Banf Michael, Zhao Kangmei, Rhee Seung Y
Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA.
EducatedGuess.ai, Siegen, Germany.
Bioinformatics. 2019 Sep 1;35(17):3178-3180. doi: 10.1093/bioinformatics/btz021.
Plants and microbes produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have yet to be elucidated. Some biosynthetic pathways are encoded by enzymes collocated in the chromosome. To facilitate a more comprehensive condition and tissue-specific expression analysis of metabolic gene clusters, we developed METACLUSTER, a probabilistic framework for characterizing metabolic gene clusters using context-specific gene expression information.
METACLUSTER is freely available at https://github.com/mbanf/METACLUSTER.
Supplementary data are available at Bioinformatics online.
植物和微生物会产生大量化合物以应对其所处环境，但其中大多数化合物的生物合成途径尚未阐明。一些生物合成途径由位于染色体上的酶编码。为便于对代谢基因簇进行更全面的条件和组织特异性表达分析，我们开发了METACLUSTER，这是一个使用上下文特异性基因表达信息来表征代谢基因簇的概率框架。
METACLUSTER可在https://github.com/mbanf/METACLUSTER上免费获取。
补充数据可在《生物信息学》在线版获取。
