MCRL: using a reference library to compress a metagenome into a non-redundant list of sequences, considering viruses as a case study.
MCRL: using a reference library to compress a metagenome into a non-redundant list of sequences, considering viruses as a case study.
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MCRL：使用参考库将宏基因组压缩为非冗余序列列表，将病毒视为案例研究。
DOI：
10.1093/bioinformatics/btab703
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Phillips,Rob
中科院分区：
文献类型：
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作者：
Tadmor,ArbelD;Phillips,Rob
动机宏基因组提供了一个样本中包含的总基因组多样性的一瞥。然而，目前，没有直接的方法来获得一个非冗余的列表中存在的一组参考序列的所有推定的同源物的宏基因组。ResultsTo解决这个问题，我们开发了一种新的聚类方法，称为“宏基因组聚类参考库”（MCRL），其中包含一组参考基因的参考库相对于组装的宏基因组进行聚类。根据我们提出的方法，参考基因同源的宏基因组序列，称为“签名”，迭代聚类贪婪的方式，保留在每一步的参考基因产生最低Evalues，并终止时，剩余的参考基因的签名有一个最小的重叠。该计算的结果是与重叠群的最小重叠集同源的参考基因的非冗余列表，其代表宏基因组中存在的基因家族的潜在候选者。与宏基因组聚类方法不同，不需要重叠群与聚类相关联，使MCRL能够在计算暂定基因家族时利用宏基因组中编码的更多信息。我们演示了如何MCRL可以用来提取候选病毒基因家族从口腔宏基因组和口腔病毒组，否则不能使用标准方法确定。我们评估了我们提出的方法的灵敏度，准确性和鲁棒性的病毒案例研究，并将其与现有的分析approaches.Availability和implementationhttps：github.com/a-tadmor/MCRL.Supplementary信息补充数据可在Bioinformaticsonline。
MotivationMetagenomes offer a glimpse into the total genomic diversity contained within a sample. Currently, however, there is no straightforward way to obtain a non-redundant list of all putative homologs of a set of reference sequences present in a metagenome.ResultsTo address this problem, we developed a novel clustering approach called ‘metagenomic clustering by reference library’ (MCRL), where a reference library containing a set of reference genes is clustered with respect to an assembled metagenome. According to our proposed approach, reference genes homologous to similar sets of metagenomic sequences, termed ‘signatures’, are iteratively clustered in a greedy fashion, retaining at each step the reference genes yielding the lowestEvalues, and terminating when signatures of remaining reference genes have a minimal overlap. The outcome of this computation is a non-redundant list of reference genes homologous to minimally overlapping sets of contigs, representing potential candidates for gene families present in the metagenome. Unlike metagenomic clustering methods, there is no need for contigs to overlap to be associated with a cluster, enabling MCRL to draw on more information encoded in the metagenome when computing tentative gene families. We demonstrate how MCRL can be used to extract candidate viral gene families from an oral metagenome and an oral virome that otherwise could not be determined using standard approaches. We evaluate the sensitivity, accuracy and robustness of our proposed method for the viral case study and compare it with existing analysis approaches.Availability and implementationhttps://github.com/a-tadmor/MCRL.Supplementary informationSupplementary data are available atBioinformaticsonline.