associated with targeted PCR-based approaches.</jats:p> </jats:sec>
Autoren
  • Fabian Ripp
  • Christopher Felix Krombholz
  • Yongchao Liu
  • Mathias Weber
  • Anne Schäfer
  • Bertil Schmidt
  • Rene Köppel
  • Thomas Hankeln
DOI
10.1186/1471-2164-15-639
eISSN
1471-2164
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC Genomics
Sprache
en
Artikelnummer
639
Online publication date
2014
Datum der Veröffentlichung
2014
Status
Published
Herausgeber
Springer Science and Business Media LLC
Herausgeber URL
http://dx.doi.org/10.1186/1471-2164-15-639
Datum der Datenerfassung
2021
Titel
All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing
Ausgabe der Zeitschrift
15

Data source: Crossref

Abstract
<h4>Background</h4>DNA-based methods like PCR efficiently identify and quantify the taxon composition of complex biological materials, but are limited to detecting species targeted by the choice of the primer assay. We show here how untargeted deep sequencing of foodstuff total genomic DNA, followed by bioinformatic analysis of sequence reads, facilitates highly accurate identification of species from all kingdoms of life, at the same time enabling quantitative measurement of the main ingredients and detection of unanticipated food components.<h4>Results</h4>Sequence data simulation and real-case Illumina sequencing of DNA from reference sausages composed of mammalian (pig, cow, horse, sheep) and avian (chicken, turkey) species are able to quantify material correctly at the 1% discrimination level via a read counting approach. An additional metagenomic step facilitates identification of traces from animal, plant and microbial DNA including unexpected species, which is prospectively important for the detection of allergens and pathogens.<h4>Conclusions</h4>Our data suggest that deep sequencing of total genomic DNA from samples of heterogeneous taxon composition promises to be a valuable screening tool for reference species identification and quantification in biosurveillance applications like food testing, potentially alleviating some of the problems in taxon representation and quantification associated with targeted PCR-based approaches.
Autoren
  • Fabian Ripp
  • Christopher Felix Krombholz
  • Yongchao Liu
  • Mathias Weber
  • Anne Schäfer
  • Bertil Schmidt
  • Rene Köppel
  • Thomas Hankeln
DOI
10.1186/1471-2164-15-639
eISSN
1471-2164
Externe Identifier
  • PubMed Identifier: 25081296
  • PubMed Central ID: PMC4131036
Open access
true
ISSN
1471-2164
Zeitschrift
BMC genomics
Schlüsselwörter
  • Animals
  • Humans
  • Calibration
  • Chromosome Mapping
  • Sequence Analysis, DNA
  • Species Specificity
  • Meat
  • Databases, Genetic
  • Biosurveillance
  • Metagenomics
  • High-Throughput Nucleotide Sequencing
  • Food Quality
Sprache
eng
Medium
Electronic
Online publication date
2014
Open access status
Open Access
Paginierung
639
Datum der Veröffentlichung
2014
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2014
Titel
All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing.
Sub types
  • Research Support, Non-U.S. Gov't
  • research-article
  • Journal Article
Ausgabe der Zeitschrift
15

Files

https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-15-639 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25081296/pdf/?tool=EBI https://europepmc.org/articles/PMC4131036?pdf=render

Data source: Europe PubMed Central

Abstract
BACKGROUND: DNA-based methods like PCR efficiently identify and quantify the taxon composition of complex biological materials, but are limited to detecting species targeted by the choice of the primer assay. We show here how untargeted deep sequencing of foodstuff total genomic DNA, followed by bioinformatic analysis of sequence reads, facilitates highly accurate identification of species from all kingdoms of life, at the same time enabling quantitative measurement of the main ingredients and detection of unanticipated food components. RESULTS: Sequence data simulation and real-case Illumina sequencing of DNA from reference sausages composed of mammalian (pig, cow, horse, sheep) and avian (chicken, turkey) species are able to quantify material correctly at the 1% discrimination level via a read counting approach. An additional metagenomic step facilitates identification of traces from animal, plant and microbial DNA including unexpected species, which is prospectively important for the detection of allergens and pathogens. CONCLUSIONS: Our data suggest that deep sequencing of total genomic DNA from samples of heterogeneous taxon composition promises to be a valuable screening tool for reference species identification and quantification in biosurveillance applications like food testing, potentially alleviating some of the problems in taxon representation and quantification associated with targeted PCR-based approaches.
Date of acceptance
2014
Autoren
  • Fabian Ripp
  • Christopher Felix Krombholz
  • Yongchao Liu
  • Mathias Weber
  • Anne Schäfer
  • Bertil Schmidt
  • Rene Köppel
  • Thomas Hankeln
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/25081296
DOI
10.1186/1471-2164-15-639
eISSN
1471-2164
Externe Identifier
  • PubMed Central ID: PMC4131036
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC Genomics
Schlüsselwörter
  • Animals
  • Biosurveillance
  • Calibration
  • Chromosome Mapping
  • Databases, Genetic
  • Food Quality
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Meat
  • Metagenomics
  • Sequence Analysis, DNA
  • Species Specificity
Sprache
eng
Country
England
Paginierung
639
PII
1471-2164-15-639
Datum der Veröffentlichung
2014
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2015
Titel
All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing.
Sub types
  • Journal Article
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
15

Data source: PubMed

Author's licence
CC-BY
Autoren
  • Fabian Ripp
  • Christopher Felix Krombholz
  • Yongchao Liu
  • Mathias Weber
  • Anne Schäfer
  • Bertil Schmidt
  • Rene Köppel
  • Thomas Hankeln
Hosting institution
Universitätsbibliothek Mainz
Sammlungen
  • DFG-OA-Publizieren (2012 - 2017)
Resource version
Published version
DOI
10.1186/1471-2164-15-639
Funding acknowledgements
  • DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin
File(s) embargoed
false
Open access
true
ISSN
1471-2164
Zeitschrift
BMC genomics
Schlüsselwörter
  • 570 Biowissenschaften
  • 570 Life sciences
Sprache
eng
Open access status
Open Access
Paginierung
Art. 639
Datum der Veröffentlichung
2014
Public URL
https://openscience.ub.uni-mainz.de/handle/20.500.12030/7984
Herausgeber
BioMed central
Herausgeber URL
http://dx.doi.org/10.1186/1471-2164-15-639
Datum der Datenerfassung
2022
Datum, an dem der Datensatz öffentlich gemacht wurde
2022
Zugang
Public
Titel
All-Food-Seq (AFS) : a quantifiable screen for species in biological samples by deep DNA sequencing
Ausgabe der Zeitschrift
15

Files

allfoodseq_afs___a_quantifiab-20220925144236499.pdf

Data source: OPENSCIENCE.UB

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