Department of Biology, University of Waterloo, Waterloo, Ontario, Canada.
PLoS One. 2013 Aug 6;8(8):e71484. doi: 10.1371/journal.pone.0071484. Print 2013.
Next-generation sequencing projects continue to drive a vast accumulation of metagenomic sequence data. Given the growth rate of this data, automated approaches to functional annotation are indispensable and a cornerstone heuristic of many computational protocols is the concept of guilt by association. The guilt by association paradigm has been heavily exploited by genomic context methods that offer functional predictions that are complementary to homology-based annotations, thereby offering a means to extend functional annotation. In particular, operon methods that exploit co-directional intergenic distances can provide homology-free functional annotation through the transfer of functions among co-operonic genes, under the assumption that guilt by association is indeed applicable. Although guilt by association is a well-accepted annotative device, its applicability to metagenomic functional annotation has not been definitively demonstrated. Here a large-scale assessment of metagenomic guilt by association is undertaken where functional associations are predicted on the basis of co-directional intergenic distances. Specifically, functional annotations are compared within pairs of adjacent co-directional genes, as well as operons of various lengths (i.e. number of member genes), in order to reveal new information about annotative cohesion versus operon length. The results suggests that co-directional gene pairs offer reduced confidence for metagenomic guilt by association due to difficulty in resolving the existence of functional associations when intergenic distance is the sole predictor of pairwise gene interactions. However, metagenomic operons, particularly those with substantial lengths, appear to be capable of providing a superior basis for metagenomic guilt by association due to increased annotative stability. The need for improved recognition of metagenomic operons is discussed, as well as the limitations of the present work.
下一代测序项目继续推动大量元基因组序列数据的积累。鉴于这些数据的增长率，自动化功能注释方法是必不可少的，许多计算协议的基石启发式方法是关联罪责的概念。关联罪责范式已被基因组上下文方法大量利用，这些方法提供了与同源性注释互补的功能预测，从而提供了扩展功能注释的手段。特别是，利用共向基因间距离的操纵子方法可以通过在共操纵基因之间转移功能，提供无同源性的功能注释，前提是关联罪责确实适用。尽管关联罪责是一种被广泛接受的注释方法，但它在元基因组功能注释中的适用性尚未得到明确证明。在这里，对元基因组关联罪责进行了大规模评估，根据共向基因间距离预测功能关联。具体来说，在相邻共向基因对以及各种长度的操纵子（即成员基因的数量）内比较功能注释，以揭示关于注释一致性与操纵子长度的新信息。结果表明，由于在仅基因间距离是基因对相互作用的唯一预测因子时，难以确定功能关联的存在，共向基因对提供的元基因组关联罪责置信度降低。然而，元基因组操纵子，特别是那些具有较大长度的操纵子，似乎能够为元基因组关联罪责提供更好的基础，因为它们具有更高的注释稳定性。讨论了改进元基因组操纵子识别的必要性，以及本工作的局限性。
