Documentation How to use the database?

     To systematically investigate the factors contributing to rhythmic gene expression in transcriptional and post-transcriptional levels, it is essential to depict rhythmic gene expression pattern with their transcriptional, post-transcriptional regulatory information by integrating published gene expression profiles, binding profiles of core clock genes and other transcriptional factors, and epigenomic regulatory factors (histone modifications and miRNA regulation).

    Herein, we constructed a freely available CirGRDB database based on high-throughput genome-wide data sets (microarray, RNA-Seq, ChIP-Seq and small-RNA-Seq) related to circadian rhythms in genome-wide. After implementing softwares to different types of data sets, we acquired 8 kinds of regulatory elements relating to control rhythmically gene expression. Further, web interface was constructed with LAMP (Linux + Apache + MySQL + PHP).

Workflow of the construction of CirGRDB.

Web interface of CirGRDB

Information provided in CirGRDB

Groups of RNA expression data sets

Group 1: Rhythmic pattern of interested gene

Group 2: Rhythmic pattern of interested gene

Filter for specific tissue

Group 3: Effect of KO/KD or Over expression specific gene

Regulatory information

Regulatory network

How to use JBrowse

Visualization with different groups

Regulatory information of Pparg.

Case of 鈥楥irGRDB鈥 search (A, B) enables integrative viewing of the expression patterns (C-F), multiple potential regulatory information (G-J) and network (K) of the target gene (Pparg as an example). User can retrieve 鈥淓xpression鈥, 鈥淩egulation鈥 and 鈥淣etwork鈥 information related to Pparg (B). 鈥淓xpression鈥 was composed with three groups. Expression patterns of Pparg with raw expression level or Z score normalized in 8 mouse tissues (C), and in livers of wild type, Sirt1-/-, and Sirt6-/- mice (D) at circadian time (CT) or zeitgeber time (ZT). Different expression patterns of PPARG in human breast cancer cell line (MCF7) and normal breast cell line (MCF10A) were observed (E). Knockout of Ncor1/Ncor2 results in increased expression of Pparg in liver, whereas knockout of Per2 results in decreased expression of Pparg in liver (F). Differential binding of Rev-erba between rhythmic expression time points (G). Peak time represents time point with the highest binding of Rev-erba, while trough time represents time point with the lowest binding of Rev-erba. H3K27ac peaks nearby Pparg show similar expression pattern with Pparg (H).Enhancer RNA (eRNA) nearby Pparg shows rhythmic expression pattern (I). Knockout of SRC2 results in deactivation of DNase I hypersensitive sites (J). Network of input gene was constructed based on PTHGRN database (K).