LightCUD: a program for diagnosing IBD based on human gut microbiome data.
LightCUD: a program for diagnosing IBD based on human gut microbiome data.
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LightCUD：基于人类肠道微生物组数据诊断 IBD 的程序
DOI：
10.1186/s13040-021-00241-2
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发表时间：
2021-01-19
期刊：
影响因子：
4.5
通讯作者：
Zhu H
中科院分区：
文献类型：
--
作者：
Xu C;Zhou M;Xie Z;Li M;Zhu X;Zhu H
背景炎症性肠病（IBD）的诊断和分型具有重要的临床意义。IBD与肠道微生物群的显著变化有关。下一代测序（NGS）技术的进步和医院生物信息学分析能力的提高促使我们开发一种基于肠道微生物组的诊断方法。ResultsUsing a set of whole-genome sequencing（WGS）data from 349 human gut microbiota samples with two types of IBD and healthy controls，我们组装并对齐WGS短读段，以获得菌株和属的特征图谱。分别将属和菌株谱用于基于16 S和基于WGS的诊断模块的构建。我们设计了一个新的特征选择过程来选择这些特定情况下的功能。有了这些特征，我们使用不同的机器学习算法建立了判别模型。机器学习算法LightGBM在本研究中优于其他算法，因此被选为核心算法。特别地，我们分别为基于WGS的健康与IBD模块和溃疡性结肠炎与克罗恩病模块确定了两组小的生物标志物（菌株），这有助于在预训练期间优化模型性能。我们发布了LightCUD作为使用LightGBM构建的IBD诊断程序。高性能已通过五重交叉验证和使用独立的测试数据集进行了验证。LightCUD是用Python实现的，并免费打包用于自定义数据库的安装。以肠道微生物组样本的WGS数据或16 S rRNA测序数据为输入，LightCUD可以高精度地区分IBD和健康对照，并进一步识别IBD的特定类型。可执行程序LightCUD已开源发布，并在网页上提供了说明 http://cqb.pku.edu.cn/ZhuLab/LightCUD/ .所确定的菌株生物标志物可用于研究疾病发展的关键因素，并建议治疗有关的变化在肠道微生物community.ConclusionsAs的第一个发布的人类肠道微生物组为基础的IBD诊断工具，LightCUD表现出高性能的WGS和16 S测序数据。从IBD患者中识别健康对照或区分IBD特定类型的菌株预期在临床上重要，可用作生物标志物。
BackgroundThe diagnosis of inflammatory bowel disease (IBD) and discrimination between the types of IBD are clinically important. IBD is associated with marked changes in the intestinal microbiota. Advances in next-generation sequencing (NGS) technology and the improved hospital bioinformatics analysis ability motivated us to develop a diagnostic method based on the gut microbiome.ResultsUsing a set of whole-genome sequencing (WGS) data from 349 human gut microbiota samples with two types of IBD and healthy controls, we assembled and aligned WGS short reads to obtain feature profiles of strains and genera. The genus and strain profiles were used for the 16S-based and WGS-based diagnostic modules construction respectively. We designed a novel feature selection procedure to select those case-specific features. With these features, we built discrimination models using different machine learning algorithms. The machine learning algorithm LightGBM outperformed other algorithms in this study and thus was chosen as the core algorithm. Specially, we identified two small sets of biomarkers (strains) separately for the WGS-based health vs IBD module and ulcerative colitis vs Crohn’s disease module, which contributed to the optimization of model performance during pre-training.We released LightCUD as an IBD diagnostic program built with LightGBM. The high performance has been validated through five-fold cross-validation and using an independent test data set. LightCUD was implemented in Python and packaged free for installation with customized databases. With WGS data or 16S rRNA sequencing data of gut microbiome samples as the input, LightCUD can discriminate IBD from healthy controls with high accuracy and further identify the specific type of IBD. The executable program LightCUD was released in open source with instructions at the webpage http://cqb.pku.edu.cn/ZhuLab/LightCUD/ . The identified strain biomarkers could be used to study the critical factors for disease development and recommend treatments regarding changes in the gut microbial community.ConclusionsAs the first released human gut microbiome-based IBD diagnostic tool, LightCUD demonstrates a high-performance for both WGS and 16S sequencing data. The strains that either identify healthy controls from IBD patients or distinguish the specific type of IBD are expected to be clinically important to serve as biomarkers.
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影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
14.9
作者：
Cole JR;Wang Q;Cardenas E;Fish J;Chai B;Farris RJ;Kulam-Syed-Mohideen AS;McGarrell DM;Marsh T;Garrity GM;Tiedje JM
通讯作者：
Tiedje JM
影响因子：
30.3
作者：
Gevers D;Kugathasan S;Denson LA;Vázquez-Baeza Y;Van Treuren W;Ren B;Schwager E;Knights D;Song SJ;Yassour M;Morgan XC;Kostic AD;Luo C;González A;McDonald D;Haberman Y;Walters T;Baker S;Rosh J;Stephens M;Heyman M;Markowitz J;Baldassano R;Griffiths A;Sylvester F;Mack D;Kim S;Crandall W;Hyams J;Huttenhower C;Knight R;Xavier RJ
通讯作者：
Xavier RJ
影响因子：
30.3
作者：
Lewis JD;Chen EZ;Baldassano RN;Otley AR;Griffiths AM;Lee D;Bittinger K;Bailey A;Friedman ES;Hoffmann C;Albenberg L;Sinha R;Compher C;Gilroy E;Nessel L;Grant A;Chehoud C;Li H;Wu GD;Bushman FD
通讯作者：
Bushman FD
DOI：
10.1093/jee/39.2.269
发表时间：
1945-01-01
期刊：
BIOMETRICS BULLETIN
影响因子：
--
作者：
WILCOXON, F
通讯作者：
WILCOXON, F