CuGenDBv2: an updated database for cucurbit genomics.
Jingyin YuShan WuHonghe SunXin WangXuemei TangShaogui GuoZhonghua ZhangSanwen HuangYong XuYiqun WengMichael MazourekCecilia McGregorSusanne S RennerSandra BranhamChandrasekar KousikW Patrick WechterAmnon LeviRebecca GrumetYi ZhengZhangjun FeiPublished in: Nucleic acids research (2022)
The Cucurbitaceae (cucurbit) family consists of about 1,000 species in 95 genera, including many economically important and popular fruit and vegetable crops. During the past several years, reference genomes have been generated for >20 cucurbit species, and variome and transcriptome profiling data have been rapidly accumulated for cucurbits. To efficiently mine, analyze and disseminate these large-scale datasets, we have developed an updated version of Cucurbit Genomics Database. The updated database, CuGenDBv2 (http://cucurbitgenomics.org/v2), currently hosts 34 reference genomes from 27 cucurbit species/subspecies belonging to 10 different genera. Protein-coding genes from these genomes have been comprehensively annotated by comparing their protein sequences to various public protein and domain databases. A novel 'Genotype' module has been implemented to facilitate mining and analysis of the functionally annotated variome data including SNPs and small indels from large-scale genome sequencing projects. An updated 'Expression' module has been developed to provide a comprehensive gene expression atlas for cucurbits. Furthermore, synteny blocks between any two and within each of the 34 genomes, representing a total of 595 pair-wise genome comparisons, have been identified and can be explored and visualized in the database.
Keyphrases
- single cell
- genome wide
- gene expression
- rna seq
- adverse drug
- binding protein
- protein protein
- big data
- electronic health record
- dna methylation
- amino acid
- poor prognosis
- genetic diversity
- healthcare
- mental health
- emergency department
- machine learning
- small molecule
- long non coding rna
- transcription factor
- artificial intelligence
- deep learning
- psychometric properties
- genome wide association