FifBase: a comprehensive fertility-associated indicators factor database for domestic animals.
Hao LiJunyao HouZiyu ChenJingyu ZengYu NiYayu LiXia XiaoYaqi ZhouNing ZhangDeyu LongHongfei LiuLuyu YangXinyue BaiQun LiTongtong LiDongxue CheLeijie LiXiaodan WangPeng ZhangMingzhi LiaoPublished in: Briefings in bioinformatics (2021)
Fertility refers to the ability of animals to maintain reproductive function and give birth to offspring, which is an important indicator to measure the productivity of animals. Fertility is affected by many factors, among which environmental factors may also play key roles. During the past years, substantial research studies have been conducted to detect the factors related to fecundity, including genetic factors and environmental factors. However, the identified genes associated with fertility from countless previous studies are randomly dispersed in the literature, whereas some other novel fertility-related genes are needed to detect from omics-based datasets. Here, we constructed a fertility index factor database FifBase based on manually curated published literature and RNA-Seq datasets. During the construction of the literature group, we obtained 3301 articles related to fecundity for 13 species from PubMed, involving 2823 genes, which are related to 75 fecundity indicators or 47 environmental factors. Eventually, 1558 genes associated with fertility were filtered in 10 species, of which 1088 and 470 were from RNA-Seq datasets and text mining data, respectively, involving 2910 fertility-gene pairs and 58 fertility-environmental factors. All these data were cataloged into FifBase (http://www.nwsuaflmz.com/FifBase/), where the fertility-related factor information, including gene annotation and environmental factors, can be browsed, retrieved and downloaded with the user-friendly interface.
Keyphrases
- rna seq
- single cell
- childhood cancer
- systematic review
- genome wide
- emergency department
- healthcare
- magnetic resonance imaging
- randomized controlled trial
- wastewater treatment
- type diabetes
- dna methylation
- magnetic resonance
- skeletal muscle
- machine learning
- drug induced
- artificial intelligence
- genome wide analysis
- data analysis
- deep learning
- high density