1. Users can browse HiChIP loops of interest by selecting an organ, tissue, or cell type in the hierarchical tree.
2. We provide comprehensive statistics and visualization with respect to the selected subset of HiChIP data.
   • The number distribution of HiChIP across various organs, tissues and cell types.
   • The number distribution of HiChIP across different chromosomes.
   • The length distribution of HiChIP interacitons.
3. The selected subset of HiChIP loops are displayed in an interactive table. Users can click the Detail tab to access detailed information about this HiChIP loop on a new page.

1. Users should first determine the scope of the HiChIP loop query by determining the resolution of the processing pipeline. Users can then search for HiChIP loops of interest by selecting the particular organ, tissue and cell type from pull-down menus. Users can also narrow down the scope of the HiChIP loop query by specifying associated gene, genomic location and/or SNP ID. We also provide an example to facilitate searching.
2. The results will be displayed in an interactive table.
   • Users can click the Detail tab to access detailed information about this HiChIP loop on a new page.
   • By clicking the Download button, users can download the search results in compressed format.

1. Detailed information includes the location of left anchor and right anchor, categories (by species, organ, tissue, or cell type), loop count, normalized loop count, q-value, preprocess pipeline, ChIP type and GEO accession number.
2. To help users view proximity information of HiCHIP in genomes, we developed a personalized genome browser using IGV with useful tracks, such as SNP annotation and Refseq Gene annotation. Users can interact with the visualization and export them in SVG formats.
3. We provide overview information about the left anchor, left anchor overlapped gene, right anchor and the right anchor overlapped gene. Users can click on the icons to be redirected to external databases (GeneCards, UniProt and NCBI, etc.) for additional information.
4. We also provide detailed SNP lists that are overlapped with the anchors. Users can click on the icons to view additional information at external databases (EBI and NCBI).

1. We first provide all anchors and loops data to download. We categorize the HiChIP by ChIP type, chromosome, method, organ, tissue, cell type and sample to facilitate the data downloading. For each of the category, we provide FitHiChIP 1kb, 5kb, 10kb, 50kb and hichipper preprocessed results.
2. The data can be downloaded in csv format. The corresponding MD5 files are provided to ensure the integrity of the downloaded data. We also provide advanced features such as searching and sorting for convenience.

1. Using the HiChIP H3K27ac loops of the human GM12878 cell type, we provide a demo for the analysis of HiChIP data. We walk through the workflow for analyzing the distance distribution of loops, the GC content of loop anchors, and the cell type specificity of HiChIP loops.

1. The citation information of HiChIPdb.
2. The interface for submitting data to HiChIPdb. Users can submit their HiChIP data with the PubMed reference ID and optional information such as URL, name, and E-mail. The approved data will be public available in the coming release. We appreciate the users for their contribution.
3. The contact information of HiChIPdb. Please feel free to contact us if you have any question about HiChIPdb.

In total, HiChIPdb currently contains 200 high-throughput HiChIP samples across 108 cell types. Under 5 kb resolution, 21,422,946 loops spanning 496,763 anchors were observed among all samples. All HiChIP samples are preprocessed using a unified framework including FastQC basic read quality check, HiC-Pro alignment, quality control and replicate merge. Due to the low read-pair density in HiChIP data, multiple resolution at 1/5/10/50 Kb are offered through computational tool FitHiChIP (Bhattacharyya et al., Nat. Commun., 2019) and variable length through hichipper (Caleb A Lareau et al., Nature methods, 2018). Each HiChIP sample in HiChIPdb is labeled with extensive details such as cell type, tissue, organ, etc. Various statistical measures including the length distribution of HiChIP anchors, the length distribution of HiChIP loops and the density distribution of HiChIP interactions in different chromosomes are provided on the Browse page for cross-referencing. Unlike Hi-C and other conventional chromatin conformation strategies, HiChIP signals indicate protein-centric in situ chromatin loops that carry significant functional implications. Hence, in addition to the above-mentioned visualization tools, HiChIPdb places strong emphasis on functional annotations of regulatory genes and GWAS catalog SNPs overlapping with HiChIP anchors. Annotated genes are listed on the Detail page of respective interactions with appropriate links to external databases (e.g., GeneCards, UniProt and NCBI, etc.). Similarly, annotated SNPs are linked to dbSNP. Other functionalities in HiChIPdb include advanced searching, hierarchical browsing, interactive visualization with custom tracks and data downloading in different formats. A detailed preprocessing and statistical analysis of a HiChIP sample is presented as an exemplary usage of the database on the Analysis page.

The first time the browser opens HiChIPdb, it needs to load some web plug-ins for advanced visualization and table features, resulting in the slow loading. We have been working on optimizing the experience of using HiChIPdb.

The accurate identification of HiChIP interactions has the potential to give insights to a more complete interpretation of GWAS risk variants and aid in developing disease therapy method. For example, Mumbach et al. takes advantage of this principle to chart the connectivity of autoimmune and cardiovascular disease genome-wide association study (GWAS)-identified SNPs and link SNPs to hundreds of potential target genes. Although non-genic SNPs have previously been paired with their closest neighboring gene, they find that the majority of these variants can engage in long-distance interactions, including skipping several promoters to predicted target genes, connecting to multiple genes, or acting in concert with enhancer cliques to contact a single gene. Further use of this approach will help to clarify hidden mechanisms of human disease that are driven by genetic perturbations in non-protein-coding DNA elements, which can now be linked to their cognate gene targets in primary cells.

The loop file is in csv format, and the meaning of each column is described below.