037672a61d01d3109e8b20dff4d9142f3c8a8c66 mspeir Mon Jul 20 16:28:24 2026 -0700 singleCellSignalsPeaks.html: reword two dataset descriptions Recast two dangling participial phrases ("...describing about 1.2 million elements", "...defining 359,022 elements") as plain clauses. refs #37820 Co-Authored-By: Claude Opus 4.8 (1M context) diff --git src/hg/makeDb/trackDb/human/hg38/singleCellSignalsPeaks.html src/hg/makeDb/trackDb/human/hg38/singleCellSignalsPeaks.html index f7150229478..da1cc38351c 100644 --- src/hg/makeDb/trackDb/human/hg38/singleCellSignalsPeaks.html +++ src/hg/makeDb/trackDb/human/hg38/singleCellSignalsPeaks.html @@ -1,266 +1,266 @@

Description

This track collects the cell-type chromatin accessibility tracks from the single-cell ATAC-seq datasets in the UCSC Cell Browser. For each dataset it shows the read-coverage signal (bigWig) and, where the study reported them, the accessible-region peak calls (bigBed and narrowPeak), split out by cell type. The datasets cover several human tissues, including brain, heart, and retina.

The subtracks come from these datasets:

Display Conventions and Configuration

This is a faceted collection, so the subtracks are chosen with filter menus rather than a long checkbox list. Use the facets on the track configuration page to narrow the subtracks by dataset, tissue, life stage, condition, data type, assay, and cell type, then turn on the ones you want. Signal subtracks draw as coverage graphs and peak subtracks draw as boxes. Each subtrack links back to its source dataset in the Cell Browser.

Methods

Each dataset was produced and processed by a different group, so the assays and analysis pipelines vary. The signal and peak files here are the same ones served by the individual Cell Browser datasets, copied into the browser without change. The table summarizes each dataset; see the linked publication for full detail.

DatasetAssayProcessing summary
Human Enhancer Atlas single-cell ATAC-seq (adult and fetal tissues) Accessibility profiled across 30 adult and 15 fetal tissues, integrated to call about 1.2 million candidate cis-regulatory elements across 222 cell types.
Cortex ATAC 10x single-cell ATAC-seq Developing human cortex and organoid nuclei clustered by accessibility; gene-activity signal derived from accessibility near genes.
Human and Mouse Retina Cell Atlas single-nucleus ATAC-seq Retinal nuclei clustered into cell types, with per-cell-type coverage and peak calls.
Risk Loci in Alzheimer's and Parkinson's 10x single-cell ATAC-seq 70,631 nuclei from six adult brain regions clustered by iterative LSI; peaks called per cluster with MACS2 (501 bp summits, blacklist-filtered), giving 359,022 cell type-specific elements.
Multiomic Human Heart single-nucleus ATAC-seq (with snRNA-seq) 106 snATAC datasets processed with SnapATAC2, batch-corrected by donor and study, integrated with expression from 299 donors.
Human Cardiogenesis single-cell ATAC-seq Human fetal hearts from three early stages profiled to map dynamic regulatory elements across cardiac differentiation trajectories.
Oligodendrocytes in Mouse EAE Model of MS single-cell ATAC-seq Accessibility of the oligodendrocyte lineage; the human tracks show accessibility in oligodendroglia from adult human brain.
BrainVar single-nucleus ATAC-seq Prenatal dorsolateral prefrontal cortex nuclei summarized as gene-activity coverage per major cell class.
SEA-AD Brain ATAC single-nucleus ATAC-seq Pseudobulk accessibility per cell subclass across donors spanning the full range of Alzheimer's disease neuropathological change, from middle temporal gyrus and prefrontal cortex; tracks colored by SEA-AD subclass.

The steps used to assemble the files into this track are recorded in the makeDoc.

Data Access

The subtracks can be explored in table form with the Table Browser or the Data Integrator, and read from scripts through our API.

For automated download and analysis, the signal and peak files are stored under http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/singleCellSignalsPeaks/, keeping the same per-dataset subdirectories shown on the configuration page. The files can be read with the command-line tools bigWigToBedGraph (for the signal bigWigs) and bigBedToBed (for the peak files), which can be compiled from source or downloaded as precompiled binaries. Both take a region so you do not have to download the whole file, for example: 

bigWigToBedGraph -chrom=chr1 -start=1000000 -end=1100000 \
   http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/singleCellSignalsPeaks/neuro-degen-atac/bigWig/neuronal-celltypes/projNeuron_LDSCgroup-VIP_Interneurons_GRanges_insertions_bin100_RIPnorm.bw \
   stdout
 
 bigBedToBed -chrom=chr21 -start=0 -end=48000000 \
   http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/singleCellSignalsPeaks/cortex-atac/hub/Enhancerpeaks/AstroOligo.bb \
   stdout

The underlying matrices, metadata, and per-dataset download details are on each dataset's page in the UCSC Cell Browser.

Credits

Thanks to the UCSC Cell Browser team and the research groups whose single-cell datasets are shown here. Questions about a particular subtrack are best directed to the dataset page it links to.

References

Zhang K, Hocker JD, Miller M, Hou X, Chiou J, Poirion OB, Qiu Y, Li YE, Gaulton KJ, Wang A et al. A single-cell atlas of chromatin accessibility in the human genome. Cell. 2021 Nov 24;184(24):5985-6001.e19. PMID: 34774128; PMC: PMC8664161

Ziffra RS, Kim CN, Ross JM, Wilfert A, Turner TN, Haeussler M, Casella AM, Przytycki PF, Keough KC, Shin D et al. Single-cell epigenomics reveals mechanisms of human cortical development. Nature. 2021 Oct;598(7879):205-213. PMID: 34616060; PMC: PMC8494642

Li J, Wang J, Ibarra IL, Cheng X, Luecken MD, Lu J, Monavarfeshani A, Yan W, Zheng Y, Zuo Z et al. Integrated multi-omics single cell atlas of the human retina. bioRxiv. 2023 Nov 8.

Corces MR, Shcherbina A, Kundu S, Gloudemans MJ, Frésard L, Granja JM, Louie BH, Eulalio T, Shams S, Bagdatli ST et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer's and Parkinson's diseases. Nat Genet. 2020 Nov;52(11):1158-1168. PMID: 33106633; PMC: PMC7606627

Gao W, Hu P, Wick B, Qiu Q, Zhang H, Li Y, Kang X, Bedi K, Haeussler M, Sasaki K et al. An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease. Genome Biol. 2026 Apr 6;27(1). PMID: 41937210; PMC: PMC13067603

Ameen M, Sundaram L, Shen M, Banerjee A, Kundu S, Nair S, Shcherbina A, Gu M, Wilson KD, Varadarajan A et al. Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non- coding mutations in congenital heart disease. Cell. 2022 Dec 22;185(26):4937-4953.e23. PMID: 36563664; PMC: PMC10122433

Meijer M, Agirre E, Kabbe M, van Tuijn CA, Heskol A, Zheng C, Mendanha Falcão A, Bartosovic M, Kirby L, Calini D et al. Epigenomic priming of immune genes implicates oligodendroglia in multiple sclerosis susceptibility. Neuron. 2022 Apr 6;110(7):1193-1210.e13. PMID: 35093191; PMC: PMC9810341

Gabitto MI, Travaglini KJ, Rachleff VM, Kaplan ES, Long B, Ariza J, Ding Y, Mahoney JT, Dee N, Goldy J et al. Integrated multimodal cell atlas of Alzheimer's disease. Nat Neurosci. 2024 Dec;27(12):2366-2383. PMID: 39402379; PMC: PMC11614693

Hawrylycz M, Kaplan ES, Travaglini KJ, Gabitto MI, Miller JA, Ng L, Close JL, Hodge RD, Long B, Mollenkopf T et al. SEA-AD is a multimodal cellular atlas and resource for Alzheimer's disease. Nat Aging. 2024 Oct;4(10):1331-1334. PMID: 39402332; PMC: PMC11577961