\documentclass{article} %\VignetteIndexEntry{CNEr} \usepackage[usenames,dvipsnames]{color} \usepackage[colorlinks=true, linkcolor=Blue, urlcolor=Blue, citecolor=Blue]{hyperref} \usepackage[round]{natbib} \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rmethod}[1]{{\texttt{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \newcommand{\Rcode}[1]{{\texttt{#1}}} \newcommand{\software}[1]{\textsf{#1}} \newcommand{\R}{\software{R}} \newcommand{\IRanges}{\Rpackage{IRanges}} \usepackage{amsmath} \title{The \textbf{CNEr} package overview} \author{Ge Tan} \begin{document} \SweaveOpts{concordance=TRUE} \maketitle <>= options(width=70) @ \tableofcontents \section{Introduction} Conserved noncoding elements (CNEs) are pervasive class of elements clustering around genes with roles in development and differentiation in Metazoa \citep{Woolfe:2004ur}. While many have been shown to act as long-range developmental enhancers \citep{Sandelin:2004bd}, 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 \Rpackage{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. \section{Pipeline of the package} 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). \subsection{CNE identification} \begin{enumerate} \item axtNets: The axtNet files can be downloaded from UCSC or generated by itself \item scan alignments: The regions with minimal \textbf{I} identities over \textbf{C} columns. \item remove elements: Elements that overlap with annotated exons/repeats. \item merge elements to get CNEs: Elements that overlap on both genomes. \end{enumerate} \subsection{CNE visualisation} \begin{enumerate} \item display parameters: Chromosome, start, end, smooth window size. \item horizon plot: Visualise CNE densities. \end{enumerate} \section{Input} \Rcode{CNEr} starts from \href{http://genome.ucsc.edu/goldenPath/help/axt.html}{axt} net files of two genomes pairwise alignments and bed files for filtering. UCSC already provides a set of precomputed axt files on \url{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 \url{http://genomewiki.ucsc.edu/index.php/Whole_genome_alignment_howto}. \subsection{axt file} So far, there is no suitable class to store the axt files in Bioconductor. Hence we created a \textbf{S4} class \Rclass{Axt} to hold the content from axt files. Basically, it utilises \Robject{GRanges} from \Rpackage{GenomicRanges} package and \Robject{DNAStringSet} from \Rpackage{Biostrings} package. To read axt file into R, \Rpackage{CNEr} provides \Rfunction{readAxt} function for highly efficient reading, which heavily depends on Kent's utilities source code \citep{Kent:2002bw}. 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) @ <<>= data(axtHg19DanRer7) data(axtDanRer7Hg19) @ <<<>= axtHg19DanRer7 axtDanRer7Hg19 @ \subsection{bed files} The gene annotation information, including exons coordinates, repeats, is provided in a bed file. Here we summarise a table of filtering information we used: \begin{center} \begin{tabular}{*{4}{l}} \hline Assembly & Name & Exon & Repeat \\ \hline 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 \\ 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 \\ \hline \end{tabular} \end{center} Of course, more customised gene annotation can be included in the bed file. To import bed file into \Robject{GRanges} in \R{R}, \Rpackage{rtracklayer} provides a general function \Rfunction{import.bed} to do that. However, if your bed file only contains 3 columns: chromosome, start and end, that are the information needed by \Rpackage{CNEr}, then it is better to use the \Rfunction{readBed} function that is much faster with C implementation. <>= bedHg19Fn <- file.path(system.file("extdata", package="CNEr"), "filter_regions.hg19.bed") bedHg19 <- readBed(bedHg19Fn) bedHg19 bedDanRer7Fn <- file.path(system.file("extdata", package="CNEr"), "filter_regions.danRer7.bed") bedDanRer7 <- readBed(bedDanRer7Fn) bedDanRer7 @ \subsection{Chromosome size of query assembly} 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 \Rfunction{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 \Robject{Seqinfo} is returned which suits the input to downstream analysis. <>= qSizesHg19 <- fetchChromSizes("hg19") qSizesDanRer7 <- fetchChromSizes("danRer7") @ <>= data(qSizesHg19) data(qSizesDanRer7) @ <>= qSizesHg19 qSizesDanRer7 @ \Robject(Seqinfo) can also be generated from local two bit file with \Rfunction{seqinfo} from \Rpackage{rtracklayer}. \section{CNE identification} In this section, we will go through the details of CNE identification. \subsection{net alignments scan} 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 \textbf{I} identities over \textbf{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 (\textbf{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 \Robject(list) of \Robject(data.frame) is returned from \Rfunction(ceScan), which contains the preliminary CNEs. Here is some exemple output: <>= #data(CNEHg19DanRer7) lapply(CNEHg19DanRer7, head) @ 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. \subsection{Merge CNEs} 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. <>= #data(CNEDanRer7Hg19) cneMergedDanRer7Hg19 <- mapply(cneMerge, CNEDanRer7Hg19, CNEHg19DanRer7, SIMPLIFY=FALSE) lapply(cneMergedDanRer7Hg19, head) @ \subsection{Realignment of CNEs} Some CNEs might be unannotated repeats. To remove them, currently we use \textbf{blat} \citep{Kent:2002jd} 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 \Rfunction{ceScanOneStep} and it returns a \Robject{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) @ \section{CNE storage and query} 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. \subsection{CNE storage and query} Since the CNEs \Robject{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 \citep{Kent:2002bw} and \href{http://genomewiki.ucsc.edu/index.php/Bin_indexing_system}{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 \Robject{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) @ \section{CNEs visualisation} To visualise the CNEs together with other gene annotations, we choose to use the Bioconductor package \Rpackage{Gviz} in this vignette. \Rpackage{Gviz}, based on the \Rpackage{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 \Rpackage{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 \Rpackage{Gviz}. Another option for visualisation is the package \Rpackage{ggbio}, which is based on \Rpackage{ggplot2}. The advantage of \Rpackage{ggbio} is the simplicity of adding any customised \Robject{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 \Robject{ggplot2} format is available from \url{http://timelyportfolio.blogspot.co.uk/2012/08/horizon-on-ggplot2.html}. \subsection{Gene annotation visualisation} For the example of hg19 vs danRer7 in this vignette, we choose hg19 as the reference and show the range of developmental gene \textbf{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") @ <>= data(axisTrack) data(ideoTrack) data(cpgIslands) data(refGenes) @ <>= 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{R} object, such as\Robject{data.frame}, \Robject{GRanges}, \Robject{IRanges} or even from a local file. Usually the \textbf{gff} file containing the gene annotation can be processed by \Rpackage{Gviz} directly. For more details, please look into the vignette of \Rpackage{Gviz}. \subsection{CNEs horizon plot} <>= 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. \section{Conclusion} 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() @ \newpage \bibliographystyle{jss} \bibliography{CNEr} \end{document}