Identification of Conserved Nocoding Elements (CNEs) from Axt alignment and visualisation of CNE density in horizon plot.
Conserved noncoding elements (CNEs) are pervasive class of elements clustering around genes with roles in development and differentiation in Metazoa (Woolfe et al. 2004). While many have been shown to act as long-range developmental enhancers (Sandelin et al. 2004), the source of their extreme conservation remains unexplained. To study the evolutionary dynamics of these elements and their relationship to the genes around which they cluster, it is essential to be able to produce genome-wide sets of these elements for a large number of species comparisons, each with multiple size and conservation thresholds.
This CNEr package aims to detect CNEs and visualise them along the genome. For performance reasons, the implementation of CNEs detection and corresponding I/O functions are primarily written as C extensions to R. We have used CNEr to produce sets of CNEs by scanning pairwise whole-genome net alignments with multiple reference species, each with two different window sizes and a range of minimum identity thresholds. Then, to pinpoint the boundaries of CNE arrays, we compute the CNE densities as the percentages of length covered by CNEs within a user specified window size. Finally, we describe a novel visualisation method using horizon plot tracks that shows a superior dynamic range to the standard density plots, simultaneously revealing CNE clusters characterized by vastly different levels of sequence conservation. Such CNE density plots generated using precise locations of CNEs can be used to identify genes involved in developmental regulation, even for novel genes that are not annotated yet.
This section will briefly demonstrate the pipeline of CNE identification and visualisation. More detailed usage of each step will be described in following sections with a concreate example of CNE identification and visualisation for Human (hg19) and Zebrafish (danRer7).
CNEr starts from axt net files of two genomes pairwise alignments and bed files for filtering. UCSC already provides a set of precomputed axt files on http://hgdownload.soe.ucsc.edu/downloads.html for most of popular genomes. In case the axt files are not available from UCSC, you can always generate the axt net files by following the UCSC wiki http://genomewiki.ucsc.edu/index.php/Whole_genome_alignment_howto.
So far, there is no suitable class to store the axt files in Bioconductor. Hence we created a S4 class Axt
to hold the content from axt files. Basically, it utilises GRanges
from GenomicRanges package and DNAStringSet
from Biostrings package.
To read axt file into R, CNEr provides readAxt
function for highly efficient reading, which heavily depends on Kent’s utilities source code (W J Kent et al. 2002). The axt file can be gzippped or in plain text file. The alignments between two genomes can also be in one file or in several files, such as chr1.hg19.mm10.net.axt.gz, chr2.hg19.mm10.net.axt.gz, etc.
library(CNEr)
axtFilesHg19DanRer7 <- file.path(system.file("extdata", package="CNEr"),
"hg19.danRer7.net.axt")
axtFilesDanRer7Hg19 <- file.path(system.file("extdata", package="CNEr"),
"danRer7.hg19.net.axt")
axtHg19DanRer7 <- readAxt(axtFilesHg19DanRer7)
axtDanRer7Hg19 <- readAxt(axtFilesDanRer7Hg19)
axtHg19DanRer7
## A Axt with 133 alignment pairs:
## 1 chr11 31082021 31082458 chr25 15030563 15031009 + 246
## ATTTTATGTGCAAGATAGACTTTAAAATTTAAATAC...AGATATTAAAGCACATTTTAATTGAAAAAAAGTCAA
## ATTTAAAGTGATAGTTTGCCCCAAAATTTTAATTTA...AAAAATAAAACCATAATTCAACTAACAGACATTAAA
## 2 chr11 31082562 31082862 chr25 15036393 15036688 + 4422
## GGGAAGGAAGGGCTGGTTAGGAGTACAGTAGCTAGC...GACTCCTTTGGATAAACTAATGTAAACAAACACATT
## GGAAAAGGGGGGC-------GAGGACGAGAGC--GT...CACCTCATCTGATTGGCTTGTAGCAACAACTCCATT
## 3 chr11 31085908 31086035 chr7 60364309 60364442 - 5679
## GGGCTTTTGAGATATTTCAATTATCTTCAAGAAATC...TCTGTCAGTACCCTACCTCTGAGCAGAAGCCTTAGG
## GGGCTCTGGAGGTATTTCAGAAAAATCAGAGAAATC...CCTGTCAACGTGGAACCTTAGAGCGAGAGCCTAGGG
## 4 chr11 31087045 31087145 chr25 15037012 15037110 + 1743
## TTGTGTAGTTGCTGTATTGTTTGGAAGTATTCAATT...ATGAATGAAAGAGCTGCATTAAGCATAGATAAGATA
## TTAAATAATTGCTTGATTATTTTGGACTGTTCA--C...ATGA--GAAAGCTTAATGTTATTGTTACATAAATTA
## 5 chr11 31090791 31090882 chr7 60364679 60364769 - 1556
## TATTCAGATAATTTTGGAACGAGATTTTTGAATCAC...GCAAAAAGAAGTTTAATCCAAAAAAAATTAGGATTT
## TATTTTAATGATTGTAATA-GTCATTTTTGAAAGAA...ACAAAAACAAAATGAATGTATAATACAATATAATTT
## ... ... ... ... ... ... ... ...
## 129 chr11 32449499 32449619 chr25 15559655 15559778 + 6263
## TACCTGCTGTAGGGCGTCCTCAGCAGCAAAGCCTGG...CCGAGTACTGCTGCTCACCTG---CAGAGAGAACCG
## TACCTGTTGTATGGGTTTCTCAGCAGCAGAGCTTGA...CGGGATACTGCTGATCCCCTGAAACAAACAGAACAG
## 130 chr11 32450037 32450174 chr25 15564924 15565061 + 6952
## GCTTACCCAGCGAGCCCTGCTGGCCCATGGGATCCT...TCCCGTCGAAGGTGACCGTGCTGTAACCTGCGGGAG
## GTTTACCCATGTTGCTCTGCTGTGTGATTGAGTCCT...CGCCGTCAAAAGCAACCGTGCCGTAACCTATAAAAG
## 131 chr11 32456239 32456689 chr25 15570666 15571071 + 13604
## TACTTACCCTGATTGCGAATAGCGGGCTGGCTCTCG...GCAGCGCGTTCAGGTCCCGCACGTCGGAGCCCATTT
## TCCTCACCTTGATTCCTGGAGCTGGATTGGCTGTCC...GAAGAGTGTTGAGGTCACGAACATCAGAACCCATTT
## 132 chr11 32456981 32457152 chr25 15571250 15571407 + 1488
## GAGTAGGTGGGAGGGAGGGCGGGAAGTGGGGGAGCG...GAGGGGCCGGCGGCGCGGGGAAGAGGAGGAGCCAGG
## GAATTGGAGAAAAGAAGAACAGGA------GGATCC...AGTCAGTCCGAGGAGTCGGGAGGAGGAGGAGTCAGG
## 133 chr11 32461267 32461582 chr25 15574876 15575192 + 6228
## GTCCCCGAGCTCCCCGAGAGTCCTCAGTGAAAGGAT...GCAAACAACAGGGGAAAAAAATGGGGAAAAGAAAAA
## GTCACTGTGATCTCAGAAGGTC--------AGGAAA...TCAACTCGGGGAAGAGGAGAGCGAGGAAAAGAAAAA
axtDanRer7Hg19
## A Axt with 190 alignment pairs:
## 1 chr12 46824249 46824342 chr11 31823296 31823389 + -311
## GGAA---CACGTGTAAATCTG------ATTTTCATA...CAACAGATGCGTTGTAACAAAGCCATGAAAATGTTT
## AGAATTTTACTCACACATCCGTTGGACACCTGCATA...AA---------TTTCAAGACAAAAATAAAATTGTTT
## 2 chr18 41195293 41195862 chr11 103744646 103745211 - 3029
## CATCATATTACAGTGCAAAATAACATAAAATATACT...GAATAAATTGCATC--ATATTGAAATACAAAATAAA
## CATAATAATACAATG--AAAGAAAGAAAAATCCAAA...-AATATATTTTATTAAATATTAAATTATGAACAAAA
## 3 chr18 44008076 44008197 chr11 32410604 32410725 + 7229
## TCAGACGGTGGGCTGCAGTTTGCTCATGTTTCTCTG...GACAGCTCGGCCAGCGGCACGTAAACGGCTTCTCAC
## TCAAAGCGCCAGCTGGAGTTTGGTCATGTTTCTCTG...GACAACTTGGCCACCGACAGCTGAAGGGCTTTTCAC
## 4 chr18 44008198 44008391 chr11 32413401 32413612 + 6896
## CTGAGACAACACACACACACACATTTTAATCAAGTC...CTCTGACACGTCTCACACTGAAAGGGTTTGACGCCT
## CTGAGTCTAAAC---CTTAGAACTTTTACACTAGTC...CGCTGACAAGTTTTACACTGGAATGGTTTCACACCT
## 5 chr18 44009282 44009389 chr11 32414210 32414318 + 4405
## ACCTGTGTGTCTGCGCTGGTGTCTCTTCAGCTGGTC...CACTGATACGGCTTTTC-CCCTGAACACCACACACA
## ACCTGTATGTCTCCTTTGGTGTCTTTTGAGCTGGTC...CACTGGTATGGTTTCTCACCTTGGGGAAGACACATA
## ... ... ... ... ... ... ... ...
## 186 chr7 16881835 16881957 chr11 103785288 103785409 - 6493
## CTTTTTCACAAGCCATTAGCTCCTGGAGCAACATCA...CAAAAGTCAGAGAAGTTGCTCTGAGCTGCAGGGAGG
## CTTTTTCACAATCCATTAATTCCTGGATCAACATCA...CAAAAGTCAGAGACATT-CTCTGAGCAGCAGGGAAG
## 187 chr7 16911295 16911385 chr11 103915635 103915726 - 1556
## AAATTATATTGTATTATACATTCATTTTGTTTTTGT...TTCTTTCAAAAATGAC-TATTACAATCATTAAAATA
## AAATCCTAATTTTTTTTGGATTAAACTTCTTTTTGC...GTGATTCAAAAATCTCGTTCCAAAATTATCTGAATA
## 188 chr7 16911622 16911755 chr11 103920482 103920609 - 5679
## CCCTAGGCTCTCGCTCTAAGGTTCCACGTTGACAGG...GATTTCTCTGATTTTTCTGAAATACCTCCAGAGCCC
## CCTAAGGCTTCTGCTCAGAGGTAGGGTACTGACAGA...GATTTCTTGAAGATAATTGAAATATCTCAAAAGCCC
## 189 chr7 16918825 16918945 chr11 103923739 103923855 - 5401
## CCAGCGCTGCGCACTGACCTGGCT-GTCGCTCACTG...TGTGACAAGCCGTCCCAGCCCGCAGCACATCTGTGT
## CCAACAAAGCATGCTGACCCAGCTTGTCACCCAGTG...TCTGACAGTTTGCCTTTG-----GGCACATCTGTGT
## 190 chr7 27639642 27639730 chr11 102899904 102899999 - 642
## CTTTTCTTTTAATTGAATTATATT---TAAGATTAA...GATTTAAA--AGTTAAGATCAAGTTCCCAGAGTCCT
## ACTTTCTTTTGTTAGATATTCATTCTGTTAGGTCTA...GACAAAGATCACTTAAGAAAGAGGTCCCCCAGTCTT
The gene annotation information, including exons coordinates, repeats, is used to filter out the undesired regions. Here we summarise a table of filtering information we used:
Assembly | Name | Exon | Repeat |
---|---|---|---|
hg38 | Human | RefSeq Genes, Ensembl Genes, UCSC Known Genes | RepeatMasker |
hg19 | Human | RefSeq Genes, Ensembl Genes, UCSC Known Genes | RepeatMasker |
mm10 | Mouse | RefSeq Genes, Ensembl Genes, UCSC Known Genes | RepeatMasker |
xenTro3 | Frog | RefSeq Genes, Ensembl Genes | RepeatMasker |
tetNig2 | Tetraodon | Ensembl Genes | |
canFam3 | Dog | RefSeq Genes, Ensembl Genes | RepeatMasker |
galGal4 | Chicken | RefSeq Genes, Ensembl Genes | RepeatMasker |
danRer10 | Zebrafish | RefSeq Genes, Ensembl Genes | RepeatMasker |
danRer7 | Zebrafish | RefSeq Genes, Ensembl Genes | RepeatMasker |
fr3 | Fugu | RefSeq Genes | RepeatMasker |
anoCar2 | Lizard | Ensembl Genes | RepeatMasker |
equCab2 | Horse | RefSeq Genes, Ensembl Genes | RepeatMasker |
oryLat2 | Medaka | RefSeq Genes, Ensembl Genes | RepeatMasker |
monDom5 | Opossum | RefSeq Genes, Ensembl Genes | RepeatMasker |
gasAcu1 | Stickleback | RefSeq Genes, Ensembl Genes | RepeatMasker |
rn5 | Rat | RefSeq Genes, Ensembl Genes | RepeatMasker |
dm3 | D. melanogaster | RefSeq Genes, Ensembl Genes | RepeatMasker |
droAna2 | D. ananassae | RepeatMasker | |
dp3 | D. pseudoobscura | RepeatMasker | |
ce4 | C. elegans | RefSeq Genes | RepeatMasker |
cb3 | C. briggsae | RepeatMasker | |
caeRem2 | C. remanei | RepeatMasker | |
caePb1 | C. brenneri | RepeatMasker |
For the sake of simplicity, all the information listed above can be fetched easily with Bioconductor package rtracklayer, biomaRt and precompiled Bioconductor annotation packages. A few examples are given here:
## To fetch rmak table from UCSC
library(rtracklayer)
mySession <- browserSession("UCSC")
genome(mySession) <- "hg19"
hg19.rmsk <- getTable(ucscTableQuery(mySession, track="RepeatMasker",
table="rmsk"))
hg19.rmskGRanges <- GRanges(seqnames=hg19.rmsk$genoName,
## The UCSC coordinate is 0-based.
ranges=IRanges(start=hg19.rmsk$genoStart+1,
end=hg19.rmsk$genoEnd),
strand=hg19.rmsk$strand)
## To fetch ensembl gene exons from BioMart
library(biomaRt)
ensembl <- useMart("ensembl")
ensembl <- useDataset("hsapiens_gene_ensembl",mart=ensembl)
attributes <- listAttributes(ensembl)
exons <- getBM(attributes=c("chromosome_name", "exon_chrom_start", "exon_chrom_end", "strand"),
mart = ensembl)
exonsRanges <- GRanges(seqnames=exons$chromosome_name,
ranges=IRanges(start=exons$exon_chrom_start,
end=exons$exon_chrom_end),
strand=ifelse(exons$strand==1L, "+", "-")
)
## Use the existing Bioconductor annotation package
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
exonsRanges <- exons(TxDb.Hsapiens.UCSC.hg19.knownGene)
Of course, more customised gene annotation can be included in the bed file. To import bed file into GRanges in R
, rtracklayer provides a general function import.bed
to do that. However, if your bed file only contains 3 columns: chromosome, start and end, that are the information needed by CNEr, then it is better to use the readBed
function that is much faster with C implementation.
bedHg19Fn <- file.path(system.file("extdata", package="CNEr"),
"filter_regions.hg19.bed")
bedHg19 <- readBed(bedHg19Fn)
## Reading /tmp/RtmpcaONVN/Rinst22c58f4320d/CNEr/extdata/filter_regions.hg19.bed
bedHg19
## GRanges object with 5574 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr11 [30000001, 30000106] +
## [2] chr11 [30000126, 30000274] +
## [3] chr11 [30001660, 30003939] +
## [4] chr11 [30003969, 30004060] +
## [5] chr11 [30004481, 30004844] +
## ... ... ... ...
## [5570] chr11 [32995613, 32995652] +
## [5571] chr11 [32995685, 32995987] +
## [5572] chr11 [32996154, 32996292] +
## [5573] chr11 [32996611, 32996640] +
## [5574] chr11 [32996794, 32997176] +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
bedDanRer7Fn <- file.path(system.file("extdata", package="CNEr"),
"filter_regions.danRer7.bed")
bedDanRer7 <- readBed(bedDanRer7Fn)
## Reading /tmp/RtmpcaONVN/Rinst22c58f4320d/CNEr/extdata/filter_regions.danRer7.bed
bedDanRer7
## GRanges object with 5000 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 [ 38, 194] +
## [2] chr1 [1101, 1123] +
## [3] chr1 [1254, 1302] +
## [4] chr1 [1473, 1508] +
## [5] chr1 [1782, 2105] +
## ... ... ... ...
## [4996] chr1 [1720306, 1720403] +
## [4997] chr1 [1720405, 1720433] +
## [4998] chr1 [1720436, 1720784] +
## [4999] chr1 [1720785, 1720988] +
## [5000] chr1 [1721374, 1721394] +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
The chromosome size of the query assembly is necessary when the filters for query assembly is provided.
To facilitate the fetch of chromosome sizes, we prepared a function fetchChromSizes
to automate the procedure. Currently the assemblies from UCSC are supported, and more assemblies from other sources will be implemented in the future. A object of Seqinfo
is returned which suits the input to downstream analysis.
qSizesHg19 <- fetchChromSizes("hg19")
qSizesDanRer7 <- fetchChromSizes("danRer7")
qSizesHg19
## Seqinfo object with 93 sequences from hg19 genome:
## seqnames seqlengths isCircular genome
## chr1 249250621 <NA> hg19
## chr2 243199373 <NA> hg19
## chr3 198022430 <NA> hg19
## chr4 191154276 <NA> hg19
## chr5 180915260 <NA> hg19
## ... ... ... ...
## chrUn_gl000231 27386 <NA> hg19
## chrUn_gl000229 19913 <NA> hg19
## chrM 16571 <NA> hg19
## chrUn_gl000226 15008 <NA> hg19
## chr18_gl000207_random 4262 <NA> hg19
qSizesDanRer7
## Seqinfo object with 1133 sequences from danRer7 genome:
## seqnames seqlengths isCircular genome
## chr7 77276063 <NA> danRer7
## chr5 75682077 <NA> danRer7
## chr3 63268876 <NA> danRer7
## chr4 62094675 <NA> danRer7
## chr1 60348388 <NA> danRer7
## ... ... ... ...
## Zv9_NA959 1142 <NA> danRer7
## Zv9_NA937 1137 <NA> danRer7
## Zv9_NA802 1126 <NA> danRer7
## Zv9_NA1000 942 <NA> danRer7
## Zv9_NA997 650 <NA> danRer7
Seqinfo
can also be generated from local two bit file with seqinfo
from rtracklayer.
In this section, we will go through the details of CNE identification.
Detecting CNEs highly relies on the whole-genome pairwise net alignments. To correct the bias of a chosen genome and capture the duplicated CNEs during genome evolution, we scan two sets of nets for each pairwise comparison, one as reference from each of the genomes.
We identify CNEs by scanning the alignments for regions with at least I identities over C alignment columns. Because different genes and loci may favor various similarity scores, we usually scan at two diffrent window sizes 30 and 50 with several similarity criterias (I/C) range from 70% to 100%.
## axt, GRanges objects as input
CNEHg19DanRer7 <- ceScan(axts=axtHg19DanRer7, tFilter=bedHg19,
qFilter=bedDanRer7, qSizes=qSizesDanRer7,
thresholds=c("45_50", "48_50", "49_50"))
CNEDanRer7Hg19 <- ceScan(axts=axtDanRer7Hg19, tFilter=bedDanRer7,
qFilter=bedHg19, qSizes=qSizesHg19,
thresholds=c("45_50", "48_50", "49_50"))
## axt and bed files as input
CNEHg19DanRer7 <- ceScan(axts=axtFilesHg19DanRer7, tFilter=bedHg19Fn,
qFilter=bedDanRer7Fn, qSizes=qSizesDanRer7,
thresholds=c("45_50", "48_50", "49_50"))
CNEDanRer7Hg19 <- ceScan(axts=axtFilesDanRer7Hg19, tFilter=bedDanRer7Fn,
qFilter=bedHg19Fn, qSizes=qSizesHg19,
thresholds=c("45_50", "48_50", "49_50"))
At this stage, a list of data.frame
is returned from ceScan
, which contains the preliminary CNEs. Here is some exemple output:
lapply(CNEHg19DanRer7, head)
## $`45_50`
## tName tStart tEnd qName qStart qEnd strand score
## 1 chr11 31085937 31085991 chr7 16911672 16911726 - 92.73
## 2 chr11 31182676 31182730 chr25 15074418 15074472 + 89.09
## 3 chr11 31182806 31182932 chr25 15074676 15074804 + 89.15
## 4 chr11 31257974 31258025 chr25 15098901 15098951 + 90.38
## 5 chr11 31263401 31263511 chr25 15100713 15100823 + 92.79
## 6 chr11 31623054 31623103 chr25 15188234 15188283 + 90.00
## cigar
## 1 55M
## 2 55M
## 3 81M1D35M1D11M
## 4 32M1I19M
## 5 111M
## 6 50M
##
## $`48_50`
## tName tStart tEnd qName qStart qEnd strand score cigar
## 1 chr11 31263427 31263497 chr25 15100739 15100809 + 95.77 71M
## 2 chr11 31685844 31685902 chr25 15243641 15243699 + 96.61 59M
## 3 chr11 31712869 31712922 chr25 15247368 15247421 + 96.30 54M
## 4 chr11 31734350 31734412 chr25 15252105 15252167 + 96.83 63M
## 5 chr11 31785866 31785928 chr25 15255878 15255940 + 96.83 63M
## 6 chr11 31785931 31785985 chr25 15255943 15255997 + 96.36 55M
##
## $`49_50`
## tName tStart tEnd qName qStart qEnd strand score cigar
## 1 chr11 31785866 31785928 chr25 15255878 15255940 + 96.83 63M
## 2 chr11 32053036 32053091 chr25 15333377 15333432 + 98.21 56M
In the result table, even though the strand for query element can be negative, the coordiante for that query element is already on the positive strand.
Because we do two rounds of CNE detections with each genome as reference, some conserved elements overlap on both genomes and are supposed to be removed. But elements that only overlap on one of the genomes are kept, so that duplicated elements remain distinct.
cneMergedDanRer7Hg19 <- mapply(cneMerge, CNEDanRer7Hg19, CNEHg19DanRer7,
SIMPLIFY=FALSE)
lapply(cneMergedDanRer7Hg19, head)
## $`45_50`
## chr1 start1 end1 chr2 start2 end2 strand similarity
## 19 chr25 15381678 15381819 chr11 32221854 32221994 + 89.44
## 20 chr25 15397394 15397480 chr11 31821057 31821143 + 89.66
## 21 chr25 15403140 15403217 chr11 31829491 31829568 + 92.31
## 22 chr25 15415208 15415296 chr11 31848222 31848310 + 89.89
## 23 chr25 15418715 15418765 chr11 31989683 31989732 + 90.20
## 24 chr25 15432800 15433014 chr11 32053010 32053222 + 91.63
## cigar
## 19 114M1I27M
## 20 87M
## 21 78M
## 22 89M
## 23 22M1I28M
## 24 82M1I32M1I99M
##
## $`48_50`
## chr1 start1 end1 chr2 start2 end2 strand similarity
## 11 chr25 15381700 15381752 chr11 32221876 32221928 + 96.23
## 12 chr25 15403140 15403193 chr11 31829491 31829544 + 96.30
## 13 chr25 15432826 15432883 chr11 32053036 32053092 + 96.55
## 14 chr25 15432887 15432970 chr11 32053096 32053178 + 96.43
## 15 chr25 15459924 15459973 chr11 32198103 32198152 + 96.00
## 17 chr25 15100739 15100809 chr11 31263427 31263497 + 95.77
## cigar
## 11 53M
## 12 54M
## 13 56M1I1M
## 14 28M1I55M
## 15 50M
## 17 71M
##
## $`49_50`
## chr1 start1 end1 chr2 start2 end2 strand similarity cigar
## 3 chr25 15432826 15432881 chr11 32053036 32053091 + 98.21 56M
## 4 chr25 15255878 15255940 chr11 31785866 31785928 + 96.83 63M
## 5 chr25 15333377 15333432 chr11 32053036 32053091 + 98.21 56M
Some CNEs might be unannotated repeats. To remove them, currently we use blat (W James Kent 2002) to realign each sequence of CNEs against the respective genomes. When the number of matches exceed certain threshold, for instance, 8, that CNE will be discarded.
This step can be very time-consuming when the number of CNEs are large. Other alignment method can also be considerd, such as Bowtie2, BWA. The two bit file for each assembly is required.
assemblyHg19Twobit <- "/Users/gtan/CSC/CNEr/2bit/hg19.2bit"
assemblyDanRer7Twobit <- "/Users/gtan/CSC/CNEr/2bit/danRer7.2bit"
cneBlatedDanRer7Hg19 <- list()
for(i in 1:length(cneMergedDanRer7Hg19)){
cneBlatedDanRer7Hg19[[names(cneMergedDanRer7Hg19)[i]]] <-
blatCNE(cneMergedDanRer7Hg19[[i]],
as.integer(sub(".+_.+_\\d+_", "", names(cneMergedDanRer7Hg19)[i])),
cutoffs1=8L, cutoffs2=8L,
assembly1Twobit=assemblyDanRer7Twobit,
assembly2Twobit=assemblyHg19Twobit,
blatBinary="blat")
}
Now at this stage, these elements are the final CNEs. We also prepare a one step function ceScanOneStep
and it returns a CNE
object directly which wrapps all the necessary information. This one-step function is highly recommended to avoid the tedious steps above.
assemblyHg19Twobit <- "/Users/gtan/CSC/CNEr/2bit/hg19.2bit"
assemblyDanRer7Twobit <- "/Users/gtan/CSC/CNEr/2bit/danRer7.2bit"
finalCNE <- ceScanOneStep(axt1=axtHg19DanRer7, filter1=bedHg19,
sizes1=qSizesHg19, assembly1="hg19",
twoBit1=assemblyHg19Twobit,
axt2=axtDanRer7Hg19, filter2=bedDanRer7,
sizes2=qSizesDanRer7, assembly2="danRer7",
twoBit2=assemblyDanRer7Twobit,
thresholds=c("45_50", "48_50", "49_50"),
blatBinary="blat", blatCutoff1=8L, blatCutoff2=8L)
As the computation of CNEs from the whole pipeline and the preparation of annotation package can be very time-consuming, for a smoother visualisation experience, we decided to use a local SQLite database to store these information.
Since the CNEs data.frame
is just a table and can be imported into a SQL table naturally. To speed up the query from the SQL database, the bin indexing system is acquired. For more information, please refer to the paper (W J Kent et al. 2002) and genomewiki.
## on individual tables
dbName <- tempfile()
data(cneBlatedDanRer7Hg19)
for(i in 1:length(cneBlatedDanRer7Hg19)){
tableName <- paste("danRer7_hg19", names(cneBlatedDanRer7Hg19)[i],
sep="_")
saveCNEToSQLite(cneBlatedDanRer7Hg19[[i]], dbName, tableName, overwrite=TRUE)
}
## on CNE class
data(finalCNE)
saveCNEToSQLite(finalCNE, dbName=dbName, overwrite=TRUE)
When querying results from the local SQLite database based on the chr, coordinates and other criterias, a IRanges
object is returned.
chr <- "chr11"
start <- 31000000L
end <- 33000000L
minLength <- 50L
tableName <- "danRer7_hg19_45_50"
fetchedCNERanges <- readCNERangesFromSQLite(dbName, tableName, chr,
start, end, whichAssembly="L",
minLength=minLength)
To visualise the CNEs together with other gene annotations, we choose to use the Bioconductor package Gviz in this vignette. Gviz, based on the grid graphics scheme, is a very powerful package for plotting data and annotation information along genomic coordinates. The functionality of integrating publicly available genome annotation data, such as UCSC or Ensembl, significantly reduced the burden of preparing annotations for common assemblies. Since the Bioconductor release 2.13 of Gviz, it provides the data track in horizon plot, which exactly meets our needs for visualisation of CNEs density plots. For more detailed usage, please check the vignette or manual of Gviz.
Another option for visualisation is the package ggbio, which is based on ggplot2. The advantage of ggbio is the simplicity of adding any customised ggplot2 style track into the plot without tuning the coordinate systems. The densities generated in the following section can be easily plot in the horizon plot. A short straightforward tutorial regarding horizon plot in ggplot2
format is available from http://timelyportfolio.blogspot.co.uk/2012/08/horizon-on-ggplot2.html.
For the example of hg19 vs danRer7 in this vignette, we choose hg19 as the reference and show the range of developmental gene PAX6.
library(Gviz)
genome <- "hg19"
chr <- "chr11"
start <- 31000000L
end <- 33000000L
axisTrack <- GenomeAxisTrack()
ideoTrack <- IdeogramTrack(genome=genome, chromosome=chr)
cpgIslands <- UcscTrack(genome=genome, chromosome=chr,
track="cpgIslandExt", from=start, to=end,
trackType="AnnotationTrack", start="chromStart",
end="chromEnd", id="name", shape="box",
fill="#006400", name="CpG Islands")
refGenes <- UcscTrack(genome="hg19", chromosome=chr,
track="refGene", from=start, to=end,
trackType="GeneRegionTrack", rstarts="exonStarts",
rends="exonEnds", gene="name2", symbol="name2",
transcript="name", strand="strand", fill="#8282d2",
name="refSeq Genes", collapseTranscripts=TRUE)
biomTrack <- BiomartGeneRegionTrack(genome="hg19", chromosome=chr,
start=start , end=end, name="Ensembl")
library(Gviz)
plotTracks(list(axisTrack, ideoTrack, cpgIslands, refGenes),
collapseTranscripts=TRUE, shape="arrow",
showId=TRUE, transcriptAnnotation="symbol")
It is also possible to plot the annotation from an ordinary R
object, such as data.frame
, GRanges
, IRanges
or even from a local file. Usually the gff file containing the gene annotation can be processed by Gviz directly. For more details, please look into the vignette of Gviz.
dbName <- file.path(system.file("extdata", package="CNEr"),
"cne.sqlite")
windowSize <- 300L
minLength <- 50L
cneHg19DanRer7_45_50 <-
CNEDensity(dbName=dbName,
tableName="danRer7_hg19_45_50",
assembly1="hg19", chr=chr, start=start,
end=end, windowSize=windowSize,
minLength=minLength)
cneHg19DanRer7_48_50 <-
CNEDensity(dbName=dbName,
tableName="danRer7_hg19_48_50",
assembly1="hg19", chr=chr, start=start,
end=end, windowSize=windowSize,
minLength=minLength)
cneHg19DanRer7_49_50 <-
CNEDensity(dbName=dbName,
tableName="danRer7_hg19_49_50",
assembly1="hg19", chr=chr, start=start,
end=end, windowSize=windowSize,
minLength=minLength)
data(cneHg19DanRer7_45_50)
data(cneHg19DanRer7_48_50)
data(cneHg19DanRer7_49_50)
genome <- "hg19"
chr <- "chr11"
start <- 31000000L
end <- 33000000L
#axisTrack = GenomeAxisTrack()
#ideoTrack = IdeogramTrack(genome=genome, chromosome=chr)
strand <- "+"
dataMatrix <- cneHg19DanRer7_45_50
dTrack1 <- DataTrack(start=dataMatrix[ ,1], end=dataMatrix[ ,1],
data=dataMatrix[ ,2], chromosome=chr, strand=strand,
genome=genome, type="horiz", horizon.scale=0.1,
fill.horizon=c("#B41414", "#E03231", "#F7A99C",
"yellow", "orange", "red"),
name="danRer7 45/50")
dataMatrix <- cneHg19DanRer7_48_50
dTrack2 <- DataTrack(start=dataMatrix[ ,1], end=dataMatrix[ ,1],
data=dataMatrix[ ,2], chromosome=chr, strand=strand,
genome=genome, type="horiz", horizon.scale=0.1,
fill.horizon=c("#B41414", "#E03231", "#F7A99C",
"yellow", "orange", "red"),
name="danRer7 48/50")
dataMatrix <- cneHg19DanRer7_49_50
dTrack3 <- DataTrack(start=dataMatrix[ ,1], end=dataMatrix[ ,1],
data=dataMatrix[ ,2], chromosome=chr, strand=strand,
genome=genome, type="horiz", horizon.scale=0.1,
fill.horizon=c("#B41414", "#E03231", "#F7A99C",
"yellow", "orange", "red"),
name="danRer7 49/50")
plotTracks(list(axisTrack, ideoTrack, cpgIslands,
refGenes,
dTrack1, dTrack2, dTrack3),
collapseTranscripts=TRUE, shape="arrow",
showId=TRUE, transcriptAnnotation="symbol")
From this horizon plot compared with Zebrafish with Human as reference genome, the developmental gene PAX6 was surrounded by the density peaks of CNEs.
With this package, we are able to identify CNEs efficiently and handle the corresponding objects conveniently in R. Horizon plot shows a superior dynamic range to the standard density plots, simultaneously revealing CNE clusters characterized by vastly different levels of sequence conservation. Such CNE density plots generated using precise locations of CNEs can be used to identify genes involved in developmental regulation, even for novel genes that are not yet annotated.
The following is the session info that generated this vignette:
sessionInfo()
## R version 3.2.3 (2015-12-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.3 LTS
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] Gviz_1.14.0 GenomicRanges_1.22.2 GenomeInfoDb_1.6.1
## [4] IRanges_2.4.6 S4Vectors_0.8.5 BiocGenerics_0.16.1
## [7] CNEr_1.6.2 BiocStyle_1.8.0
##
## loaded via a namespace (and not attached):
## [1] SummarizedExperiment_1.0.1 VariantAnnotation_1.16.4
## [3] reshape2_1.4.1 splines_3.2.3
## [5] lattice_0.20-33 colorspace_1.2-6
## [7] htmltools_0.2.6 GenomicFeatures_1.22.6
## [9] rtracklayer_1.30.1 yaml_2.1.13
## [11] XML_3.98-1.3 survival_2.38-3
## [13] foreign_0.8-66 DBI_0.3.1
## [15] BiocParallel_1.4.3 RColorBrewer_1.1-2
## [17] lambda.r_1.1.7 matrixStats_0.50.1
## [19] plyr_1.8.3 stringr_1.0.0
## [21] zlibbioc_1.16.0 Biostrings_2.38.2
## [23] munsell_0.4.2 gtable_0.1.2
## [25] futile.logger_1.4.1 evaluate_0.8
## [27] latticeExtra_0.6-26 Biobase_2.30.0
## [29] knitr_1.11 biomaRt_2.26.1
## [31] AnnotationDbi_1.32.2 proto_0.3-10
## [33] Rcpp_0.12.2 acepack_1.3-3.3
## [35] BSgenome_1.38.0 scales_0.3.0
## [37] formatR_1.2.1 Hmisc_3.17-0
## [39] XVector_0.10.0 Rsamtools_1.22.0
## [41] gridExtra_2.0.0 ggplot2_1.0.1
## [43] digest_0.6.8 stringi_1.0-1
## [45] biovizBase_1.18.0 tools_3.2.3
## [47] bitops_1.0-6 magrittr_1.5
## [49] RCurl_1.95-4.7 RSQLite_1.0.0
## [51] dichromat_2.0-0 Formula_1.2-1
## [53] cluster_2.0.3 futile.options_1.0.0
## [55] MASS_7.3-45 rmarkdown_0.8.1
## [57] rpart_4.1-10 GenomicAlignments_1.6.1
## [59] nnet_7.3-11
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Kent, W James. 2002. “BLAT–the BLAST-like alignment tool.” Genome Research 12 (4): 656–64.
Sandelin, Albin, Peter Bailey, Sara Bruce, Pär G Engström, Joanna M Klos, Wyeth W Wasserman, Johan Ericson, and Boris Lenhard. 2004. “Arrays of ultraconserved non-coding regions span the loci of key developmental genes in vertebrate genomes.” BMC Genomics 5 (1): 99.
Woolfe, Adam, Martin Goodson, Debbie K Goode, Phil Snell, Gayle K McEwen, Tanya Vavouri, Sarah F Smith, Phil North, Heather Callaway, and Krys Kelly. 2004. “Highly conserved non-coding sequences are associated with vertebrate development.” PLoS Biology 3 (1): e7.