Haubold Bernhard, Klötzl Fabian, Hellberg Lars, Thompson Daniel, Cavalar Markus
Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.
Molecular Infection Diagnostics, Euroimmun Medizinische Labordiagnostika, Lübeck, Germany.
Bioinformatics. 2021 Aug 9;37(15):2081-2087. doi: 10.1093/bioinformatics/btab059.
Unique marker sequences are highly sought after in molecular diagnostics. Nevertheless, there are only few programs available to search for marker sequences, compared to the many programs for similarity search. We therefore wrote the program Fur for Finding Unique genomic Regions.
Fur takes as input a sample of target sequences and a sample of closely related neighbors. It returns the regions present in all targets and absent from all neighbors. The recently published program genmap can also be used for this purpose and we compared it to fur. When analyzing a sample of 33 genomes representing the major phylogroups of E.coli, fur was 40 times faster than genmap but used three times more memory. On the other hand, genmap yielded three times more markers, but they were less accurate when tested in silico on a sample of 237 E.coli genomes. We also designed phylogroup-specific PCR primers based on the markers proposed by genmap and fur, and tested them by analyzing their virtual amplicons in GenBank. Finally, we used fur to design primers specific to a Lactobacillus species, and found excellent sensitivity and specificity in vitro.
Fur sources and documentation are available from https://github.com/evolbioinf/fur. The compiled software is posted as a docker container at https://hub.docker.com/r/haubold/fox.
Supplementary data are available at Bioinformatics online.
在分子诊断中，独特的标记序列备受追捧。然而，与众多用于相似性搜索的程序相比，可用于搜索标记序列的程序却很少。因此，我们编写了用于寻找独特基因组区域的程序Fur。
Fur将目标序列样本和密切相关的近缘序列样本作为输入。它返回所有目标序列中存在而所有近缘序列中不存在的区域。最近发布的程序genmap也可用于此目的，我们将它与Fur进行了比较。在分析代表大肠杆菌主要系统发育群的33个基因组样本时，Fur的速度比genmap快40倍，但内存使用量多出两倍。另一方面，genmap产生的标记多两倍，但在对237个大肠杆菌基因组样本进行虚拟测试时，其准确性较低。我们还根据genmap和Fur提出的标记设计了系统发育群特异性PCR引物，并通过在GenBank中分析其虚拟扩增子对它们进行了测试。最后，我们使用Fur设计了针对一种乳酸杆菌的引物，并在体外发现了出色的灵敏度和特异性。
Fur的源代码和文档可从https://github.com/evolbioinf/fur获取。编译后的软件作为Docker容器发布在https://hub.docker.com/r/haubold/fox。
补充数据可在《生物信息学》在线获取。
