RT Journal Article
T1 A big data approach to metagenomics for all-food-sequencing
A1 Kobus, Robin
A1 Abuín Mosquera, José Manuel
A1 Müller, André
A1 Hellmann, Sören Lukas
A1 Pichel Campos, Juan Carlos
A1 Fernández Pena, Anselmo Tomás
A1 Hildebrandt, Andreas
A1 Hankeln, Thomas
A1 Schmidt, Bertil
K1 Next-generation sequencing
K1 Metagenomics
K1 Species identification
K1 Eukaryotic genomes
K1 Locality sensitive hashing
K1 Bigdata
AB BackgroundAll-Food-Sequencing (AFS) is an untargeted metagenomic sequencing method that allows for the detection and quantification of food ingredients including animals, plants, and microbiota. While this approach avoids some of the shortcomings of targeted PCR-based methods, it requires the comparison of sequence reads to large collections of reference genomes. The steadily increasing amount of available reference genomes establishes the need for efficient big data approaches.ResultsWe introduce an alignment-free k-mer based method for detection and quantification of species composition in food and other complex biological matters. It is orders-of-magnitude faster than our previous alignment-based AFS pipeline. In comparison to the established tools CLARK, Kraken2, and Kraken2+Bracken it is superior in terms of false-positive rate and quantification accuracy. Furthermore, the usage of an efficient database partitioning scheme allows for the processing of massive collections of reference genomes with reduced memory requirements on a workstation (AFS-MetaCache) or on a Spark-based compute cluster (MetaCacheSpark).ConclusionsWe present a fast yet accurate screening method for whole genome shotgun sequencing-based biosurveillance applications such as food testing. By relying on a big data approach it can scale efficiently towards large-scale collections of complex eukaryotic and bacterial reference genomes. AFS-MetaCache and MetaCacheSpark are suitable tools for broad-scale metagenomic screening applications. They are available at https://muellan.github.io/metacache/afs.html (C++ version for a workstation) and https://github.com/jmabuin/MetaCacheSpark (Spark version for big data clusters).
PB BMC
YR 2020
FD 2020
LK http://hdl.handle.net/10347/21839
UL http://hdl.handle.net/10347/21839
LA eng
NO Kobus, R., Abuín, J.M., Müller, A. et al. A big data approach to metagenomics for all-food-sequencing. BMC Bioinformatics 21, 102 (2020)
NO This work was partially supported by the Deutsche Forschungsgemeinschaft (DFG), Project HySim, the MINECO under award RTI2018-093336-B-C21, Xunta de Galicia under awards ED481B 2018/013 and ED431C 2018/19, the European Regional Development Fund, and by the Federal Office for Agriculture and Food
DS Minerva
RD 26 abr 2026