Deciphering the comprehensive knowledgebase landscape featuring infertility with IDDB Xtra.

Infertility affects ∼15% of couples globally and half of cases are related to genetic disorders. Despite growing data and unprecedented improvements in high-throughput sequencing technologies, accumulated fertility-related issues concerning genetic diagnosis and potential treatment are urgent to be solved. However, there is a lack of comprehensive platforms that characterise various infertility-related records to provide research applications for exploring infertility in-depth and genetic counselling of infertility couple. To solve this problem, we provide IDDB Xtra by further integrating phenotypic manifestations, genomic datasets, epigenetics, modulators in collaboration with numerous interactive tools into our previous infertility database, IDDB. IDDB Xtra houses manually-curated 2369 genes of human and nine model organisms, 273 chromosomal abnormalities, 884 phenotypes, 60 genomic datasets, 464 epigenetic records, 1144 modulators relevant to infertility diagnosis and treatment. Additionally, IDDB Xtra incorporated customized graphical applications for researchers and clinicians to decipher in-depth disease mechanisms from the perspectives of developmental atlas, mutation effects, and clinical manifestations. Users can browse genes across developmental stages of human and mouse, filter candidate genes, mine potential variants and retrieve infertility biomedical network in an intuitive web interface. In summary, IDDB Xtra not only captures valuable research and data, but also provides useful applications to facilitate the genetic counselling and drug discovery of infertility. IDDB Xtra is freely available at https://mdl.shsmu.edu.cn/IDDB/and http://www.allostery.net/IDDB.

Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes.

G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.