This track was produced by the DANIO-CODE project. It assigns DANIO-CODE regulatory elements to a cell type using single-cell ATAC-seq, which measures open chromatin one cell at a time instead of averaging over the whole embryo.
This track shows the DANIO-CODE regulatory elements that could be assigned to a cell type this way. There is one track per cell type and a combined track holding all 54,230 assigned elements. The cell types are blood, central nervous system (two classes), differentiating neurons, endothelium, epidermis, epidermis with olfactory and pronephric cells, midbrain, muscle (two classes), neural crest, optic vesicle, spinal cord, and four mixed classes covering periderm, tailbud and pharyngeal mesoderm.
Each element is drawn in the color the consortium assigned to its cell type, so the combined track can be read by color. The per-cell-type tracks use the same colors.
Four of the mixed classes, epidermis/periderm, muscle/tailbud, pharyngeal mesoderm/tailbud and tailbud/spinal cord, do not have their own track because the consortium's files for them are missing from their server. Their elements are present in the combined all-cell-types track.
The DANIO-CODE consortium assembled 1,802 zebrafish developmental genomics datasets, 1,438 of them already published and 366 generated by consortium members, and reprocessed all of them from the raw sequencing reads so that samples from different laboratories and different protocols can be compared with each other. ChIP-seq and ATAC-seq were run through the ENCODE pipelines, CAGE-seq through the FANTOM pipeline, and Hi-C and 4C-seq through the pipelines of the groups that produced them. The pipelines are published at gitlab.com/danio-code, and samples were assigned to developmental stages using ZFIN and ENCODE nomenclature. See Baranasic et al. 2022 for details.
Cell-type assignment used published single-cell ATAC-seq of zebrafish embryos. Cells were clustered, the clusters were labeled with known marker genes, and each candidate regulatory element was assigned to the cluster in which it is open. Element counts per cell type range from 1,357 for blood to 7,085 for epidermis; the combined track holds 54,230 elements.
At UCSC the tracks were converted from the consortium's public track hub at trackhub2.genereg.net/DANIO-CODE with the script danioCodeHubToRa.py, and the data files were copied from the same server. The data themselves were not modified. The steps are documented in our makeDoc.
The data can be explored interactively in table format with the Table Browser or the Data Integrator and exported from there to spreadsheet or tab-separated tables. From scripts, the data can be accessed through our API, track=dcComp_cell_type.
For automated download and analysis, the annotations are stored in bigBed files that can be downloaded from our download server. Files are named after the cell type, for example Blood.colors.bb, and the combined file is cell_types.colors.sorted.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain features within a given range, for example
bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/danRer11/danioCode/cell_types.colors.sorted.bb \
-chrom=chr1 -start=20000000 -end=20100000 stdout
The original data files, and the sample and protocol metadata behind them, are available from the DANIO-CODE data coordination center at danio-code.zfin.org and from the consortium's track hub at trackhub2.genereg.net/DANIO-CODE.
Thanks to the DANIO-CODE consortium for collecting, reprocessing and publishing these data, and to the laboratories that produced the original datasets.
Baranasic D, Hörtenhuber M, Balwierz PJ, Zehnder T, Mukarram AK, Nepal C, Várnai C, Hadzhiev Y, Jimenez-Gonzalez A, Li N et al. Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis- regulatory elements. Nat Genet. 2022 Jul;54(7):1037-1050. PMID: 35789323; PMC: PMC9279159