sj-docx-1-ajr-10.1177_19458924221124363 - Supplemental material for Daphnetin Mitigates Ovalbumin-Induced Allergic Rhinitis in Mice by Regulating Nrf2/HO-1 and TLR4/NF-kB Signaling

Supplemental material, sj-docx-1-ajr-10.1177_19458924221124363 for Daphnetin Mitigates Ovalbumin-Induced Allergic Rhinitis in Mice by Regulating Nrf2/HO-1 and TLR4/NF-kB Signaling by Bo Tian, Xin Ma and Rui Jiang in American Journal of Rhinology & Allergy</p

Integrating Multiple Genomic Data to Predict Disease-Causing Nonsynonymous Single Nucleotide Variants in Exome Sequencing Studies

<div><p>Exome sequencing has been widely used in detecting pathogenic nonsynonymous single nucleotide variants (SNVs) for human inherited diseases. However, traditional statistical genetics methods are ineffective in analyzing exome sequencing data, due to such facts as the large number of sequenced variants, the presence of non-negligible fraction of pathogenic rare variants or <i>de novo</i> mutations, and the limited size of affected and normal populations. Indeed, prevalent applications of exome sequencing have been appealing for an effective computational method for identifying causative nonsynonymous SNVs from a large number of sequenced variants. Here, we propose a bioinformatics approach called SPRING (<i>Snv PRioritization via the INtegration of Genomic data</i>) for identifying pathogenic nonsynonymous SNVs for a given query disease. Based on six functional effect scores calculated by existing methods (SIFT, PolyPhen2, LRT, MutationTaster, GERP and PhyloP) and five association scores derived from a variety of genomic data sources (gene ontology, protein-protein interactions, protein sequences, protein domain annotations and gene pathway annotations), SPRING calculates the statistical significance that an SNV is causative for a query disease and hence provides a means of prioritizing candidate SNVs. With a series of comprehensive validation experiments, we demonstrate that SPRING is valid for diseases whose genetic bases are either partly known or completely unknown and effective for diseases with a variety of inheritance styles. In applications of our method to real exome sequencing data sets, we show the capability of SPRING in detecting causative <i>de novo</i> mutations for autism, epileptic encephalopathies and intellectual disability. We further provide an online service, the standalone software and genome-wide predictions of causative SNVs for 5,080 diseases at <a href="http://bioinfo.au.tsinghua.edu.cn/spring" target="_blank">http://bioinfo.au.tsinghua.edu.cn/spring</a>.</p></div

sj-tif-2-ajr-10.1177_19458924221124363 - Supplemental material for Daphnetin Mitigates Ovalbumin-Induced Allergic Rhinitis in Mice by Regulating Nrf2/HO-1 and TLR4/NF-kB Signaling

Supplemental material, sj-tif-2-ajr-10.1177_19458924221124363 for Daphnetin Mitigates Ovalbumin-Induced Allergic Rhinitis in Mice by Regulating Nrf2/HO-1 and TLR4/NF-kB Signaling by Bo Tian, Xin Ma and Rui Jiang in American Journal of Rhinology & Allergy</p

Performance of SPRING for diseases of different inheritance styles.

<p>(A) MRRs when validating against the neutral, disease, and combined control sets, respectively. (B) AUCs when validating against the neutral, disease, and combined control sets, respectively.</p

Workflow of SPRING.

<p>Given a query disease and a set of candidate SNVs as inputs, SPRING calculates a <i>q</i>-value for each candidate and generates a ranking list of the candidates as the output. A <i>q</i>-value is calculated by using Fisher's method with dependence correction to integrate six functional effect <i>p</i>-values and five association <i>p</i>-values.</p

ROC curves of individual data sources.

<p>(A) Results when validating against the neutral control set. (B) Results when validating against the disease control set. (C) Results when validating against the combined control set.</p

Performance of SPRING in the validation experiments.

<p>(A) and (B) MRRs and AUCs for diseases with partly known genetic bases, respectively. (C) and (D) MRRs and AUCs for diseases of unknown genetic bases, respectively.</p

Rank distributions of the test SNVs.

<p>(A–C) Results for diseases with partly known genetic bases when validating against the neutral, disease, and combined control sets, respectively. (D–F) Results for diseases of unknown genetic bases when validating against the neutral, disease, and combined control sets, respectively.</p

Additional file 1: of Prediction of enhancer-promoter interactions via natural language processing

Supplementary Tables and Supplementary Figures. (DOCX 302 kb

Additional file 1: of Global inference of disease-causing single nucleotide variants from exome sequencing data

Detailed information about data preprocessing. (DOC 2331 kb