Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons.
Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons.
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DOI：
10.1186/s12864-016-2889-6
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发表时间：
2016-09-26
期刊：
影响因子：
4.4
通讯作者：
Corbeil J
中科院分区：
文献类型：
--
作者：
Drouin A;Giguère S;Déraspe M;Marchand M;Tyers M;Loo VG;Bourgault AM;Laviolette F;Corbeil J
基因组生物标记物的鉴定是改进诊断测试和治疗的关键一步。我们为这项任务提出了一种无参考的方法，它依赖于基因组的k-mer表示和产生可理解模型的机器学习算法。该方法在计算上是可伸缩的，非常适合于全基因组测序研究。通过建立预测艰难梭菌、结核分枝杆菌、铜绿假单胞菌和肺炎链球菌对17种抗生素耐药性的模型，验证了该方法的有效性。所获得的模型是准确的，忠于抗生素靶向的生物途径，并提供了对耐药性获得过程的洞察。此外，对该方法的理论分析表明，所获得的模型的准确性得到了严格的统计保证，支持其与基因组生物标记物发现的相关性。我们的方法允许生成精确的和可解释的表型预测模型，这些模型依赖于一小部分基因组变异。该方法不仅限于预测细菌中的抗生素耐药性，而且适用于各种生物体和表型。KOVER是我们方法的有效实现，它是开源的，应该指导生物学努力理解过多的表型(http://github.com/aldro61/kover/).本文的在线版本(doi：10.1186/s12864-0162889-6)包含补充材料，授权用户可以使用。
The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies. The method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery. Our method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes (http://github.com/aldro61/kover/). The online version of this article (doi:10.1186/s12864-016-2889-6) contains supplementary material, which is available to authorized users.
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影响因子：
16.6
作者：
Bradley P;Gordon NC;Walker TM;Dunn L;Heys S;Huang B;Earle S;Pankhurst LJ;Anson L;de Cesare M;Piazza P;Votintseva AA;Golubchik T;Wilson DJ;Wyllie DH;Diel R;Niemann S;Feuerriegel S;Kohl TA;Ismail N;Omar SV;Smith EG;Buck D;McVean G;Walker AS;Peto TE;Crook DW;Iqbal Z
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Shortridge, D
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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