PathogenFinder--distinguishing friend from foe using bacterial whole genome sequence data.
PathogenFinder--distinguishing friend from foe using bacterial whole genome sequence data.
复制标题
DOI：
10.1371/journal.pone.0077302
复制
发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Lund O
中科院分区：
文献类型：
--
作者：
Cosentino S;Voldby Larsen M;Møller Aarestrup F;Lund O
尽管大多数细菌对其宿主无害甚至有益，但其他细菌的毒性很强，可导致严重疾病，甚至死亡。由于高通量测序的成本不断降低，现在有许多完全测序的基因组可从人类致病和无害菌株获得。这些数据可用于识别与致病性相关的基因家族，并开发工具来预测新测序菌株的致病性，以前的研究主要通过更昂贵和耗时的实验方法进行。我们描述了Pathogenesis（http：//cge.cbs.dtu.dk/services/PathogenFinder/），这是一个通过分析用户提供的输入蛋白质组、基因组或原始读数来预测细菌致病性的网络服务器。该方法依赖于蛋白质组，而不考虑它们的注释功能或已知参与致病性。该方法已被构建为与所有细菌分类组一起使用，并使用整个训练集，通过正确分类449个完全测序的细菌中的398个，在独立测试集上实现了88.6%的准确率。本文提出的方法不偏向于已知与致病性相关的基因组，因此该方法可以帮助发现新的致病因子。此外，致病性预测网络服务器可用于分离已知和未知菌株的潜在致病性特征。
Although the majority of bacteria are harmless or even beneficial to their host, others are highly virulent and can cause serious diseases, and even death. Due to the constantly decreasing cost of high-throughput sequencing there are now many completely sequenced genomes available from both human pathogenic and innocuous strains. The data can be used to identify gene families that correlate with pathogenicity and to develop tools to predict the pathogenicity of newly sequenced strains, investigations that previously were mainly done by means of more expensive and time consuming experimental approaches. We describe PathogenFinder (http://cge.cbs.dtu.dk/services/PathogenFinder/), a web-server for the prediction of bacterial pathogenicity by analysing the input proteome, genome, or raw reads provided by the user. The method relies on groups of proteins, created without regard to their annotated function or known involvement in pathogenicity. The method has been built to work with all taxonomic groups of bacteria and using the entire training-set, achieved an accuracy of 88.6% on an independent test-set, by correctly classifying 398 out of 449 completely sequenced bacteria. The approach here proposed is not biased on sets of genes known to be associated with pathogenicity, thus the approach could aid the discovery of novel pathogenicity factors. Furthermore the pathogenicity prediction web-server could be used to isolate the potential pathogenic features of both known and unknown strains.
登录
查看更多内容
影响因子：
11.8
作者：
Aarestrup FM;Brown EW;Detter C;Gerner-Smidt P;Gilmour MW;Harmsen D;Hendriksen RS;Hewson R;Heymann DL;Johansson K;Ijaz K;Keim PS;Koopmans M;Kroneman A;Lo Fo Wong D;Lund O;Palm D;Sawanpanyalert P;Sobel J;Schlundt J
通讯作者：
Schlundt J
影响因子：
56.9
作者：
FLEISCHMANN, RD;ADAMS, MD;VENTER, JC
通讯作者：
VENTER, JC
影响因子：
56.9
作者：
FRASER, CM;GOCAYNE, JD;VENTER, JC
通讯作者：
VENTER, JC
有毒剂：一种基于SVM的细菌病原体中毒素的预测方法。
影响因子：
3
作者：
Garg, Aarti;Gupta, Dinesh
通讯作者：
Gupta, Dinesh
影响因子：
56.3
作者：
den Heijer, Casper D. J.;van Bijnen, Evelien M. E.;Stobberingh, Ellen E.
通讯作者：
Stobberingh, Ellen E.