excluderanges, genomic ranges of problematic genomic regions

Ogata, Jonathan D, Wancen Mu, Eric S Davis, Bingjie Xue, J Chuck Harrell, Nathan C Sheffield, Douglas H Phanstiel, Michael I Love, and Mikhail G Dozmorov. “Excluderanges: Exclusion Sets for T2T-CHM13, GRCm39, and Other Genome Assemblies.” Bioinformatics, April 3, 2023, PMID: 37067481, DOI: 10.1093/bioinformatics/btad198

Coordinates of problematic genomic regions that should be avoided when working with genomic data (Ogata et al. 2023). For human, mouse, and selected model organisms. Please, open an issue to suggest other resources. Package on Bioconductor: excluderanges.

New (2022-09-20) - Exclusion sets for human T2T-CHM13 and mouse GRCm39/mm39 genome assemblies are available. Download all data from Google Drive.

TL;DR - For human hg38 genome assembly, Anshul and we recommend ENCFF356LFX exclusion list regions. Also available as hg38.Kundaje.GRCh38_unified_Excludable excluderanges object (AnnotationHub ID: AH107305) and BEDbase.org.

BED files of exclusion regions are available on the ENCODE project website and scattered across various websites, such as Blacklist (Amemiya, Kundaje, and Boyle 2019), Peakpass (Wimberley and Heber 2019), Greenscreen (Klasfeld and Wagner 2022). Human and mouse genome assemblies have the largest number of exclusion sets generated by multiple labs. These exclusion sets frequently lack annotation and curation methods, creating uncertainty what to use. The purpose of this package is to provide a unified place for informed retrieval of exclusion regions.

Naming convention: <genome assembly>.<lab>.<original file name>, e.g., hg19.Birney.wgEncodeDacMapabilityConsensusExcludable.

See make-data.R how we created excluderanges objects.

Installation instructions

Install the latest release of R, then get the latest version of Bioconductor by starting R and entering the commands:

# if (!require("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install(version = "3.16")

Then, install additional packages using the following code:

# BiocManager::install("AnnotationHub", update = FALSE) 
# BiocManager::install("GenomicRanges", update = FALSE)
# BiocManager::install("plyranges", update = FALSE)

Use excluderanges

Get an overview of what’s available

suppressMessages(library(GenomicRanges))
suppressMessages(library(AnnotationHub))
ah <- AnnotationHub()
#> snapshotDate(): 2022-10-31
query_data <- subset(ah, preparerclass == "excluderanges")
# You can search for multiple terms
# query_data <- query(ah, c("excluderanges", "Kundaje", "hg38"))
query_data
#> AnnotationHub with 82 records
#> # snapshotDate(): 2022-10-31
#> # $dataprovider: UCSC, GitHub, ENCODE, UCSChub, excluderanges, Stanford.edu,...
#> # $species: Homo sapiens, Mus musculus, Drosophila melanogaster, Danio rerio...
#> # $rdataclass: GRanges
#> # additional mcols(): taxonomyid, genome, description,
#> #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> #   rdatapath, sourceurl, sourcetype 
#> # retrieve records with, e.g., 'object[["AH107304"]]' 
#> 
#>              title                                 
#>   AH107304 | T2T.excluderanges                     
#>   AH107305 | hg38.Kundaje.GRCh38_unified_Excludable
#>   AH107306 | hg38.Bernstein.Mint_Excludable_GRCh38 
#>   AH107307 | hg38.Boyle.hg38-Excludable.v2         
#>   AH107308 | hg38.Kundaje.GRCh38.Excludable        
#>   ...        ...                                   
#>   AH107381 | danRer10.UCSC.scaffold                
#>   AH107382 | dm6.UCSC.other                        
#>   AH107383 | dm3.UCSC.contig                       
#>   AH107384 | dm3.UCSC.scaffold                     
#>   AH107385 | TAIR10.UCSC.araTha1.gap

hg38.Kundaje.GRCh38_unified_Excludable object recommended by Anshul

excludeGR.hg38.Kundaje.1 <- query_data[["AH107305"]]
#> loading from cache
# Always a good idea to sort GRanges and keep standard chromosomes
excludeGR.hg38.Kundaje.1 <- excludeGR.hg38.Kundaje.1 %>% 
  sort() %>% keepStandardChromosomes(pruning.mode = "tidy")
excludeGR.hg38.Kundaje.1
#> GRanges object with 910 ranges and 0 metadata columns:
#>         seqnames            ranges strand
#>            <Rle>         <IRanges>  <Rle>
#>     [1]     chr1     628903-635104      *
#>     [2]     chr1   5850087-5850571      *
#>     [3]     chr1   8909610-8910014      *
#>     [4]     chr1   9574580-9574997      *
#>     [5]     chr1 32043823-32044203      *
#>     ...      ...               ...    ...
#>   [906]     chrY 11290797-11334278      *
#>   [907]     chrY 11493053-11592850      *
#>   [908]     chrY 11671014-11671046      *
#>   [909]     chrY 11721528-11749472      *
#>   [910]     chrY 56694632-56889743      *
#>   -------
#>   seqinfo: 24 sequences from hg38 genome

Save the data in a BED file, if needed.

# Note that rtracklayer::import and rtracklayer::export perform unexplained
# start coordinate conversion, likely related to 0- and 1-based coordinate
# system. We recommend converting GRanges to a data frame and save tab-separated
write.table(as.data.frame(excludeGR.hg38.Kundaje.1), 
            file = "hg38.Kundaje.GRCh38_unified_Excludable.bed",
            sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)

We can load other excludable regions for the hg38 genome assembly and compare them.

query_data <- query(ah, c("excluderanges", "hg38"))
query_data
#> AnnotationHub with 17 records
#> # snapshotDate(): 2022-10-31
#> # $dataprovider: UCSC, ENCODE, GitHub, UCSChub
#> # $species: Homo sapiens
#> # $rdataclass: GRanges
#> # additional mcols(): taxonomyid, genome, description,
#> #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> #   rdatapath, sourceurl, sourcetype 
#> # retrieve records with, e.g., 'object[["AH107305"]]' 
#> 
#>              title                                 
#>   AH107305 | hg38.Kundaje.GRCh38_unified_Excludable
#>   AH107306 | hg38.Bernstein.Mint_Excludable_GRCh38 
#>   AH107307 | hg38.Boyle.hg38-Excludable.v2         
#>   AH107308 | hg38.Kundaje.GRCh38.Excludable        
#>   AH107309 | hg38.Lareau.hg38.full.Excludable      
#>   ...        ...                                   
#>   AH107355 | hg38.UCSC.telomere                    
#>   AH107356 | hg38.UCSC.short_arm                   
#>   AH107357 | hg38.UCSC.heterochromatin             
#>   AH107358 | hg38.UCSC.contig                      
#>   AH107359 | hg38.UCSC.scaffold
excludeGR.hg38.Bernstein <- query_data[["AH107306"]]
#> loading from cache
excludeGR.hg38.Boyle     <- query_data[["AH107307"]]
#> loading from cache
excludeGR.hg38.Kundaje.2 <- query_data[["AH107308"]]
#> loading from cache
excludeGR.hg38.Lareau    <- query_data[["AH107309"]]
#> loading from cache
excludeGR.hg38.Reddy     <- query_data[["AH107310"]]
#> loading from cache
excludeGR.hg38.Wimberley <- query_data[["AH107311"]]
#> loading from cache
excludeGR.hg38.Wold      <- query_data[["AH107312"]]
#> loading from cache
excludeGR.hg38.Yeo       <- query_data[["AH107313"]]
#> loading from cache

Compare the number of excludable regions 

library(ggplot2)
mtx_to_plot <- data.frame(Count = c(length(excludeGR.hg38.Bernstein), 
                                    length(excludeGR.hg38.Boyle),
                                    length(excludeGR.hg38.Kundaje.1), 
                                    length(excludeGR.hg38.Kundaje.2), 
                                    length(excludeGR.hg38.Lareau),
                                    length(excludeGR.hg38.Reddy), 
                                    length(excludeGR.hg38.Wimberley),
                                    length(excludeGR.hg38.Wold), 
                                    length(excludeGR.hg38.Yeo)),
                          Source = c("Bernstein.Mint_Excludable_GRCh38", 
                                     "Boyle.hg38-Excludable.v2",
                                     "Kundaje.GRCh38_unified_Excludable", 
                                     "Kundaje.GRCh38.Excludable", 
                                     "Lareau.hg38.full.Excludable",
                                     "Reddy.wgEncodeDacMapabilityConsensusExcludable", 
                                     "Wimberley.peakPass60Perc_sorted",
                                     "Wold.hg38mitoExcludable", 
                                     "Yeo.eCLIP_Excludableregions.hg38liftover.bed"))
# Order Source by the number of regions
mtx_to_plot$Source <- factor(mtx_to_plot$Source, levels = mtx_to_plot$Source[order(mtx_to_plot$Count)])

ggplot(mtx_to_plot, aes(x = Source, y = Count, fill = Source)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  theme_bw() + theme(legend.position = "none")

# ggsave("man/figures/excluderanges_hg38_count.png", width = 5.5, height = 2)

Compare the width of excludable regions

log2 scale because of heavy right tail distributions.

library(ggridges)
library(dplyr)
mtx_to_plot <- data.frame(Width = c(width(excludeGR.hg38.Bernstein), 
                                    width(excludeGR.hg38.Boyle),
                                    width(excludeGR.hg38.Kundaje.1), 
                                    width(excludeGR.hg38.Kundaje.2), 
                                    width(excludeGR.hg38.Lareau),
                                    width(excludeGR.hg38.Reddy), 
                                    width(excludeGR.hg38.Wimberley),
                                    width(excludeGR.hg38.Wold), 
                                    width(excludeGR.hg38.Yeo)),
                          Source = c(rep("Bernstein.Mint_Excludable_GRCh38", length(excludeGR.hg38.Bernstein)),
                                     rep("Boyle.hg38-Excludable.v2", length(excludeGR.hg38.Boyle)),
                                     rep("Kundaje.GRCh38_unified_Excludable", length(excludeGR.hg38.Kundaje.1)),
                                     rep("Kundaje.GRCh38.Excludable", length(excludeGR.hg38.Kundaje.2)),
                                     rep("Lareau.hg38.full.Excludable", length(excludeGR.hg38.Lareau)),
                                     rep("Reddy.wgEncodeDacMapabilityConsensusExcludable", length(excludeGR.hg38.Reddy)),
                                     rep("Wimberley.peakPass60Perc_sorted", length(excludeGR.hg38.Wimberley)),
                                     rep("Wold.hg38mitoExcludable", length(excludeGR.hg38.Wold)),
                                     rep("Yeo.eCLIP_Excludableregions.hg38liftover.bed", length(excludeGR.hg38.Yeo))))

# Order objects by decreasing width
mtx_to_plot$Source <- factor(mtx_to_plot$Source, levels = mtx_to_plot %>% 
                               group_by(Source) %>% summarise(Mean = mean(Width)) %>% 
                               arrange(Mean) %>% pull(Source))
ggplot(mtx_to_plot, aes(x = log2(Width), y = Source, fill = Source)) +
  geom_density_ridges() +
  theme_bw() + theme(legend.position = "none")

# ggsave("man/figures/excluderanges_hg38_width.png", width = 5.5, height = 2)

We can investigate the total width of each set of excludable ranges.

mtx_to_plot <- data.frame(TotalWidth = c(sum(width(excludeGR.hg38.Bernstein)), 
                                         sum(width(excludeGR.hg38.Boyle)),
                                         sum(width(excludeGR.hg38.Kundaje.1)), 
                                         sum(width(excludeGR.hg38.Kundaje.2)), 
                                         sum(width(excludeGR.hg38.Lareau)),
                                         sum(width(excludeGR.hg38.Reddy)), 
                                         sum(width(excludeGR.hg38.Wimberley)),
                                         sum(width(excludeGR.hg38.Wold)), 
                                         sum(width(excludeGR.hg38.Yeo))), 
                          Source = c("Bernstein.Mint_Excludable_GRCh38", 
                                     "Boyle.hg38-Excludable.v2",
                                     "Kundaje.GRCh38_unified_Excludable", 
                                     "Kundaje.GRCh38.Excludable", 
                                     "Lareau.hg38.full.Excludable",
                                     "Reddy.wgEncodeDacMapabilityConsensusExcludable", 
                                     "Wimberley.peakPass60Perc_sorted",
                                     "Wold.hg38mitoExcludable", 
                                     "Yeo.eCLIP_Excludableregions.hg38liftover.bed"))
# Order objects by decreasing width
mtx_to_plot$Source <- factor(mtx_to_plot$Source, levels = mtx_to_plot %>% 
                               group_by(Source) %>% arrange(TotalWidth) %>% pull(Source))

ggplot(mtx_to_plot, aes(x = TotalWidth, y = Source, fill = Source)) + 
  geom_bar(stat="identity") + scale_x_log10() + scale_y_discrete(label=abbreviate, limits=rev) +
  xlab("log10 total width")

# ggsave("man/figures/excluderanges_hg38_sumwidth.png", width = 6.5, height = 2)

Compare overlaps among sets

We can compare overlap coefficients between those sets of excludable regions.

library(pheatmap)
library(stringr)
# Overlap coefficient calculations
overlap_coefficient <- function(gr_a, gr_b) {
  intersects <- GenomicRanges::intersect(gr_a, gr_b, ignore.strand = TRUE)
  intersection_width <- sum(width(intersects))
  min_width <- min(sum(width(gr_a)), sum(width(gr_b)))
  DataFrame(intersection_width, min_width, 
            overlap_coefficient = intersection_width/min_width,
             n_intersections = length(intersects))
}
# List and names of all excludable regions
all_excludeGR_list <- list(excludeGR.hg38.Bernstein, 
                            excludeGR.hg38.Boyle,
                            excludeGR.hg38.Kundaje.1, 
                            excludeGR.hg38.Kundaje.2,
                            excludeGR.hg38.Lareau,
                            excludeGR.hg38.Reddy,
                            excludeGR.hg38.Wimberley,
                            excludeGR.hg38.Wold,
                            excludeGR.hg38.Yeo)
all_excludeGR_name <- c("Bernstein.Mint_Excludable_GRCh38", 
                         "Boyle.hg38-Excludable.v2",
                         "Kundaje.GRCh38_unified_Excludable", 
                         "Kundaje.GRCh38.Excludable", 
                         "Lareau.hg38.full.Excludable",
                         "Reddy.wgEncodeDacMapabilityConsensusExcludable", 
                         "Wimberley.peakPass60Perc_sorted",
                         "Wold.hg38mitoExcludable", 
                         "Yeo.eCLIP_Excludableregions.hg38liftover.bed")
# Correlation matrix, empty
mtx_to_plot <- matrix(data = 0, nrow = length(all_excludeGR_list), ncol = length(all_excludeGR_list))
# Fill it in
for (i in 1:length(all_excludeGR_list)) {
  for (j in 1:length(all_excludeGR_list)) {
    # If diagonal, set to zero
    if (i == j) mtx_to_plot[i, j] <- 0
    # Process only one half, the other is symmetric
    if (i > j) {
      mtx_to_plot[i, j] <- mtx_to_plot[j, i] <- overlap_coefficient(all_excludeGR_list[[i]], all_excludeGR_list[[j]])[["overlap_coefficient"]]
    }
  }
}
# Trim row/colnames
rownames(mtx_to_plot) <- colnames(mtx_to_plot) <- str_trunc(all_excludeGR_name, width = 25) 
# Save the plot
# png("man/figures/excluderanges_hg38_jaccard.png", width = 1000, height = 900, res = 200)
pheatmap(data.matrix(mtx_to_plot), clustering_method = "ward.D")

# dev.off()

Metadata analysis

Note that some excludable ranges objects contain six columns, implying there may be some interesting metadata. Let’s explore one.

mcols(excludeGR.hg38.Reddy)
#> DataFrame with 396 rows and 2 columns
#>                       name     score
#>                <character> <numeric>
#> 1   High_Mappability_isl..      1000
#> 2         Satellite_repeat      1000
#> 3                 BSR/Beta      1000
#> 4   Low_mappability_island      1000
#> 5                 (CATTC)n      1000
#> ...                    ...       ...
#> 392       Satellite_repeat      1000
#> 393               (CATTC)n      1000
#> 394               (CATTC)n      1000
#> 395                   TAR1      1000
#> 396                   chrM      1000
mtx_to_plot <- table(mcols(excludeGR.hg38.Reddy)[["name"]]) %>%
  as.data.frame()
colnames(mtx_to_plot) <- c("Type", "Number")
mtx_to_plot <- mtx_to_plot[order(mtx_to_plot$Number), ]
mtx_to_plot$Type <- factor(mtx_to_plot$Type, 
                           levels = mtx_to_plot$Type)
ggplot(mtx_to_plot, aes(x = Number, y = Type, fill = Type)) +
  geom_bar(stat="identity") +
  theme_bw() + theme(legend.position = "none")

# ggsave("man/figures/excluderanges_hg38_Reddy_metadata.png", width = 5, height = 2.5)

One may decide to combine the excludable ranges from all labs, although from previous results we may decide to follow Anshul’s advice about the ENCFF356LFX exclusion list regions and use the excludeGR.hg38.Kundaje.1 object.

excludeGR.hg38.all <- reduce(c(excludeGR.hg38.Bernstein, 
                               excludeGR.hg38.Boyle,
                               excludeGR.hg38.Kundaje.1, 
                               excludeGR.hg38.Kundaje.2, 
                               excludeGR.hg38.Lareau,
                               excludeGR.hg38.Reddy, 
                               excludeGR.hg38.Wimberley,
                               excludeGR.hg38.Wold, 
                               excludeGR.hg38.Yeo))
# Sort and Keep only standard chromosomes
excludeGR.hg38.all <- excludeGR.hg38.all %>% sort %>% 
  keepStandardChromosomes(pruning.mode = "tidy")
print(length(excludeGR.hg38.all))
#> [1] 15998
summary(width(excludeGR.hg38.all))
#>     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
#>        5     1703     2294    17713     3001 30739401

BEDbase data download

BEDbase.org is a repository for storing and analyzing BED files and BED sets, developed by Sheffield lab. It provides API for data access and retrieval.

Using BEDbase ID (e.g., 1a561729234c2844303a051b16f66656 for the hg38.Kundaje.GRCh38_unified_Excludable excluderanges object), we can construct a URL showing the splash screen with detailed information of the corresponding object, http://bedbase.org/#/bedsplash/1a561729234c2844303a051b16f66656.

We can also get the BED file using the BEDbase API.

wget http://bedbase.org/api/bed/1a561729234c2844303a051b16f66656/file/bed \
  -O hg38.Kundaje.GRCh38_unified_Excludable.bed.gz

See also the R code to download T2T.excluderanges and mm39.excluderanges.

Mitochondrial DNA sequences, NUMTs

Mitochondrial DNA sequences (mtDNA, 100-600K mitochondria per human cell) transferred to the nucleus give rise to the so-called mitochondrial DNA sequences in the nuclear genome (NUMTs). In the settings of DNA/chromatin sequencing (e.g., ATAC-seq), we may observe up to 80% of mitochondrial sequencing reads that may pile up in the NUMT sequences. Similar to exclusion sets, genomic regions highly homologous to mtDNA can be masked to improve biological signal.

The reference human nuclear mitochondrial sequences have been available in the UCSC genome browser for hg19 and mm8 human/mouse genome assemblies. We collected NUMT sets for hg38, T2T-CHM13, mm10, generated by Caleb Lareau in the mitoblacklist GitHub repository. These NUMT sets can be combined with exclusion sets.

Example of the hg38.Lareau.hg38_peaks object

suppressMessages(library(httr))
# Get hg38.Lareau.hg38_peaks BEDbase ID
bedbase_id <- "9fa55701a3bd3e7a598d1d2815e3390f"
# Construct output file name
fileNameOut <- "hg38.Lareau.hg38_peak.bed.gz"
# API token for BED data
token2 <- paste0("http://bedbase.org/api/bed/", bedbase_id, "/file/bed")
# Download file
GET(url = token2, write_disk(fileNameOut, overwrite = TRUE)) # , verbose()
#> Response [http://data.bedbase.org/bed_files/hg38.Lareau.hg38_peaks.bed.gz]
#>   Date: 2023-07-06 01:34
#>   Status: 200
#>   Content-Type: application/vnd.realvnc.bed
#>   Size: 11.8 kB
#> <ON DISK>  /Users/mdozmorov/Documents/Work/GitHub/excluderanges/hg38.Lareau.hg38_peak.bed.gz
# Read the data in
hg38.Lareau.hg38_peaks <- read.table(fileNameOut, sep = "\t", header = FALSE)
# Assign column names depending on the number of columns
all_columns <- c("chr", "start", "stop", "name", "score", "strand", 
                 "signalValue", "pValue", "qValue", "peak")
colnames(hg38.Lareau.hg38_peaks) <- all_columns[1:ncol(hg38.Lareau.hg38_peaks)]
# Convert to GRanges object
hg38.Lareau.hg38_peaks <- makeGRangesFromDataFrame(hg38.Lareau.hg38_peaks, 
                                                   keep.extra.columns = TRUE)
hg38.Lareau.hg38_peaks
#> GRanges object with 784 ranges and 2 metadata columns:
#>         seqnames              ranges strand |                name     score
#>            <Rle>           <IRanges>  <Rle> |         <character> <integer>
#>     [1]     chr1       628903-635104      * |   peaks/hg38_peak_1   2523899
#>     [2]     chr1     5850087-5850571      * |   peaks/hg38_peak_2     32940
#>     [3]     chr1     8909610-8910014      * |   peaks/hg38_peak_3       123
#>     [4]     chr1     9574580-9574997      * |   peaks/hg38_peak_4     28828
#>     [5]     chr1   32043823-32044203      * |   peaks/hg38_peak_5      8083
#>     ...      ...                 ...    ... .                 ...       ...
#>   [780]     chrX 143431716-143432219      * | peaks/hg38_peak_780     41434
#>   [781]     chrX 143432410-143433212      * | peaks/hg38_peak_781      9271
#>   [782]     chrX 143433510-143434156      * | peaks/hg38_peak_782      5741
#>   [783]     chrX 143543636-143544023      * | peaks/hg38_peak_783      3320
#>   [784]     chrX 146995842-146996224      * | peaks/hg38_peak_784     27222
#>   -------
#>   seqinfo: 23 sequences from an unspecified genome; no seqlengths

Centromeres, telomeres, etc.

Besides the ENCODE-produced excludable regions, we may want to exclude centromeres, telomeres, and other gap locations. The “Gap Locations” track for Homo Sapiens is available for the GRcH37/hg19 genome assembly as a UCSC ‘gap’ table. It can be retrieved from AnnotationHub, but lacks the metadata columns needed to decide the type of gaps.

# Search for the gap track
# ahData <- query(ah, c("gap", "Homo sapiens", "hg19"))
# ahData[ahData$title == "Gap"]
gaps <- ahData[["AH6444"]]

The UCSC ‘gap’ table provides better granularity about the types of gaps available. E.g., for human, hg19, we have the following types and the number of gaps.

Those objects are provided as individual GRanges.

Naming convention: <genome assembly>.UCSC.<gap type>, e.g., hg38.UCSC.gap_centromere. We can similarly load any gap type object.

query_data <- query(ah, c("excluderanges", "UCSC", "Homo Sapiens", "hg38"))
query_data
#> AnnotationHub with 7 records
#> # snapshotDate(): 2022-10-31
#> # $dataprovider: UCSC, UCSChub
#> # $species: Homo sapiens
#> # $rdataclass: GRanges
#> # additional mcols(): taxonomyid, genome, description,
#> #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> #   rdatapath, sourceurl, sourcetype 
#> # retrieve records with, e.g., 'object[["AH107353"]]' 
#> 
#>              title                    
#>   AH107353 | T2T.UCSC.hgUnique.hg38   
#>   AH107354 | hg38.UCSC.centromere     
#>   AH107355 | hg38.UCSC.telomere       
#>   AH107356 | hg38.UCSC.short_arm      
#>   AH107357 | hg38.UCSC.heterochromatin
#>   AH107358 | hg38.UCSC.contig         
#>   AH107359 | hg38.UCSC.scaffold

gapsGR_hg38_centromere <- query_data[["AH107354"]]
#> loading from cache
gapsGR_hg38_centromere
#> GRanges object with 109 ranges and 2 metadata columns:
#>         seqnames              ranges strand |       bin        name
#>            <Rle>           <IRanges>  <Rle> | <numeric> <character>
#>     [1]     chr1 122026459-122224535      * |       189  GJ211836.1
#>     [2]     chr1 122224635-122503147      * |       189  GJ211837.1
#>     [3]     chr1 122503247-124785432      * |        23  GJ212202.1
#>     [4]     chr1 124785532-124849129      * |      1537  GJ211855.1
#>     [5]     chr1 124849229-124932724      * |       192  GJ211857.1
#>     ...      ...                 ...    ... .       ...         ...
#>   [105]    chr22   14419994-14420334      * |       695  GJ212209.1
#>   [106]    chr22   14420434-14421632      * |       695  GJ212186.2
#>   [107]    chr22   14421732-15054318      * |        10  GJ212191.2
#>   [108]     chrX   58605579-62412542      * |         1  GJ212192.1
#>   [109]     chrY   10316944-10544039      * |        10  GJ212193.1
#>   -------
#>   seqinfo: 24 sequences from hg38 genome

CUT&RUN excludable sets

Nordin et al. 2022 (Nordin et al. 2022) generated excludable regions for the CUT&RUN technology. They are available as Supplementary Material. We uniformly processed them and made available on Google Drive

# hg38 CUT&RUN exclusion set, BED
download.file("https://drive.google.com/uc?export=download&id=1rKIu7kdiEySTi-cq3nYxXJP4VQX1IPcS",
              destfile = "hg38.Nordin.CandRblacklist_hg38.bed")
# hg38 CUT&RUN exclusion set, RDS
download.file("https://drive.google.com/uc?export=download&id=1JuB1h-QQUw1mddBavI7CIuH7R-lwwczU",
              destfile = "hg38.Nordin.CandRblacklist_hg38.rds")
# And then load the GRanges object
mtx <- readRDS("hg38.Nordin.CandRblacklist_hg38.rds")
# mm10 CUT&RUN exclusion set, BED
download.file("https://drive.google.com/uc?export=download&id=1CRAojdphMbAzd3MnW_UmO1WtsDrHsrU1",
              destfile = "mm10.Nordin.CandRblacklist_mm10.bed")
# mm10 CUT&RUN exclusion set, RDS
download.file("https://drive.google.com/uc?export=download&id=1orPXLWUZ4-C4n_Jt2gH-WERLpY9Kn0t_",
              destfile = "mm10.Nordin.CandRblacklist_mm10.rds")

Summary table

Full summary table.

Name Ahub.IDs.BioC.3.16.and.above BEDbase.URL Description Filtered.Region.count
T2T.excluderanges AH107304 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions 3565
hg38.Kundaje.GRCh38_unified_Excludable AH107305 link Defined as a combination of hg38.Lareau.hg38_peaks, hg38.Boyle.hg38-Excludable.v2, and hg38.Wimberley.peakPass60Perc_sorted, followed by manual curation. Supersedes hg38.Kundaje.GRCh38.Excludable. 910
hg38.Bernstein.Mint_Excludable_GRCh38 AH107306 link Defined from Mint-ChIP (low input, multiplexed ChIP-seq) data 12052
hg38.Boyle.hg38-Excludable.v2 AH107307 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions 636
hg38.Kundaje.GRCh38.Excludable AH107308 link Defined by Anshul Kundaje as a part of ENCODE and modENCODE consortia 38
hg38.Lareau.hg38.full.Excludable AH107309 link ENCODE excludable regions combined with regions of high homology to mtDNA (NUMT regions) 820
hg38.Reddy.wgEncodeDacMapabilityConsensusExcludable.hg38 AH107310 link Defined by the ENCODE consortium, includes satellite repeats (CATTC, GAATG, GAGTG, ACRO1), RepeatMasker repeats (ALR/Alpha, BSR/Beta), centromeric repeats, chrM, High/Low mappability islands. Has extra chromosomes, use keepStandardChromosomes() filtering 396
hg38.Wimberley.peakPass60Perc_sorted AH107311 link Defined by the ewimberley/peakPass software 5078
hg38.Wold.hg38mitoExcludable AH107312 link Definition method unknown 299
hg38.Yeo.eCLIP_Excludableregions.hg38liftover.bed.fixed AH107313 link Defined from eCLIP data 56
hg38.Nordin.CandRblacklist_hg38 NA link Defined from CUT&RUN negative controls as 0.1% top significant SEACR peaks in over 30% of samples 885
hg19.Boyle.hg19-Excludable.v2 AH107314 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions 834
hg19.Bernstein.Mint_Excludable_hg19 AH107315 link Defined from Mint-ChIP (low input, multiplexed ChIP-seq) data 9035
hg19.Birney.wgEncodeDacMapabilityConsensusExcludable AH107316 link Defined by the ENCODE consortium, includes satellite repeats (CATTC, GAATG, GAGTG, ACRO1), RepeatMasker repeats (ALR/Alpha, BSR/Beta), centromeric repeats, chrM, High/Low mappability islands 411
hg19.Crawford.wgEncodeDukeMapabilityRegionsExcludable AH107317 link Defined by the ENCODE consortium, includes satellite repeats (CATTC, GAATG, GAGTG, ACRO1), RepeatMasker repeats (ALR/Alpha, BSR/Beta), human satellite repeat HSATII, chrM, ribosomal subunit consensus sequences LSU-rRNA_Hsa, SSU-rRNA_Hsa. Has extra chromosomes, use keepStandardChromosomes() filtering 1566
hg19.Lareau.hg19.full.Excludable AH107318 link ENCODE excludable regions combined with regions of high homology to mtDNA (NUMT regions) 902
hg19.Wold.hg19mitoExcludable AH107319 link Definition method unknown 295
hg19.Yeo.eCLIP_Excludableregions.hg19 AH107320 link Defined from eCLIP data, includes skyscraper, rRNA pseudogene, unreliably mapped satellite repeat, and low complexity skyscraper peak regions 57
mm39.excluderanges AH107321 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions 3147
mm10.Boyle.mm10-Excludable.v2 AH107322 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions 3435
mm10.Hardison.Excludable.full AH107323 link Definition method unknown 7865
mm10.Hardison.psuExcludable.mm10 AH107324 link Definition method unknown 5552
mm10.Kundaje.anshul.Excludable.mm10 AH107325 link Defined by Anshul Kundaje as a part of ENCODE and modENCODE consortia 3010
mm10.Kundaje.mm10.Excludable AH107326 link Defined by Anshul Kundaje as a part of ENCODE and modENCODE consortia 164
mm10.Lareau.mm10.full.Excludable AH107327 link ENCODE excludable regions combined with regions of high homology to mtDNA (NUMT regions) 523
mm10.Wold.mm10mitoExcludable AH107328 link Definition method unknown 123
mm10.Nordin.CandRblacklist_mm10 NA link Defined from CUT&RUN negative controls as 0.1% top significant SEACR peaks in over 30% of samples 559
mm9.Lareau.mm9.full.Excludable AH107329 link ENCODE excludable regions combined with regions of high homology to mtDNA (NUMT regions) 3415
mm9.Wold.mm9mitoExcludable AH107330 link Definition method unknown 123
ce11.Boyle.ce11-Excludable.v2 AH107331 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions 97
ce10.Boyle.ce10-Excludable.v2 AH107332 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions 100
ce10.Kundaje.ce10-Excludable AH107333 link Defined by Anshul Kundaje, superseded by ce10.Boyle.ce10-Excludable.v2 122
danRer10.Domingues.Excludableed AH107334 link Defined manually using total RNA-seq. 57
danRer10.Yang.Supplemental_Table_19.ChIP-seq_black_list_in_the_zebrafish_genome AH107335 link Defined via MACS2 peak calling using ChIP-seq (PMID: 33239788) 853
dm6.Boyle.dm6-Excludable.v2 AH107336 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions 182
dm3.Boyle.dm3-Excludable.v2 AH107337 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions 248
dm3.Kundaje.dm3-Excludable AH107338 link Defined by Anshul Kundaje. Contains heterochromatin chromosomes chr2LHet. Superseded by dm3.Boyle.dm3-Excludable.v2 306
TAIR10.Wimberley.predicted_excluded_list_sorted_0.6 AH107339 link Defined by the ewimberley/peakPass software 887
TAIR10.Klasfeld.arabidopsis_Excludable_20inputs AH107340 link Defined by the Boyle-Lab/Blacklist software, High Signal and Low Mappability regions (DOI: 10.1101/2022.02.27.482177) 83
TAIR10.Klasfeld.arabidopsis_greenscreen_20inputs AH107341 link Defined by the green screen pipeline (DOI: 10.1101/2022.02.27.482177) 36
T2T.Lareau.chm13v2.0_peaks AH107342 link Regions of high homology to mtDNA (NUMT regions) defined by caleblareau/mitoblacklist 817
hg38.Lareau.hg38_peaks AH107343 link Regions of high homology to mtDNA (NUMT regions) defined by caleblareau/mitoblacklist 784
hg19.Lareau.hg19_peaks AH107344 link Regions of high homology to mtDNA (NUMT regions) defined by caleblareau/mitoblacklist 779
mm10.Lareau.mm10_peaks AH107345 link Regions of high homology to mtDNA (NUMT regions) defined by caleblareau/mitoblacklist 387
mm9.Lareau.mm9_peaks AH107346 link Regions of high homology to mtDNA (NUMT regions) defined by caleblareau/mitoblacklist 395
hg19.UCSC.numtS AH107347 link Human NumtS mitochondrial sequence 766
mm9.UCSC.numtS AH107348 link Mouse NumtS mitochondrial sequence 172
T2T.CHM13.chm13.draft_v2.0.cen_mask AH107349 link Centromeric satellite masking bed file (v2.0) 23
T2T.CHM13.chm13.draft_v1.1.telomere AH107350 link Telomere identified by the VGP pipeline (v1.1) 48
T2T.UCSC.censat AH107351 link T2T peri/centromeric satellite annotation (v2.0, 20220329, CHM13 v2.0) 2523
T2T.UCSC.gap AH107352 link Locations of assembly gaps, as determine by strings of ‘N’ characters (v1.0) 5
T2T.UCSC.hgUnique.hg38 AH107353 link Regions unique to the T2T-CHM13 v2.0 assembly compared to the GRCh38/hg38 and GRCh37/hg19 reference assemblies 615
hg38.UCSC.centromere AH107354 link Gaps from centromeres 109
hg38.UCSC.telomere AH107355 link Gaps from telomeres 48
hg38.UCSC.short_arm AH107356 link Gaps on the short arm of the chromosome 5
hg38.UCSC.heterochromatin AH107357 link Gaps from large blocks of heterochromatin 11
hg38.UCSC.contig AH107358 link Gaps between contigs in scaffolds 285
hg38.UCSC.scaffold AH107359 link Gaps between scaffolds in chromosome assemblies. Has extra chromosomes, use keepStandardChromosomes() filtering 254
hg19.UCSC.centromere AH107360 link Gaps from centromeres 24
hg19.UCSC.telomere AH107361 link Gaps from telomeres 46
hg19.UCSC.short_arm AH107362 link Gaps on the short arm of the chromosome 5
hg19.UCSC.heterochromatin AH107363 link Gaps from large blocks of heterochromatin 12
hg19.UCSC.clone AH107364 link Gaps between clones in the same map contig. Has extra chromosomes, use keepStandardChromosomes() filtering 107
hg19.UCSC.contig AH107365 link Gaps between contigs in scaffolds 163
hg19.UCSC.scaffold AH107366 NA Gaps between scaffolds in chromosome assemblies. Only non-autosomal chromosomes 0
mm39.UCSC.centromere AH107367 link Gaps from centromeres 20
mm39.UCSC.telomere AH107368 link Gaps from telomeres 42
mm39.UCSC.short_arm AH107369 link Gaps on the short arm of the chromosome 21
mm39.UCSC.contig AH107370 link Gaps between contigs in scaffolds 60
mm39.UCSC.scaffold AH107371 link Gaps between scaffolds in chromosome assemblies 115
mm10.UCSC.centromere AH107372 link Gaps from centromeres 20
mm10.UCSC.telomere AH107373 link Gaps from telomeres 42
mm10.UCSC.short_arm AH107374 link Gaps on the short arm of the chromosome 21
mm10.UCSC.clone AH107375 link Gaps between clones in the same map contig. Has extra chromosomes, use keepStandardChromosomes() filtering 4
mm10.UCSC.contig AH107376 link Gaps between contigs in scaffolds 104
mm10.UCSC.scaffold AH107377 NA Gaps between scaffolds in chromosome assemblies 0
mm10.UCSC.other AH107378 link Sequence of Ns in the assembly that were not marked as gaps in the AGP (A Golden Path) assembly definition file. Has extra chromosomes, use keepStandardChromosomes() filtering 383
mm10.UCSC.fragment AH107379 NA A single gap of 31 bases in chrX_GL456233_random 0
mm9.UCSC.centromere AH107380 link Gaps from centromeres 21
mm9.UCSC.fragment AH107381 link Gaps between the contigs of a draft clone. (In this context, a contig is a set of overlapping sequence reads). Has extra chromosomes, use keepStandardChromosomes() filtering 436
mm9.UCSC.contig AH107382 link Gaps between contigs in scaffolds. Has extra chromosomes, use keepStandardChromosomes() filtering 105
danRer10.UCSC.contig AH107383 link Gaps between contigs in scaffolds 2338
danRer10.UCSC.scaffold AH107384 link Gaps between scaffolds in chromosome assemblies 16496
dm6.UCSC.other AH107385 link Sequence of Ns in the assembly that were not marked as gaps in the AGP (A Golden Path) assembly definition file 268
dm3.UCSC.contig NA link Gaps between contigs in scaffolds 7
dm3.UCSC.scaffold NA link Gaps between scaffolds in chromosome assemblies 1
TAIR10.UCSC.araTha1.gap NA link Gaps in the May 2011 Arabidopsis thaliana genome assembly 357

Citation

Below is the citation output from using citation('excluderanges') in R. Please run this yourself to check for any updates on how to cite excluderanges.

print(citation("excluderanges"), bibtex = TRUE)
#> 
#> To cite package 'excluderanges' in publications use:
#> 
#>   Dozmorov MG, Davis E, Mu W, Lee S, Triche T, Phanstiel D, Love M
#>   (2023). _excluderanges_.
#>   https://github.com/dozmorovlab/excluderanges/excluderanges - R
#>   package version 0.99.8,
#>   <https://github.com/dozmorovlab/excluderanges>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {excluderanges},
#>     author = {Mikhail G. Dozmorov and Eric Davis and Wancen Mu and Stuart Lee and Tim Triche and Douglas Phanstiel and Michael Love},
#>     year = {2023},
#>     url = {https://github.com/dozmorovlab/excluderanges},
#>     note = {https://github.com/dozmorovlab/excluderanges/excluderanges - R package version 0.99.8},
#>   }

Code of Conduct

Please note that the excluderanges project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

This package was developed using biocthis.

References

Amemiya, Haley M, Anshul Kundaje, and Alan P Boyle. 2019. “The ENCODE Blacklist: Identification of Problematic Regions of the Genome.” Sci Rep 9 (1): 9354. https://doi.org/10.1038/s41598-019-45839-z.
Klasfeld, Sammy, and Doris Wagner. 2022. “Greenscreen Decreases Type I Errors and Increases True Peak Detection in Genomic Datasets Including ChIP-seq.” bioRxiv.
Nordin, Anna, Gianluca Zambanini, Pierfrancesco Pagella, and Claudio Cantu. 2022. “The CUT&RUN Blacklist of Problematic Regions of the Genome.” bioRxiv.
Ogata, Jonathan D, Wancen Mu, Eric S Davis, Bingjie Xue, J Chuck Harrell, Nathan C Sheffield, Douglas H Phanstiel, Michael I Love, and Mikhail G Dozmorov. 2023. “Excluderanges: Exclusion Sets for T2T-CHM13, GRCm39, and Other Genome Assemblies.” Bioinformatics 39 (4). https://doi.org/10.1093/bioinformatics/btad198.
Wimberley, Charles E, and Steffen Heber. 2019. “PeakPass: Automating ChIP-Seq Blacklist Creation.” J Comput Biol, December. https://doi.org/10.1089/cmb.2019.0295.