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Motif-independent de novo detection of secondary metabolite gene clusters—toward identification from filamentous fungi

机译:不依赖基序的 de novo 检测次生代谢产物基因簇—旨在从丝状真菌中鉴定

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Secondary metabolites are produced mostly by clustered genes that are essential to their biosynthesis. The transcriptional expression of these genes is often cooperatively regulated by a transcription factor located inside or close to a cluster. Most of the secondary metabolism biosynthesis (SMB) gene clusters identified to date contain so-called core genes with distinctive sequence features, such as polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS). Recent efforts in sequencing fungal genomes have revealed far more SMB gene clusters than expected based on the number of core genes in the genomes. Several bioinformatics tools have been developed to survey SMB gene clusters using the sequence motif information of the core genes, including SMURF and antiSMASH. More recently, accompanied by the development of sequencing techniques allowing to obtain large-scale genomic and transcriptomic data, motif-independent prediction methods of SMB gene clusters, including MIDDAS-M, have been developed. Most these methods detect the clusters in which the genes are cooperatively regulated at transcriptional levels, thus allowing the identification of novel SMB gene clusters regardless of the presence of the core genes. Another type of the method, MIPS-CG, uses the characteristics of SMB genes, which are highly enriched in non-syntenic blocks (NSBs), enabling the prediction even without transcriptome data although the results have not been evaluated in detail. Considering that large portion of SMB gene clusters might be sufficiently expressed only in limited uncommon conditions, it seems that prediction of SMB gene clusters by bioinformatics and successive experimental validation is an only way to efficiently uncover hidden SMB gene clusters. Here, we describe and discuss possible novel approaches for the determination of SMB gene clusters that have not been identified using conventional methods.
机译:次生代谢产物主要由对其生物合成必不可少的簇状基因产生。这些基因的转录表达通常受到位于簇内部或簇附近的转录因子的协同调控。迄今为止,大多数次级代谢生物合成(SMB)基因簇都包含具有独特序列特征的所谓核心基因,例如聚酮化合物合酶(PKS)和非核糖体肽合成酶(NRPS)。最近对真菌基因组进行测序的努力表明,基于基因组核心基因的数量,SMB基因簇比预期的要多得多。已经开发了几种生物信息学工具来使用核心基因的序列基序信息来调查SMB基因簇,包括SMURF和antiSMASH。最近,伴随着测序技术的发展,该技术允许获得大规模的基因组和转录组数据,已经开发了包括MIDDAS-M在内的SMB基因簇的独立于基序的预测方法。大多数这些方法检测在转录水平上协同调节基因的簇,因此无论核心基因的存在如何都可以鉴定新的SMB基因簇。另一种类型的方法MIPS-CG利用SMB基因的特征,该特征高度富含非同义区(NSB),即使没有转录组数据也可以进行预测,尽管结果尚未得到详细评估。考虑到大部分SMB基因簇只有在有限的罕见条件下才能充分表达,看来通过生物信息学预测SMB基因簇并进行连续的实验验证是有效发现隐藏的SMB基因簇的唯一方法。在这里,我们描述和讨论可能的新颖方法,用于确定尚未使用常规方法鉴定的SMB基因簇。

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