Boolean implication analysis unveils candidate universal relationships in microbiome data.
Boolean implication analysis unveils candidate universal relationships in microbiome data.
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布尔蕴涵分析揭示了微生物组数据中的候选通用关系。
DOI：
10.1186/s12859-020-03941-4
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发表时间：
2021-02-05
影响因子：
3
通讯作者：
Sahoo D
中科院分区：
文献类型：
--
作者：
Vo D;Singh SC;Safa S;Sahoo D
微生物组由细菌、病毒和其他微生物组成，在生物体和环境中发挥许多不同的功能。过去对微生物组的分析集中在使用相关性来确定微生物和疾病之间的线性关系。由于微生物对之间的非线性而导致的弱相关性可能会导致研究人员忽视数据的关键组成部分。随着可用微生物组的丰富，我们需要一种方法来全面研究微生物组以及它们之间的关系。我们收集了来自人类、环境和动物样本的公开数据集，以确定一对微生物之间的对称和非对称布尔蕴涵关系。然后，我们发现了潜在不变的关系，这意味着它们将在任何微生物群落中保持不变。换句话说，如果我们确定两种微生物之间存在关系，我们希望这种关系在几乎所有情况下都成立。我们发现，在我们研究的几乎所有数据集中，大约有33万对微生物普遍表现出相同的关系，因此使它们成为不变量的良好候选者。我们的研究结果也证实了已知的生物学特性，在疾病诊断方面似乎很有希望。由于这些关系可能是普遍的，我们希望它们在临床环境中以及一般人群中保持不变。如果这些强不变量存在于疾病环境中，则可以提供对临床相关疾病的预后、预测或治疗特性的洞察。例如，我们的研究结果表明，患有或未患有IBD，湿疹和银屑病的患者之间的微生物分布存在差异。这些新的分析可能会提高疾病诊断和药物开发的准确性和效率。
Microbiomes consist of bacteria, viruses, and other microorganisms, and are responsible for many different functions in both organisms and the environment. Past analyses of microbiomes focused on using correlation to determine linear relationships between microbes and diseases. Weak correlations due to nonlinearity between microbe pairs may cause researchers to overlook critical components of the data. With the abundance of available microbiome, we need a method that comprehensively studies microbiomes and how they are related to each other. We collected publicly available datasets from human, environment, and animal samples to determine both symmetric and asymmetric Boolean implication relationships between a pair of microbes. We then found relationships that are potentially invariants, meaning they will hold in any microbe community. In other words, if we determine there is a relationship between two microbes, we expect the relationship to hold in almost all contexts. We discovered that around 330,000 pairs of microbes universally exhibit the same relationship in almost all the datasets we studied, thus making them good candidates for invariants. Our results also confirm known biological properties and seem promising in terms of disease diagnosis. Since the relationships are likely universal, we expect them to hold in clinical settings, as well as general populations. If these strong invariants are present in disease settings, it may provide insight into prognostic, predictive, or therapeutic properties of clinically relevant diseases. For example, our results indicate that there is a difference in the microbe distributions between patients who have or do not have IBD, eczema and psoriasis. These new analyses may improve disease diagnosis and drug development in terms of accuracy and efficiency.
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影响因子：
1.3
作者：
Lazarova S;Peneva V;Kumari S
通讯作者：
Kumari S
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影响因子：
14.9
作者：
Sahoo D;Dill DL;Tibshirani R;Plevritis SK
通讯作者：
Plevritis SK
影响因子：
46.9
作者：
通讯作者：
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影响因子：
4.3
作者：
Steinway SN;Biggs MB;Loughran TP Jr;Papin JA;Albert R
通讯作者：
Albert R
影响因子：
64.8
作者：
Chen JY;Miyanishi M;Wang SK;Yamazaki S;Sinha R;Kao KS;Seita J;Sahoo D;Nakauchi H;Weissman IL
通讯作者：
Weissman IL