FunGeneClusterS: Predicting fungal gene clusters from genome and transcriptome data.
FunGeneClusterS: Predicting fungal gene clusters from genome and transcriptome data.
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DOI：
10.1016/j.synbio.2016.01.002
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发表时间：
2016-06
影响因子：
4.8
通讯作者：
Andersen MR
中科院分区：
文献类型：
--
作者：
Vesth TC;Brandl J;Andersen MR
真菌的次级代谢产物由于其多产的生物活性以及真菌次级代谢产物的生物合成通常发生于共调控和共定位的基因簇的事实而受到越来越多的关注。这使得基因簇对合成生物学和工业生物技术应用具有吸引力。我们之前已经发表了一种从基因组和转录组数据准确预测聚类的方法，这也可能表明交叉化学，然而，这种方法在可以调整的参数数量以及用户友好性方面都受到限制。此外，对转录组数据的敏感性需要人工管理预测。在目前的工作中，我们的目标是改善这些功能。FunGeneticterS是我们以前的方法的改进实现，具有离线和在线使用的图形用户界面。新方法增加了调整正在寻找的基因簇的大小的选项，以及算法对簇中似乎不与簇的其余部分共同调节的基因具有灵活性的选项。我们已经基准的方法使用的数据，从研究良好的构巢曲霉，发现该方法是一个改进前一个。特别是，它可以更准确地预测具有超过10个基因的簇，并允许鉴定共调节基因簇，而不管基因的功能如何。它还大大减少了对预测结果的手动管理的需要。我们还将该方法应用于尼日尔的转录组数据。使用所确定的最佳参数集，我们能够确定76个先前预测的次级代谢产物脱氢酶/合成酶中的31个的聚类。此外，我们确定了其他推定的次级代谢物基因簇。我们总共预测了432个共转录基因簇。尼日尔（跨越1.323个基因，占基因组的12%）。其中一些具有与初级代谢相关的功能，例如，我们已经确定了生物素生物合成的簇，以及芳香族化合物降解的簇。这些数据表明，真菌基因组中比先前预期的更大的部分作为基因簇运作。这包括初级和次级代谢以及其他细胞维持功能。我们已经在图形化实现中开发了FunGeneticker S，并使该方法能够调整到不同的数据集和目标集群。该方法是通用的，因为它可以预测不限于次级代谢的共调节簇。我们对数据的分析不仅表明了该方法的有效性，而且还强烈表明真菌初级代谢和细胞功能的大部分都是共同调节和共同定位的。
Secondary metabolites of fungi are receiving an increasing amount of interest due to their prolific bioactivities and the fact that fungal biosynthesis of secondary metabolites often occurs from co-regulated and co-located gene clusters. This makes the gene clusters attractive for synthetic biology and industrial biotechnology applications. We have previously published a method for accurate prediction of clusters from genome and transcriptome data, which could also suggest cross-chemistry, however, this method was limited both in the number of parameters which could be adjusted as well as in user-friendliness. Furthermore, sensitivity to the transcriptome data required manual curation of the predictions. In the present work, we have aimed at improving these features. FunGeneClusterS is an improved implementation of our previous method with a graphical user interface for off- and on-line use. The new method adds options to adjust the size of the gene cluster(s) being sought as well as an option for the algorithm to be flexible with genes in the cluster which may not seem to be co-regulated with the remainder of the cluster. We have benchmarked the method using data from the well-studied Aspergillus nidulans and found that the method is an improvement over the previous one. In particular, it makes it possible to predict clusters with more than 10 genes more accurately, and allows identification of co-regulated gene clusters irrespective of the function of the genes. It also greatly reduces the need for manual curation of the prediction results. We furthermore applied the method to transcriptome data from A. niger. Using the identified best set of parameters, we were able to identify clusters for 31 out of 76 previously predicted secondary metabolite synthases/synthetases. Furthermore, we identified additional putative secondary metabolite gene clusters. In total, we predicted 432 co-transcribed gene clusters in A. niger (spanning 1.323 genes, 12% of the genome). Some of these had functions related to primary metabolism, e.g. we have identified a cluster for biosynthesis of biotin, as well as several for degradation of aromatic compounds. The data identifies that suggests that larger parts of the fungal genome than previously anticipated operates as gene clusters. This includes both primary and secondary metabolism as well as other cellular maintenance functions. We have developed FunGeneClusterS in a graphical implementation and made the method capable of adjustments to different datasets and target clusters. The method is versatile in that it can predict co-regulated clusters not limited to secondary metabolism. Our analysis of data has shown not only the validity of the method, but also strongly suggests that large parts of fungal primary metabolism and cellular functions are both co-regulated and co-located.
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DOI：
10.1073/pnas.1205532110
发表时间：
2013-01-02
影响因子：
11.1
作者：
Andersen, Mikael R.;Nielsen, Jakob B.;Mortensen, Uffe H.
通讯作者：
Mortensen, Uffe H.
影响因子：
46.9
作者：
Pel, Herman J.;de Winde, Johannes H.;Stam, Hein
通讯作者：
Stam, Hein
影响因子：
7
作者：
Andersen, Mikael R.;Salazar, Margarita P.;Baker, Scott E.
通讯作者：
Baker, Scott E.
影响因子：
7
作者：
Lee, JM;Sonnhammer, ELL
通讯作者：
Sonnhammer, ELL
影响因子：
--
作者：
Holm, Dorte K.;Petersen, Lene M.;Mortensen, Uffe H.
通讯作者：
Mortensen, Uffe H.