Liu Yongchao, Ripp Fabian, Koeppel Rene, Schmidt Hanno, Hellmann Sören Lukas, Weber Mathias, Krombholz Christopher Felix, Schmidt Bertil, Hankeln Thomas
Institute of Computer Science, Johannes Gutenberg University Mainz, 55099 Mainz, Germany.
Georgia Institute of Technology, School of Computational Science and Engineering, Atlanta, GA 30332, USA.
Bioinformatics. 2017 May 1;33(9):1396-1398. doi: 10.1093/bioinformatics/btw822.
DNA-based methods to detect and quantify taxon composition in biological materials are often based on species-specific polymerase chain reaction, limited to detecting species targeted by the assay. Next-generation sequencing overcomes this drawback by untargeted shotgun sequencing of whole metagenomes at affordable cost. Here we present AFS, a software pipeline for quantification of species composition in food. AFS uses metagenomic shotgun sequencing and sequence read counting to infer species proportions. Using Illumina data from a reference sausage comprising four species, we reveal that AFS is independent of the sequencing assay and library preparation protocol. Cost-saving short (50-bp) single-end reads and Nextera ® library preparation yield reliable results.
Datasets, binaries and usage instructions are available under http://all-food-seq.sourceforge.net. Raw data is available at NCBI's SRA with accession number PRJNA271645.
Supplementary data are available at Bioinformatics online.
用于检测和定量生物材料中分类单元组成的基于DNA的方法通常基于物种特异性聚合酶链反应，仅限于检测该分析所针对的物种。新一代测序通过以可承受的成本对整个宏基因组进行非靶向鸟枪法测序克服了这一缺点。在这里，我们展示了AFS，一种用于定量食品中物种组成的软件管道。AFS使用宏基因组鸟枪法测序和序列读数计数来推断物种比例。使用来自包含四个物种的参考香肠的Illumina数据，我们发现AFS与测序分析和文库制备方案无关。节省成本的短（50碱基对）单端读数和Nextera®文库制备产生可靠的结果。
数据集、二进制文件和使用说明可在http://all-food-seq.sourceforge.net获得。原始数据可在NCBI的SRA上获得，登录号为PRJNA271645。
补充数据可在《生物信息学》在线获取。
