GametesOmics: A Comprehensive Multi-omics Database for Exploring the Gametogenesis in Humans and Mice.
Jianting AnJing WangSi-Ming KongShi SongWei ChenPeng YuanQi-Long HeYi-Dong ChenYe LiYi YangWei WangRong LiLi-Ying YanZhi-Qiang YanJie QiaoPublished in: Genomics, proteomics & bioinformatics (2024)
Gametogenesis plays an important role in the reproduction and evolution of species. The transcriptomic and epigenetic alterations in this process can influence the reproductive capacity, fertilization, and embryonic development. The rapidly increasing single-cell studies have provided valuable multi-omics resources. However, data from different layers and sequencing platforms have not been uniformed and integrated, which greatly limits their use for exploring the molecular mechanisms that underlie oogenesis and spermatogenesis. Here, we develop GametesOmics, a comprehensive database that integrates the data of gene expression, DNA methylation, and chromatin accessibility during oogenesis and spermatogenesis in humans and mice. GametesOmics provides a user-friendly website and various tools, including Search and Advanced Search for querying the expression and epigenetic modification(s) of each gene; Tools with Differentially expressed gene (DEG) analysis for identifying DEGs, Correlation analysis for demonstrating the genetic and epigenetic changes, Visualization for displaying single-cell clusters and screening marker genes as well as master transcription factors (TFs), and MethylView for studying the genomic distribution of epigenetic modifications. GametesOmics also provides Genome Browser and Ortholog for tracking and comparing gene expression, DNA methylation, and chromatin accessibility between humans and mice. GametesOmics offers a comprehensive resource for biologists and clinicians to decipher the cell fate transition in germ cell development, and can be accessed at http://gametesomics.cn/.
Keyphrases
- dna methylation
- genome wide
- single cell
- gene expression
- copy number
- rna seq
- high throughput
- high fat diet induced
- transcription factor
- cell fate
- genome wide identification
- germ cell
- poor prognosis
- electronic health record
- squamous cell carcinoma
- metabolic syndrome
- artificial intelligence
- dna damage
- insulin resistance
- adipose tissue
- long non coding rna
- deep learning
- dna binding
- data analysis