<b>Inappropriate application of mapping algorithms results in length-dependent gene abundances in metagenomic analysis</b>

Multiple biases still exist in metagenomic analysis workflows compromising a precise quantification of microbial species and function. In this study, we demonstrated that inappropriate reads mapping could result in length-dependent gene abundances and in turn influence downstream analyses. Specifically, mapping reads directly to predicted genes using Bowtie 2 with the default global-alignment mode resulted in a gradual decrease of abundance values with the diminution of gene length. A local-alignment mode could reduce a large part, but not all, of such technical variability. Particularly, the alignment-free Salmon, which was originally designed and tested for the mapping of RNA-seq reads, were not suitable for metagenomes due to a rapid increase of the TPM (transcripts per million) measure with the diminution of gene length. Different mapping methodologies identified different sets of KEGG Orthologs with significant differences in abundance. For short-read sequencing datasets, we proposed the 'contig mapping' method, i.e., calculating gene abundances using coverage depths of contigs and gene coordinates, which minimized the technical variability.

宏基因组分析流程中仍存在多种偏差，这些偏差会损害微生物物种与功能的精确定量效果。本研究表明，不恰当的读段比对（reads mapping）会导致基因丰度呈现长度依赖性，进而对下游分析产生影响。具体而言，使用Bowtie 2的默认全局比对模式（global-alignment mode）将读段直接比对至预测基因时，会出现基因丰度值随基因长度缩短而逐渐降低的现象。局部比对模式（local-alignment mode）虽可大幅降低此类技术变异（technical variability），但无法完全消除。尤为值得注意的是，原本为RNA-seq读段比对开发并验证的无比对工具Salmon，并不适用于宏基因组分析：其TPM（转录本每百万计数，transcripts per million）数值会随基因长度缩短而快速升高。不同的比对方法会鉴定得到不同的KEGG直系同源基因（KEGG Orthologs）集合，且各集合间的丰度存在显著差异。针对短读长测序数据集（short-read sequencing datasets），本研究提出了"重叠群比对（contig mapping）"方法，即通过重叠群的覆盖深度（coverage depths）与基因坐标（gene coordinates）计算基因丰度，该方法可将技术变异降至最低。