├── DRB-TTseq.html
├── DRB_TT-seq.md
├── LICENSE
├── README.md
├── align.md
├── bigwig.md
├── bigwig_bedtools.md
├── data
    ├── README.md
    └── chrom.sizes.txt
├── metaprofiles.md
└── scripts
    ├── DRB-TTseq.R
    ├── DRB-TTseq.Rmd
    ├── align.sh
    ├── bigwig.sh
    ├── bigwig_bedtools.sh
    └── metaprofiles.sh


/DRB_TT-seq.md:
--------------------------------------------------------------------------------
  1 | This is a companion script to the publication below.  It describes a pipeline for calling RNA Pol II transcription wave peak positions and elongation rates from DRB/TT-seq time-series data using R.  Instructions are given for calculating wave peaks at both the single-gene and meta-gene level.  
  2 |   
  3 | 
  4 | *Nascent transcriptome profiles and measurement of transcription elongation using TT-seq.*
  5 | Lea H. Gregersen<sup>1</sup> Richard Mitter<sup>2</sup> and Jesper Q. Svejstrup<sup>1</sup>
  6 | <sup>1</sup>Mechanisms of Transcription Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
  7 | <sup>2</sup>Bioinformatics and Biostatistics, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
  8 | 
  9 | ---
 10 | 
 11 | 
 12 | #### Define genic intervals and calculate coverage directly from BAM files.
 13 | 
 14 | ```bash
 15 | library(Rsamtools)
 16 | library(GenomicRanges)
 17 | library(rtracklayer)
 18 | library(GenomicFeatures)
 19 | library(bamsignals)
 20 | library(reshape2)
 21 | library(ggplot2)
 22 | library(DT)
 23 | ```
 24 | 
 25 | 
 26 | 
 27 | ```bash
 28 | work.dir <- "/path/to/my/working_directory/"
 29 | setwd(work.dir)
 30 | ```
 31 | 
 32 | 
 33 | #### Create a data.frame detailing BAM files and sample names and any pertinent meta-data such as DRB release time.
 34 | ```bash
 35 | drb.df <- data.frame(
 36 |   name = c("DRB.10min","DRB.20min","DRB.30min","DRB.40min"),
 37 |   bam  = c("bam/DRB.10min.bam","bam/DRB.20min.bam","bam/DRB.30min.bam","bam/DRB.40min.bam"),
 38 |   time = c(10,20,30,40),
 39 |   stringsAsFactors=F)
 40 | drb.df <- drb.df[order(drb.df$time),]
 41 | ```
 42 | 
 43 | 
 44 | #### Genome transcriptome GTF files are available for download from [Ensembl](http://www.ensembl.org/index.html).  The GTF file may be indexed using [igvtools](https://software.broadinstitute.org/software/igv/igvtools_commandline) *index* command. Make sure that the assembly matches the one used for read alignments.
 45 | ```bash
 46 | gtf.file     <- "data/Homo_sapiens.GRCh38.86.gtf"
 47 | ```
 48 | 
 49 | 
 50 | #### Filters genes to remove any that are overly short, overly long, any non-coding genes any overlapping another gene and any that map to non-standard chromosomes.
 51 | ```bash
 52 | gtf.dat <- import(gtf.file)
 53 | gene.gr <- gtf.dat[gtf.dat$type %in% "gene",]
 54 | names(gene.gr) <- gene.gr$gene_id
 55 | gene.gr$gene.width <- width(gene.gr)
 56 | 
 57 | gene.gr <- gene.gr[width(gene.gr) >= 60000 & width(gene.gr) < 300000]
 58 | gene.gr <- gene.gr[gene.gr$gene_biotype %in% "protein_coding"]
 59 | gene.gr <- gene.gr[countOverlaps(gene.gr,gene.gr) == 1]
 60 | gene.gr <- keepSeqlevels(gene.gr,c(as.character(1:22),"X","Y"),pruning.mode="coarse")
 61 | ```
 62 | 
 63 | 
 64 | #### Create GRanges object representing filtered genes -2kb:+120kb around their promoters.
 65 | ```bash
 66 | up.ext  <- 2000
 67 | dn.ext  <- 120000
 68 | intervals.gr <- promoters(gene.gr, upstream=up.ext, downstream=dn.ext)
 69 | intervals.gr <- trim(intervals.gr)
 70 | intervals.gr <- intervals.gr[width(intervals.gr) == (up.ext+dn.ext)]
 71 | ```
 72 | 
 73 | 
 74 | #### Calculate bp-level coverage over the extended gene intervals.
 75 | Currently this is not strand-specific as "bamCoverage" doesn't allow it, but filtering out overlapping genes should take care of most of the problems.  Alternatively, one could filter the bam file by strand prior to calculating coverage.
 76 | ```bash
 77 | sigs.list <- list()
 78 | for (r in 1:nrow(drb.df)) {
 79 |     sigs <- bamCoverage(
 80 |         bampath                    = drb.df$bam[r],
 81 |         gr                         = intervals.gr,
 82 |         filteredFlag               = 1024,  # remove duplicates
 83 |         paired.end                 = "ignore",
 84 |         mapq                       = 20,
 85 |         verbose                    = FALSE)
 86 |     sigs.list[[drb.df$name[r]]] <- t(alignSignals(sigs))
 87 |     rownames(sigs.list[[drb.df$name[r]]]) <- names(intervals.gr)
 88 | }
 89 | ```
 90 | 
 91 | 
 92 | #### Scale to Read counts Per Million (RPM).
 93 | ```bash
 94 | read.length <- 75
 95 | rpm.list   <- list()
 96 | for (n in 1:length(sigs.list)) {
 97 |     n.sig <- sigs.list[[n]] / read.length
 98 |     n.sum <- sum(n.sig)
 99 |     n.sf  <- n.sum / 1000000
100 |     rpm.list[[names(sigs.list)[n]]] <- n.sig / n.sf
101 | }
102 | ```
103 | 
104 | ***
105 | 
106 | ## Meta-gene level wave peak calling.
107 | 
108 | #### Create meta-profiles by taking a trimmed mean.
109 | ```bash
110 | meta.dat <- sapply(rpm.list,function(x){ apply(x,2,function(y) { mean(y,na.rm=T,trim=0.01) }) })
111 | ```
112 | 
113 | 
114 | #### Fit a smoothing spline to each meta-profile.
115 | ```bash
116 | # The spar parameter might need tweaking depending on the fit
117 | spline.dat <- t(apply(meta.dat,2,function(x){smooth.spline(1:length(x),x,spar=0.9)$y }))
118 | ```
119 | 
120 | 
121 | #### Plot the meta-profiles.
122 | ```bash
123 | # Coverage
124 | cov.plot <- melt(meta.dat)
125 | colnames(cov.plot) <- c("position","name","RPM")
126 | cov.plot$position  <- cov.plot$position-up.ext-1
127 | cov.plot$time      <- drb.df$time[match(cov.plot$name,drb.df$name)]
128 | 
129 | # Spline
130 | spline.plot <- melt(t(spline.dat))
131 | colnames(spline.plot) <- c("position","name","RPM")
132 | spline.plot$position  <- spline.plot$position-up.ext-1
133 | spline.plot$time      <- drb.df$time[match(spline.plot$name,drb.df$name)]
134 | 
135 | P1 <- ggplot(cov.plot,aes(x=position,y=RPM,colour=name)) + geom_line(alpha=0.4)
136 | P1 <- P1 + geom_line(aes(x=position,y=RPM,colour=name),spline.plot,size=2)
137 | P1 <- P1 + xlab("Position relative to TSS (kb)") + ylab("RPM") + ggtitle("DRB/TT-seq coverage")
138 | P1 <- P1 + scale_x_continuous(breaks=c(0,40000,80000,120000),label=c("TSS","40kb","80kb","120kb"))
139 | #P1 <- P1 + scale_colour_manual(values=c("DRB.10min"="#231F20", "DRB.20min"="#58595B", "DRB.30min"="#A7A9AC", "DRB.40min"="#D1D3D4"))
140 | #ggsave(filename="results/DRB_metaprofile.png",plot=P1,device="png",height=5)
141 | P1
142 | ```
143 | 
144 | 
145 | #### Calculate wave peaks from meta-profiles as the maximum point on the spline.
146 | Maxima are only called after the preceeding timepoint's maximum position, forcing the wave to advance with time.
147 | ```bash
148 | wf.dat <- drb.df[,c("name","time")]
149 | wf.dat$wave <- 0
150 | for (r in 1:nrow(wf.dat)) {
151 |   present.sample <- wf.dat$name[r]
152 |   if (r==1) {
153 |     previous.wf <- 0
154 |     wf.dat$wave[r] <- which.max(spline.dat[present.sample,])
155 |   } else {
156 |     previous.wf    <- wf.dat$wave[r-1]
157 |     wf.dat$wave[r] <- which.max(spline.dat[present.sample,-1:-previous.wf])+previous.wf
158 |   }
159 | }
160 | wf.dat$wave <- wf.dat$wave - 2001
161 | wf.dat$wave <- wf.dat$wave / 1000
162 | 
163 | # Optionally add an additional time=0 datapoint which assumes a wave peak at position=0.
164 | wf.dat <- data.frame(rbind(c("DRB.0min",0,0),wf.dat),stringsAsFactors=F)
165 | wf.dat$time <- as.numeric(wf.dat$time)
166 | wf.dat$wave <- as.numeric(wf.dat$wave)
167 | ```
168 | 
169 | 
170 | #### Wave peaks calculated from the meta-gene profiles.
171 | ```bash
172 | datatable(wf.dat,rownames=FALSE)
173 | ```
174 | 
175 | 
176 | #### Fit a linear model to the wave peak positions as a function of time to determine the rate of elongation, kb/min.
177 | ```bash
178 | lm.fit <- lm(wf.dat$wave~wf.dat$time)
179 | elongation.rate <- lm.fit$coefficients[2]
180 | P2 <- ggplot(wf.dat,aes(x=time,y=wave)) + geom_point(size=4)
181 | P2 <- P2 + xlab("Time (min)") + ylab("Wave position (kb)")
182 | P2 <- P2 + geom_abline(intercept = lm.fit$coefficients[1], slope = lm.fit$coefficients[2])
183 | P2 <- P2 + annotate(geom="text", x=10, y=75, label=paste("y = ",round(lm.fit$coefficients[2],2),"x",round(lm.fit$coefficients[1],2),sep=''))
184 | #ggsave(filename="results/DRB_metaprofile_elongation_rate.png",plot=P2,device="png")
185 | P2
186 | ```
187 | 
188 | 
189 | ***
190 | 
191 | ## Single gene level wave peak calling
192 | 
193 | #### Generate a set of gene ids that pass an arbitrary expression threshold.
194 | ```bash
195 | expr.mat  <- sapply(rpm.list,function(x){apply(x,1,function(y){sum(y,na.rm=T)})})
196 | expr.plot <- ggplot(melt(expr.mat),aes(x=log2(value+0.1),fill=Var2)) + geom_histogram(bins=50)
197 | expr.plot <- expr.plot + geom_vline(aes(xintercept=log2(100+1)),colour="darkred") + facet_grid(Var2~.) + xlab("log2(RPM+0.1)") + ylab("frequency") + ggtitle("Expression filter")
198 | expr.gids <- rownames(expr.mat)[rowSums(expr.mat > 100) == ncol(expr.mat)]
199 | #ggsave(filename="results/DRB_expression_filter.png",plot=expr.plot ,device="png")
200 | expr.plot
201 | ```
202 | 
203 | 
204 | #### Fit a smoothing spline over the RPM data for each gene for each sample.
205 | ```bash
206 | spline.list <- list()
207 | for (n in 1:length(rpm.list)) {
208 |     spline.dat <- t(apply(rpm.list[[n]],1,function(x){smooth.spline(1:length(x),x,spar=0.9)$y }))
209 |     spline.list[[names(rpm.list)[n]]] <- spline.dat
210 | }
211 | ```
212 | 
213 | 
214 | #### Calculate wave peak for each gene as the maximum point on the spline.
215 | ```bash
216 | wf.genes <- sapply(spline.list,function(x){ apply(x,1,function(y){which.max(y)}) })
217 | rownames(wf.genes) <- rownames(spline.list[[1]])
218 | ```
219 | 
220 | 
221 | #### Filter the gene level wave peak predictions.  Remove any genes that are lowly expressed, have missng values, have duplicate values or whose wave doesn't advance with time.  Select only genes with a wave-peak after the first 2 kb in the 10min sample.
222 | ```bash
223 | wf.genes.filt <- wf.genes[expr.gids,c("DRB.10min","DRB.20min","DRB.30min","DRB.40min")]
224 | wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(is.na(x))}),]
225 | wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(duplicated(x))}),]
226 | wf.genes.filt <- wf.genes.filt[apply(wf.genes.filt,1,function(x){all(order(x)==1:nrow(drb.df))}),]
227 | wf.genes.filt <- wf.genes.filt[wf.genes.filt[,"DRB.10min"]>2000,]
228 | 
229 | ```
230 | 
231 | 
232 | #### Sometimes it is necessary to disregard the final timepoint when generating the filter as transcription might have already reached the end of the gene at that point.  This version only uses the first three timepoints.
233 | ```bash
234 | # wf.genes.filt <- wf.genes[expr.gids,c("DRB.10min","DRB.20min","DRB.30min")]
235 | # wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(is.na(x))}),]
236 | # wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(duplicated(x))}),]
237 | # wf.genes.filt <- wf.genes.filt[apply(wf.genes.filt,1,function(x){all(order(x)==1:3)}),]
238 | # wf.genes.filt <- wf.genes.filt[wf.genes.filt[,"DRB.10min"]>2000,]
239 | 
240 | ```
241 | 
242 | 
243 | #### Compile results.
244 | ```bash
245 | wf.genes.dBase <- data.frame(
246 |     gene_id     = rownames(wf.genes),
247 |     gene_name   = intervals.gr[rownames(wf.genes),]$gene_id,
248 |     gene_width  = intervals.gr[rownames(wf.genes),]$gene.width,
249 |     mean.rpkm   = rowMeans(expr.mat[rownames(wf.genes),]),
250 |     wf.order    = apply(wf.genes[rownames(wf.genes),drb.df$name],1,function(x){paste(order(x),sep='',collapse='')}),
251 |     wf.genes,
252 |     filter      = rownames(wf.genes) %in% rownames(wf.genes.filt),
253 |     stringsAsFactors=F,
254 |     check.names=F)
255 | ```
256 | 
257 | 
258 | #### Fit a linear model to the wave peak positions as a function of time to determine the rate of elongation, kb/min.
259 | ```bash
260 | lm.dat <- wf.genes.dBase[,drb.df$name]
261 | wf.genes.dBase$elongation.rate <- apply(lm.dat,1,function(x) {
262 |   time <- c(0,drb.df$time)
263 |   wf   <- c(0,x)/1000
264 |   lm(wf~time)$coef[2]
265 | })
266 | ```
267 | 
268 | 
269 | #### Plot a distribution of elongation rates for genes passing the filter.
270 | ```bash
271 | single.elong_rate.dat <- data.frame(wf.genes.dBase[wf.genes.dBase$filter,c("gene_name","elongation.rate")],stringsAsFactors=F)
272 | P3 <- ggplot(single.elong_rate.dat,aes(x=elongation.rate)) + geom_histogram(alpha=0.5,colour="#7CAE00",fill="#7CAE00")
273 | P3 <- P3 + ylab("frequency") + xlab("Elongation rate (kb/min)") + ggtitle(paste("Elongation rate, n=",nrow(single.elong_rate.dat),sep=''))
274 | #ggsave(filename="results/DRB_elongation_rate_distribution.png",plot=P3,device="png")
275 | P3
276 | ```
277 | 
278 | 
279 | #### Session information
280 | ```bash
281 | sessionInfo()
282 | ```
283 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # DRB_TT-seq
 2 | 
 3 | Scripts for the analysis of TT-seq and DRB/TT-seq data.
 4 | 
 5 | This is a companion repository to the publication below.  
 6 | 
 7 | *Using TTchem-Seq to Profile Nascent Transcription and Measuring Transcript Elongation.*<br>
 8 | *Lea H. Gregersen<sup>1</sup> Richard Mitter<sup>2</sup> and Jesper Q. Svejstrup<sup>1</sup>.*<br>
 9 | *<sup>1</sup>Mechanisms of Transcription Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK.*<br>
10 | *<sup>2</sup>Bioinformatics and Biostatistics, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.*<br>
11 | 
12 | 
13 | ---
14 | 
15 | ### [align.md](https://github.com/crickbabs/DRB_TT-seq/blob/master/align.md)
16 | This is a bash markdown document for aligning paired Illumina sequence reads against a reference genome using STAR resulting in a sorted and indexed BAM file.
17 | 
18 | ### [bigwig.md](https://github.com/crickbabs/DRB_TT-seq/blob/master/bigwig.md)
19 | This is a bash markdown document for generating scaled strand-specific BIGWIG files from a BAM file containing paired reads.
20 | 
21 | ### [bigwig_bedtools.md](https://github.com/crickbabs/DRB_TT-seq/blob/master/bigwig_bedtools.md)
22 | This is a bash markdown document for generating scaled strand-specific BIGWIG files from a BAM file containing paired reads.  It is meant as an alternative to **bigwig.md** to be used on large bam files when deeptools struggles.
23 | 
24 | ### [metaprofiles.md](https://github.com/crickbabs/DRB_TT-seq/blob/master/metaprofiles.md)
25 | This is a bash markdown document for generating strand-specific metagene, TSS and TES profiles from a BAM file using "ngs.plot".
26 | 
27 | ### [DRB-TTseq.md](https://github.com/crickbabs/DRB_TT-seq/blob/master/DRB_TT-seq.md)
28 | This is a markdown document describing a pipeline for calling RNA Pol II transcription wave peak positions and elongation rates from DRB/TT-seq time-series data using R.  Instructions are given for calculating wave peaks at both the single-gene and meta-gene level.  An Rmarkdown version of the scripts is available in the [scripts](https://github.com/crickbabs/DRB_TT-seq/blob/master/scripts)
29 | An example html output of this script is given in **[DRB-TTseq.html](https://github.com/crickbabs/DRB_TT-seq/blob/master/DRB-TTseq.html)** - view raw, save to your desktop then open with your browser in order to view it.
30 | Users unfamiliar with R markdown are recommended to explore it using [rstudio](https://www.rstudio.com/).
31 | 
32 | ### [data](https://github.com/crickbabs/DRB_TT-seq/blob/master/data/README.md)
33 | This directory contains details of demo FASTQ data available from the NCBI's Short Read Archive (SRA).
34 | 
35 | ### [scripts](https://github.com/crickbabs/DRB_TT-seq/blob/master/scripts)
36 | This directory contains plain script versions of the markdown documents.
37 | 
38 | 
39 | ---
40 | 
41 | ### Dependencies and requirements
42 | 
43 | Scripts were tested using the following software versions:
44 | 
45 | * SAMtools v1.3.1
46 | * deepTools v2.5.3
47 | * BEDTools/2.27.1
48 | * kentUtils
49 | * STAR 2.5.2a
50 | * Picard v2.1.1
51 | * R v3.5.1 running Bioconductor version 3.7
52 | * ngsplot v2.63
53 | 
54 | Example Bash scripts are written to be executed in a linux environment.  Scripts were tested on a linux server equipped with a 8-core Intel E5-2640 Haswell CPU running at 2.6GHz and using 8 processors and 8gb RAM.  
55 | 
56 | The R script may be run on any machine able to run R v3.5.1 or higher, though for large datasets it is recommended that at least 16gb RAM be made available to the process.
57 | 
58 | ---
59 | 
60 | ### References
61 | 
62 | * Andrews, S. FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).
63 | 
64 | * Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21, doi:10.1093/bioinformatics/bts635 (2013).
65 | 
66 | * Hunt, S. E. et al. Ensembl variation resources. Database (Oxford) 2018, doi:10.1093/database/bay119 (2018).
67 | 
68 | * Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics. 2010 Sep 1;26(17):2204-7.
69 | 
70 | * Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput Biol 9, e1003118, doi:10.1371/journal.pcbi.1003118 (2013).
71 | 
72 | * Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-2079, doi:10.1093/bioinformatics/btp352 (2009).
73 | 
74 | * Mammana, A. H., J. bamsignals: Extract read count signals from bam files. R package version 1.12.11 (2016).
75 | 
76 | * Quinlan AR. BEDTools: The Swiss-Army Tool for Genome Feature Analysis. CurrProtoc Bioinformatics. 2014 Sep 8;47:11.12.1-34.
77 | 
78 | * Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data.  Nucleic Acids Res 42, W187-191, doi:10.1093/nar/gku365 (2014).
79 | 
80 | * Shen, L., Shao, N., Liu, X. and Nestler, E. (2014) ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases, BMC Genomics, 15, 284.
81 | 
82 | * http://broadinstitute.github.io/picard/.
83 | 


--------------------------------------------------------------------------------
/align.md:
--------------------------------------------------------------------------------
 1 | ## This bash script provides instruction for aligning paired Illumina sequence reads against a reference genome using STAR.
 2 | 
 3 | In this example a *S. cerevisiae* RNA spike-in was inserted into the human RNA sample prior to sequencing.  Spike-in abundance was estimated by a separate mapping of the sequence reads to the yeast genome using STAR.  However, creating a composite human/yeast genome and aligning to that or using an alignment free abundance estimator such as [kallisto](https://pachterlab.github.io/kallisto/) are also valid options.
 4 | 
 5 | The purpose of the spike-in is to generate a scale-factor to account for differences in library size between multiple samples.  A scale factor may be as simple as a ratio of mapped spike-in reads between two samples.  A more robust scale factor may be calculated across multiple samples using the "estimateSizeFactors" function from the [Bioconductor](https://bioconductor.org/) package [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) using sample specific spike-in gene count information.
 6 | 
 7 | ---
 8 | 
 9 | The output of this script are two sorted, duplicate marked and indexed BAM files - one for the target organism and one for the spike-in.  There are also gene level counts produced by STAR ("*.ReadsPerGene.out.tab") that may be may used to generate scale factors.
10 | 
11 | *It is assumed that the FASTQ reads have been checked for quality and any filtering, adapter trimming etc. that might be required has been done prior to running this script.*
12 | 
13 | If there are multiple sets of FASTQ files per sample, i.e. more than 1 set of paired reads, it is recommended to align these separately and merge the resultant BAM files using "samtools merge" before continuing with the downstream analysis.
14 | 
15 | Marking duplicate reads isn't strictly necessary but the calculated levels of duplication provide insight into sample quality.  Also, counts of unique mapped reads may be useful for scale factor normalisation.
16 | 
17 | Dependencies:<br>
18 | SAMtools     http://samtools.sourceforge.net/<br>
19 | Picard       https://broadinstitute.github.io/picard/<br>
20 | STAR         https://github.com/alexdobin/STAR<br>
21 | 
22 | ---
23 | 
24 | #### Set working, temporary and results directories
25 | ```bash
26 | WORKDIR="/path/to/my/working_directory/"
27 | TMPDIR="${WORKDIR}tmp/"
28 | ALIGNDIR="${WORKDIR}alignments/"
29 | SPIKEDIR="${WORKDIR}alignments_spike/"
30 | mkdir -p $TMPDIR
31 | mkdir -p $ALIGNDIR
32 | mkdir -p $SPIKEDIR
33 | ```
34 | 
35 | 
36 | #### Sample information: sample name and location of paired FASTQ files.
37 | ```bash
38 | SAMPLE="WT";
39 | FQ1="${WORKDIR}FQ1.fastq.gz"
40 | FQ2="${WORKDIR}FQ2.fastq.gz"
41 | ```
42 | 
43 | 
44 | #### Threads - set to take advantage of multi-threading and speed things up.
45 | ```bash
46 | THREADS=8
47 | ```
48 | 
49 | 
50 | #### Path to STAR genome indices
51 | These were created using GRCh38 Ensembl v86 (*Homo sapiens*) and R64-1-1 Ensembl v86 (*Saccharomyces cerevisiae*) genome sequences and GTF files downloaded from the [Ensembl](https://www.ensembl.org/index.html) database.  Please refer to the STAR manual for information on how to create your own genomes indices.
52 | ```bash
53 | HUMANIDX="/path/to/my/human_genome_index/"
54 | SPIKEIDX="/path/to/my/yeast_genome_index/"
55 | ```
56 | 
57 | 
58 | #### Align to the human genome.  Sort, mark duplicates and index the genome BAM.
59 | ```bash
60 | cd $ALIGNDIR
61 | STAR --runThreadN ${THREADS} --runMode alignReads --genomeDir ${HUMANIDX} --readFilesIn ${FQ1} ${FQ2} --readFilesCommand zcat --quantMode TranscriptomeSAM GeneCounts --twopassMode Basic --outSAMunmapped None --outSAMattrRGline ID:${SAMPLE} PU:${SAMPLE} SM:${SAMPLE} LB:unknown PL:illumina --outSAMtype BAM Unsorted --outTmpDir ${TMPDIR}${SAMPLE} --outFileNamePrefix ${ALIGNDIR}${SAMPLE}.
62 | samtools sort --threads ${THREADS} -o ${ALIGNDIR}${SAMPLE}.sorted.bam ${ALIGNDIR}${SAMPLE}.Aligned.out.bam
63 | java -jar picard.jar MarkDuplicates INPUT=${ALIGNDIR}${SAMPLE}.sorted.bam OUTPUT=${ALIGNDIR}${SAMPLE}.sorted.marked.bam METRICS_FILE=${ALIGNDIR}${SAMPLE}.sorted.marked.metrics REMOVE_DUPLICATES=false ASSUME_SORTED=true MAX_RECORDS_IN_RAM=2000000 VALIDATION_STRINGENCY=LENIENT TMP_DIR=${TMPDIR}${SAMPLE}
64 | samtools index ${ALIGNDIR}${SAMPLE}.sorted.marked.bam
65 | rm ${ALIGNDIR}${SAMPLE}.sorted.bam
66 | ```
67 | 
68 | #### Align to the yeast genome (spike-in).  Sort, mark duplicates and index the genome BAM.
69 | ```bash
70 | cd $SPIKEDIR
71 | STAR --runThreadN ${THREADS} --runMode alignReads --genomeDir ${SPIKEIDX} --readFilesIn ${FQ1} ${FQ2} --readFilesCommand zcat --quantMode TranscriptomeSAM GeneCounts --twopassMode Basic --outSAMunmapped None --outSAMattrRGline ID:${SAMPLE} PU:${SAMPLE} SM:${SAMPLE} LB:unknown PL:illumina --outSAMtype BAM Unsorted --outTmpDir ${TMPDIR}${SAMPLE}.spike --outFileNamePrefix ${ALIGNDIR}${SAMPLE}.
72 | samtools sort --threads ${THREADS} -o ${SPIKEDIR}${SAMPLE}.sorted.bam ${SPIKEDIR}${SAMPLE}.Aligned.out.bam
73 | java -jar picard.jar MarkDuplicates INPUT=${SPIKEDIR}${SAMPLE}.sorted.bam OUTPUT=${SPIKEDIR}${SAMPLE}.sorted.marked.bam METRICS_FILE=${SPIKEDIR}${SAMPLE}.sorted.marked.metrics REMOVE_DUPLICATES=false ASSUME_SORTED=true MAX_RECORDS_IN_RAM=2000000 VALIDATION_STRINGENCY=LENIENT TMP_DIR=${TMPDIR}${SAMPLE}
74 | samtools index ${SPIKEDIR}${SAMPLE}.sorted.marked.bam 
75 | rm ${SPIKEDIR}${SAMPLE}.sorted.bam
76 | ```
77 | 


--------------------------------------------------------------------------------
/bigwig.md:
--------------------------------------------------------------------------------
  1 | ## This bash script provides a means of generating scaled strand-specific BIGWIG files from a BAM file containing paired reads.
  2 | 
  3 | Adapted from:<br>
  4 | *Ramírez, Fidel, Devon P. Ryan, Björn Grüning, Vivek Bhardwaj, Fabian Kilpert, Andreas S. Richter, Steffen Heyne, Friederike Dündar, and Thomas Manke.*<br>
  5 | *deepTools2: A next Generation Web Server for Deep-Sequencing Data Analysis. Nucleic Acids Research (2016). doi:10.1093/nar/gkw257.*<br>
  6 | *https://deeptools.readthedocs.io/en/develop/content/tools/bamCoverage.html*<gr>
  7 | 
  8 | Dependencies:<br>
  9 |     SAMtools     http://samtools.sourceforge.net/<br>
 10 |     deepTools    https://deeptools.readthedocs.io/en/develop/index.html<br>
 11 | 
 12 | If you experience performance issues using this script it is recommended to try [bigwig_bedtools.sh](https://github.com/crickbabs/DRB_TT-seq/blob/master/bigwig_bedtools.md) as an alternative.
 13 | 
 14 | ---
 15 | 
 16 | #### Set working, temporary and results directories.
 17 | ```bash
 18 | WORKDIR="/path/to/my/working_directory/"
 19 | TMPDIR="${WORKDIR}tmp/"
 20 | BIGWIGDIR="${WORKDIR}bigwig/"
 21 | mkdir -p $TMPDIR
 22 | mkdir -p $BIGWIGDIR
 23 | ```
 24 | 
 25 | 
 26 | #### Sample information: sample name and BAM file location.
 27 | ```bash
 28 | SAMPLE="WT";
 29 | BAM="${WORKDIR}WT.bam"
 30 | ```
 31 | 
 32 | 
 33 | #### Threads - set to take advantage of multi-threading and speed things up.
 34 | ```bash
 35 | THREADS=1
 36 | ```
 37 | 
 38 | 
 39 | #### Temporaty BAM files.
 40 | ```bash
 41 | BAMFOR="${TMPDIR}${SAMPLE}.fwd.bam"     # BAM file representing reads mapping to forward strand
 42 | BAMREV="${TMPDIR}${SAMPLE}.rev.bam"     # BAM file representing reads mapping to reverse strand
 43 | BAMFOR1="${TMPDIR}${SAMPLE}.fwd1.bam"
 44 | BAMFOR2="${TMPDIR}${SAMPLE}.fwd2.bam"
 45 | BAMREV1="${TMPDIR}${SAMPLE}.rev1.bam"
 46 | BAMREV2="${TMPDIR}${SAMPLE}.rev2.bam"
 47 | ```
 48 | 
 49 | 
 50 | #### bigwig files.
 51 | ```bash
 52 | BIGWIG="${BIGWIGDIR}${SAMPLE}.bigwig"           # BIGWIG file representing all reads
 53 | BIGWIGFOR="${BIGWIGDIR}${SAMPLE}.for.bigwig"    # BIGWIG file representing reads mapping to forward strand
 54 | BIGWIGREV="${BIGWIGDIR}${SAMPLE}.rev.bigwig"    # BIGWIG file representing reads mapping to reverse strand
 55 | ```
 56 | 
 57 | #### Scale factor.
 58 | A scale factor is used to normalise for differences in library sizes across samples.  There are many ways to generate such a factor, such as the ratio of reads between two samples of spike-ins.  A more robust scale factor may be calculated using the "estimateSizeFactors" function from the Bioconductor package DESeq2 using sample gene count information.  Setting this to 1 indicates no scaling.  Note that it is necessary to use the recipricol of the the scale factor returned by DESeq2 when passing to the bamCoverage function since coverage will be multiplied by this.
 59 | ```bash
 60 | SCALEFACTOR=1
 61 | ```
 62 | 
 63 | 
 64 | #### Create bigwig file for all reads.
 65 | ```bash
 66 | bamCoverage --scaleFactor $SCALEFACTOR -p $THREADS -b $BAM -o $BIGWIG
 67 | ```
 68 | 
 69 | 
 70 | #### Create bigwig file for the forward strand.
 71 | Get file for transcripts originating on the forward strand.<br>
 72 | Include reads that are 2nd in a pair (128).  Exclude reads that are mapped to the reverse strand (16)<br>
 73 | Exclude reads that are mapped to the reverse strand (16) and first in a pair (64): 64 + 16 = 80<br>
 74 | ```bash
 75 | samtools view -b -f 128 -F 16 --threads $THREADS $BAM > $BAMFOR1
 76 | samtools view -b -f 80  --threads $THREADS $BAM > $BAMFOR2
 77 | samtools merge --threads $THREADS -f $BAMFOR $BAMFOR1 $BAMFOR2
 78 | samtools index $BAMFOR
 79 | bamCoverage --scaleFactor $SCALEFACTOR -p $THREADS -b $BAMFOR -o $BIGWIGFOR
 80 | ```
 81 | 
 82 | 
 83 | #### Create bigwig file for the reverse strand.
 84 | Get the file for transcripts that originated from the reverse strand:<br>
 85 | Include reads that map to the reverse strand (128) and are second in a pair (16): 128 + 16 = 144<br>
 86 | Include reads that are first in a pair (64), but exclude those ones that map to the reverse strand (16)<br>
 87 | ```bash
 88 | samtools view -b -f 144 --threads $THREADS $BAM > $BAMREV1
 89 | samtools view -b -f 64 -F 16 --threads $THREADS $BAM > $BAMREV2
 90 | samtools merge --threads $THREADS -f $BAMREV $BAMREV1 $BAMREV2
 91 | samtools index $BAMREV
 92 | bamCoverage --scaleFactor $SCALEFACTOR -p $THREADS -b $BAMREV -o $BIGWIGREV
 93 | ```
 94 | 
 95 | 
 96 | #### Remove temporary files.
 97 | ```bash
 98 | rm $BAMFOR $BAMFFOR1 $BAMFFOR2 $BAMREV $BAMREV1 $BAMREV2
 99 | ```
100 | 


--------------------------------------------------------------------------------
/bigwig_bedtools.md:
--------------------------------------------------------------------------------
  1 | ## This bash script provides a means of generating scaled strand-specific BIGWIG files from a BAM file containing paired reads.
  2 | 
  3 | This alternative to "bigwig.sh" uses bedtools (Quinlan, *et al* 2014) rather than deeptools to generate bedgraph files which are in turn converted to bigwig format via Jim Kent's BigWig and BigBed tools (kent, *et al*, 2010).  It is better able to deal with large bam files.
  4 | 
  5 | Dependencies:<br>
  6 |     SAMtools     http://samtools.sourceforge.net/<br>
  7 |     bedtools     https://bedtools.readthedocs.io/en/latest/<br>
  8 |     kentUtils    http://bioinformatics.oxfordjournals.org/content/26/17/2204.long</br>
  9 | 
 10 | ---
 11 | 
 12 | 
 13 | #### Set working, temporary and results directories.
 14 | ```bash
 15 | WORKDIR="/path/to/my/working_directory/"
 16 | TMPDIR="${WORKDIR}tmp/"
 17 | BIGWIGDIR="${WORKDIR}bigwig/"
 18 | mkdir -p $TMPDIR
 19 | mkdir -p $BIGWIGDIR
 20 | ```
 21 | 
 22 | #### Sample information: sample name and BAM file location.
 23 | ```bash
 24 | SAMPLE="WT";
 25 | BAM="${WORKDIR}${SAMPLE}.sorted.marked.bam"
 26 | ```
 27 | 
 28 | #### Chromosome size information.  An unheadered, two-column tab-delimited text file: <chromosome name> <size in bases>
 29 | ```bash
 30 | CHRSIZE="${WORKDIR}chrom.sizes.txt"
 31 | ```
 32 | 
 33 | #### Threads - set to take advantage of multi-threading and speed things up.
 34 | ```bash
 35 | THREADS=8
 36 | ```
 37 | 
 38 | #### Temporaty BAM files.
 39 | ```bash
 40 | BAMFOR="${TMPDIR}${SAMPLE}.fwd.bam"     # BAM file representing reads mapping to forward strand
 41 | BAMREV="${TMPDIR}${SAMPLE}.rev.bam"     # BAM file representing reads mapping to reverse strand
 42 | BAMFOR1="${TMPDIR}${SAMPLE}.fwd1.bam"
 43 | BAMFOR2="${TMPDIR}${SAMPLE}.fwd2.bam"
 44 | BAMREV1="${TMPDIR}${SAMPLE}.rev1.bam"
 45 | BAMREV2="${TMPDIR}${SAMPLE}.rev2.bam"
 46 | ```
 47 | 
 48 | #### bedgraph files.
 49 | ```bash
 50 | BEDGRAPH="${TMPDIR}${SAMPLE}.bedgraph"           # BEDGRAPH file representing all reads
 51 | BEDGRAPHFOR="${TMPDIR}${SAMPLE}.for.bedgraph"    # BEDGRAPH file representing reads mapping to forward strand
 52 | BEDGRAPHREV="${TMPDIR}${SAMPLE}.rev.bedgraph"    # BEDGRAPH file representing reads mapping to reverse strand
 53 | ```
 54 | 
 55 | #### Sorted bedgraph files.
 56 | ```bash
 57 | BEDGRAPHSORTED="${TMPDIR}${SAMPLE}.sorted.bedgraph"           # Sorted BEDGRAPH file representing all reads
 58 | BEDGRAPHFORSORTED="${TMPDIR}${SAMPLE}.for.sorted.bedgraph"    # Sorted BEDGRAPH file representing reads mapping to forward strand
 59 | BEDGRAPHREVSORTED="${TMPDIR}${SAMPLE}.rev.sorted.bedgraph"    # Sorted BEDGRAPH file representing reads mapping to reverse strand
 60 | ```
 61 | 
 62 | #### bigwig files.
 63 | ```bash
 64 | BIGWIG="${BIGWIGDIR}${SAMPLE}.bigwig"           # BIGWIG file representing all reads
 65 | BIGWIGFOR="${BIGWIGDIR}${SAMPLE}.for.bigwig"    # BIGWIG file representing reads mapping to forward strand
 66 | BIGWIGREV="${BIGWIGDIR}${SAMPLE}.rev.bigwig"    # BIGWIG file representing reads mapping to reverse strand
 67 | ```
 68 | 
 69 | #### Scale factor.
 70 | ```bash
 71 | SCALEFACTOR=1
 72 | ```
 73 | 
 74 | #### Create bigwig file for all reads.
 75 | ```bash
 76 | bedtools genomecov -ibam $BAM -bg -split -scale $SCALEFACTOR > $BEDGRAPH
 77 | sort -k1,1 -k2,2n $BEDGRAPH > $BEDGRAPHSORTED
 78 | bedGraphToBigWig $BEDGRAPHSORTED $CHRSIZE $BIGWIG
 79 | ```
 80 | 
 81 | #### Create bigwig file for the forward strand.
 82 | ```bash
 83 | samtools view -b -f 128 -F 16 --threads $THREADS $BAM > $BAMFOR1
 84 | samtools view -b -f 80  --threads $THREADS $BAM > $BAMFOR2
 85 | samtools merge --threads $THREADS -f $BAMFOR $BAMFOR1 $BAMFOR2
 86 | samtools index $BAMFOR
 87 | bedtools genomecov -ibam $BAMFOR -bg -split -strand + -scale $SCALEFACTOR > $BEDGRAPHFOR
 88 | sort -k1,1 -k2,2 $BEDGRAPHFOR > $BEDGRAPHFORSORTED
 89 | bedGraphToBigWig $BEDGRAPHFORSORTED $CHRSIZE $BIGWIGFOR
 90 | ```
 91 | 
 92 | #### Create bigwig file for the reverse strand.
 93 | ```bash
 94 | samtools view -b -f 144 --threads $THREADS $BAM > $BAMREV1
 95 | samtools view -b -f 64 -F 16 --threads $THREADS $BAM > $BAMREV2
 96 | samtools merge --threads $THREADS -f $BAMREV $BAMREV1 $BAMREV2
 97 | samtools index $BAMREV
 98 | bedtools genomecov -ibam $BAMREV -bg -split -strand - -scale $SCALEFACTOR > $BEDGRAPHREV
 99 | sort -k1,1 -k2,2 $BEDGRAPHREV > $BEDGRAPHREVSORTED
100 | bedGraphToBigWig $BEDGRAPHREVSORTED $CHRSIZE $BIGWIGREV
101 | ```
102 | 
103 | #### Remove temporary files.
104 | ```bash
105 | rm $BAMFOR $BAMFFOR1 $BAMFFOR2 $BAMREV $BAMREV1 $BAMREV2 $BEDGRAPH $BEDGRAPHFOR $BEDGRAPHREV $BEDGRAPHSORTED $BEDGRAPHFORSORTED $BEDGRAPHREVSORTED
106 | ```
107 | 


--------------------------------------------------------------------------------
/data/README.md:
--------------------------------------------------------------------------------
 1 | Example data for running these scripts are available from the NCBI's Short Read Archive [(SRA)](https://www.ncbi.nlm.nih.gov/sra/) using the accession numbers given below.
 2 | 
 3 | ---
 4 | 
 5 | # DRB_TT-seq
 6 | 
 7 | | accession  | description |
 8 | | ---------- | ----------- |
 9 | | SRR8112728 | DRB_10min   |
10 | | SRR8112732 | DRB_20min   |
11 | | SRR8112736 | DRB_30min   |
12 | | SRR8112740 | DRB_40min   |
13 | 
14 | The files above represent 4 samples generated at 4 timepoints (10, 20, 30 and 40 mniutes) after DRB release.
15 | 
16 | ---
17 | 
18 | # TT-seq
19 | 
20 | | accession  | description    |
21 | | ---------- | -------------- |
22 | | SRR8112935 | WT1_replicate1 |
23 | | SRR8112947 | WT1_replicate2 |
24 | | SRR8112941 | WT2_replicate1 |
25 | | SRR8112953 | WT2_replicate2 |
26 | 
27 | Note that details of 4 paired-end fastq files are given for the example TT-seq data.  These represent two replicates of a single wild-type biological sample, with each replicate being split across 2 sequencing runs.  It is recommended to align each separately.  For the purposes of visualisation in the manscript, the resulting BAM files were merged prior to downstream analysis.  However, this step is not necessary to achieve bigwigs/metaprofiles and analysis of just a single sample should provide reasonable results.
28 | 


--------------------------------------------------------------------------------
/data/chrom.sizes.txt:
--------------------------------------------------------------------------------
  1 | 1	248956422
  2 | 10	133797422
  3 | 11	135086622
  4 | 12	133275309
  5 | 13	114364328
  6 | 14	107043718
  7 | 15	101991189
  8 | 16	90338345
  9 | 17	83257441
 10 | 18	80373285
 11 | 19	58617616
 12 | 2	242193529
 13 | 20	64444167
 14 | 21	46709983
 15 | 22	50818468
 16 | 3	198295559
 17 | 4	190214555
 18 | 5	181538259
 19 | 6	170805979
 20 | 7	159345973
 21 | 8	145138636
 22 | 9	138394717
 23 | MT	16569
 24 | X	156040895
 25 | Y	57227415
 26 | KI270728.1	1872759
 27 | KI270727.1	448248
 28 | KI270442.1	392061
 29 | KI270729.1	280839
 30 | GL000225.1	211173
 31 | KI270743.1	210658
 32 | GL000008.2	209709
 33 | GL000009.2	201709
 34 | KI270747.1	198735
 35 | KI270722.1	194050
 36 | GL000194.1	191469
 37 | KI270742.1	186739
 38 | GL000205.2	185591
 39 | GL000195.1	182896
 40 | KI270736.1	181920
 41 | KI270733.1	179772
 42 | GL000224.1	179693
 43 | GL000219.1	179198
 44 | KI270719.1	176845
 45 | GL000216.2	176608
 46 | KI270712.1	176043
 47 | KI270706.1	175055
 48 | KI270725.1	172810
 49 | KI270744.1	168472
 50 | KI270734.1	165050
 51 | GL000213.1	164239
 52 | GL000220.1	161802
 53 | KI270715.1	161471
 54 | GL000218.1	161147
 55 | KI270749.1	158759
 56 | KI270741.1	157432
 57 | GL000221.1	155397
 58 | KI270716.1	153799
 59 | KI270731.1	150754
 60 | KI270751.1	150742
 61 | KI270750.1	148850
 62 | KI270519.1	138126
 63 | GL000214.1	137718
 64 | KI270708.1	127682
 65 | KI270730.1	112551
 66 | KI270438.1	112505
 67 | KI270737.1	103838
 68 | KI270721.1	100316
 69 | KI270738.1	99375
 70 | KI270748.1	93321
 71 | KI270435.1	92983
 72 | GL000208.1	92689
 73 | KI270538.1	91309
 74 | KI270756.1	79590
 75 | KI270739.1	73985
 76 | KI270757.1	71251
 77 | KI270709.1	66860
 78 | KI270746.1	66486
 79 | KI270753.1	62944
 80 | KI270589.1	44474
 81 | KI270726.1	43739
 82 | KI270735.1	42811
 83 | KI270711.1	42210
 84 | KI270745.1	41891
 85 | KI270714.1	41717
 86 | KI270732.1	41543
 87 | KI270713.1	40745
 88 | KI270754.1	40191
 89 | KI270710.1	40176
 90 | KI270717.1	40062
 91 | KI270724.1	39555
 92 | KI270720.1	39050
 93 | KI270723.1	38115
 94 | KI270718.1	38054
 95 | KI270317.1	37690
 96 | KI270740.1	37240
 97 | KI270755.1	36723
 98 | KI270707.1	32032
 99 | KI270579.1	31033
100 | KI270752.1	27745
101 | KI270512.1	22689
102 | KI270322.1	21476
103 | GL000226.1	15008
104 | KI270311.1	12399
105 | KI270366.1	8320
106 | KI270511.1	8127
107 | KI270448.1	7992
108 | KI270521.1	7642
109 | KI270581.1	7046
110 | KI270582.1	6504
111 | KI270515.1	6361
112 | KI270588.1	6158
113 | KI270591.1	5796
114 | KI270522.1	5674
115 | KI270507.1	5353
116 | KI270590.1	4685
117 | KI270584.1	4513
118 | KI270320.1	4416
119 | KI270382.1	4215
120 | KI270468.1	4055
121 | KI270467.1	3920
122 | KI270362.1	3530
123 | KI270517.1	3253
124 | KI270593.1	3041
125 | KI270528.1	2983
126 | KI270587.1	2969
127 | KI270364.1	2855
128 | KI270371.1	2805
129 | KI270333.1	2699
130 | KI270374.1	2656
131 | KI270411.1	2646
132 | KI270414.1	2489
133 | KI270510.1	2415
134 | KI270390.1	2387
135 | KI270375.1	2378
136 | KI270420.1	2321
137 | KI270509.1	2318
138 | KI270315.1	2276
139 | KI270302.1	2274
140 | KI270518.1	2186
141 | KI270530.1	2168
142 | KI270304.1	2165
143 | KI270418.1	2145
144 | KI270424.1	2140
145 | KI270417.1	2043
146 | KI270508.1	1951
147 | KI270303.1	1942
148 | KI270381.1	1930
149 | KI270529.1	1899
150 | KI270425.1	1884
151 | KI270396.1	1880
152 | KI270363.1	1803
153 | KI270386.1	1788
154 | KI270465.1	1774
155 | KI270383.1	1750
156 | KI270384.1	1658
157 | KI270330.1	1652
158 | KI270372.1	1650
159 | KI270548.1	1599
160 | KI270580.1	1553
161 | KI270387.1	1537
162 | KI270391.1	1484
163 | KI270305.1	1472
164 | KI270373.1	1451
165 | KI270422.1	1445
166 | KI270316.1	1444
167 | KI270340.1	1428
168 | KI270338.1	1428
169 | KI270583.1	1400
170 | KI270334.1	1368
171 | KI270429.1	1361
172 | KI270393.1	1308
173 | KI270516.1	1300
174 | KI270389.1	1298
175 | KI270466.1	1233
176 | KI270388.1	1216
177 | KI270544.1	1202
178 | KI270310.1	1201
179 | KI270412.1	1179
180 | KI270395.1	1143
181 | KI270376.1	1136
182 | KI270337.1	1121
183 | KI270335.1	1048
184 | KI270378.1	1048
185 | KI270379.1	1045
186 | KI270329.1	1040
187 | KI270419.1	1029
188 | KI270336.1	1026
189 | KI270312.1	998
190 | KI270539.1	993
191 | KI270385.1	990
192 | KI270423.1	981
193 | KI270392.1	971
194 | KI270394.1	970
195 | 


--------------------------------------------------------------------------------
/metaprofiles.md:
--------------------------------------------------------------------------------
  1 | ## This bash script provides a means of generating strand-specific metagene, TSS and TES profiles from a BAM file using "ngs.plot".
  2 | 
  3 | *Of course, this will only work if your libraries were created in a strand-specific fashion.*
  4 | 
  5 | Shen, L.*, Shao, N., Liu, X. and Nestler, E. (2014).<br>
  6 | ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases, BMC Genomics, 15, 284.<br>
  7 | https://github.com/shenlab-sinai/ngsplot<br>
  8 | 
  9 | Dependencies:<br>
 10 | SAMtools     http://samtools.sourceforge.net/<br>
 11 | ngs.plot     https://github.com/shenlab-sinai/ngsplot<br>
 12 | 
 13 | ---
 14 | 
 15 | 
 16 | #### Set working, temporary and results directories.
 17 | ```bash
 18 | WORKDIR="/path/to/my/working_directory/"
 19 | TMPDIR="${WORKDIR}tmp/"
 20 | PROFDIR="${WORKDIR}metaprofiles/"
 21 | mkdir -p $TMPDIR
 22 | mkdir -p $PROFDIR
 23 | ```
 24 | 
 25 | 
 26 | #### Sample information: sample name and BAM file location.
 27 | ```bash
 28 | SAMPLE="WT";
 29 | BAM="${WORKDIR}WT.bam"
 30 | MATE1="${WORKDIR}WT.mate1.bam"
 31 | ```
 32 | 
 33 | 
 34 | #### Threads - set to take advantage of multi-threading and speed things up.
 35 | ```bash
 36 | THREADS=1
 37 | ```
 38 | 
 39 | #### Restict to first mate reads.
 40 | If the BAM file contains paired reads, create a new version containing only the first mate reads.<br>
 41 | This step may be skipped if your reads are not paired.<br>
 42 | ```bash
 43 | samtools view --threads $THREADS -h -b -f 64 $BAM -o $MATE1
 44 | samtools index $MATE1
 45 | ```
 46 | 
 47 | 
 48 | #### BAM header compliance.
 49 | It might be necessary to re-header the BAM file so that the chromosome names match those in the ngs.plot database, e.g. standard chromosomes preceeded with "chr" for hg38.  This is most easily achieved using SAMtools "reheader" function.<br>
 50 | For human hg38 Ensembl alignments the following steps should do the job.<br>
 51 | This step may be skipped if your header is already compliant.<br>
 52 | ```bash
 53 | MATE1REHEADER="${WORKDIR}WT.mate1.reheader.bam"
 54 | samtools view --threads $THREADS -H ${MATE1} | sed -e 's/SN:\([0-9XY]*\)/SN:chr\1/' -e 's/SN:MT/SN:chrM/' | samtools reheader - ${MATE1} > ${MATE1REHEADER}
 55 | samtools index ${MATE1REHEADER}
 56 | ```
 57 | 
 58 | 
 59 | #### Run ngs.plot.
 60 | Finally, use ngs.plot to create sense and anti-sense profiles for your regions of interest using the correct BAM file.
 61 | Note that if your libraries were produced such that mate2 represents the forward strand the sense/anti-sense profiles will be reversed.
 62 | ```bash
 63 | ## Genebody
 64 | REGION="genebody"
 65 | for STRAND in both same opposite
 66 | do
 67 |     OUTPUT="${PROFDIR}${SAMPLE}.${REGION}.${STRAND}"
 68 |     ngs.plot.r -G hg38 -R $REGION -C ${MATE1REHEADER} -O $OUTPUT -P $THREADS -SS $STRAND -SE 1 -L 5000 -F chipseq -D ensembl
 69 | done
 70 | 
 71 | ## TSS
 72 | REGION="tss"
 73 | for STRAND in both same opposite
 74 | do
 75 |     OUTPUT="${PROFDIR}${SAMPLE}.${REGION}.${STRAND}"
 76 |     ngs.plot.r -G hg38 -R $REGION -C ${MATE1REHEADER} -O $OUTPUT -P $THREADS -SS $STRAND -SE 1 -L 5000 -F chipseq -D ensembl
 77 | done
 78 | 
 79 | ## TES
 80 | REGION="tes"
 81 | for STRAND in both same opposite
 82 | do
 83 |     OUTPUT="${PROFDIR}${SAMPLE}.${REGION}.${STRAND}"
 84 |     ngs.plot.r -G hg38 -R $REGION -C ${MATE1REHEADER} -O $OUTPUT -P $THREADS -SS $STRAND -SE 1 -L 5000 -F chipseq -D ensembl
 85 | done
 86 | ```
 87 | 
 88 | 
 89 | #### Combining sense and anti-sense profiles
 90 | By default ngs.plot produces average profiles in .pdf format.  There is also a zip file containing the underlying data,  This may be used to combine the sense and anti-sense profiles on a single set of axes.  This is best achieved using R.
 91 | 
 92 | 
 93 | #### Scaling the profiles
 94 | A scale factor may be used to normalise for differences in library sizes across samples.  There are many ways to generate such a factor, such as the ratio of reads between two samples of spike-ins.  A more robust scale factor may be calculated using the "estimateSizeFactors" function from the Bioconductor package DESeq2 using sample gene count information.<br>
 95 | 
 96 | The scale factor may be used to modify the number of valid reads discovered in the BAM file given in the resulting ".cnt" file.  Edit this file to multiply read count by your scale factor and re-run ngs.plot using the same parameters.
 97 | 
 98 | 
 99 | #### Custom genes / intervals
100 | Profiles may be restricted to specific gene sets and also to user-defined intervals.  See the ngs.plot documentation for further details.<br>
101 | https://github.com/shenlab-sinai/ngsplot<br>
102 | https://github.com/shenlab-sinai/ngsplot/wiki/ProgramArguments101<br>
103 | 


--------------------------------------------------------------------------------
/scripts/DRB-TTseq.R:
--------------------------------------------------------------------------------
  1 | #This is a companion script to the publication below.  It describes a pipeline for calling RNA Pol II transcription wave peak positions and elongation rates from DRB/TT-seq time-series data using R.  Instructions are given for calculating wave peaks at both the single-gene and meta-gene level.  
  2 |   
  3 | 
  4 | # Nascent transcriptome profiles and measurement of transcription elongation using TT-seq.
  5 | # Lea H. Gregersen (1) Richard Mitter (2) and Jesper Q. Svejstrup (1)
  6 | # (1) Mechanisms of Transcription Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
  7 | # (2) Bioinformatics and Biostatistics, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
  8 | 
  9 | ### Define genic intervals and calculate coverage directly from BAM files.
 10 | 
 11 | library(Rsamtools)
 12 | library(GenomicRanges)
 13 | library(rtracklayer)
 14 | library(GenomicFeatures)
 15 | library(bamsignals)
 16 | library(reshape2)
 17 | library(ggplot2)
 18 | library(DT)
 19 | 
 20 | 
 21 | 
 22 | work.dir <- "/path/to/my/working_directory/"
 23 | setwd(work.dir)
 24 | 
 25 | 
 26 | # Create a data.frame detailing BAM files and sample names and any pertinent meta-data such as DRB release time.
 27 | drb.df <- data.frame(
 28 |   name = c("DRB.10min","DRB.20min","DRB.30min","DRB.40min"),
 29 |   bam  = c("bam/DRB.10min.bam","bam/DRB.20min.bam","bam/DRB.30min.bam","bam/DRB.40min.bam"),
 30 |   time = c(10,20,30,40),
 31 |   stringsAsFactors=F)
 32 | drb.df <- drb.df[order(drb.df$time),]
 33 | 
 34 | 
 35 | # Genome transcriptome GTF files are available for download from [Ensembl](http://www.ensembl.org/index.html).  The GTF file may be indexed using [igvtools](https://software.broadinstitute.org/software/igv/igvtools_commandline) *index* command. Make sure that the assembly matches the one used for read alignments.
 36 | gtf.file     <- "data/Homo_sapiens.GRCh38.86.gtf"
 37 | 
 38 | 
 39 | # Filters genes to remove any that are overly short, overly long, any non-coding genes any overlapping another gene and any that map to non-standard chromosomes.
 40 | gtf.dat <- import(gtf.file)
 41 | gene.gr <- gtf.dat[gtf.dat$type %in% "gene",]
 42 | names(gene.gr) <- gene.gr$gene_id
 43 | gene.gr$gene.width <- width(gene.gr)
 44 | 
 45 | gene.gr <- gene.gr[width(gene.gr) >= 60000 & width(gene.gr) < 300000]
 46 | gene.gr <- gene.gr[gene.gr$gene_biotype %in% "protein_coding"]
 47 | gene.gr <- gene.gr[countOverlaps(gene.gr,gene.gr) == 1]
 48 | gene.gr <- keepSeqlevels(gene.gr,c(as.character(1:22),"X","Y"),pruning.mode="coarse")
 49 | 
 50 | 
 51 | # Create GRanges object representing filtered genes -2kb:+120kb around their promoters.
 52 | up.ext  <- 2000
 53 | dn.ext  <- 120000
 54 | intervals.gr <- promoters(gene.gr, upstream=up.ext, downstream=dn.ext)
 55 | intervals.gr <- trim(intervals.gr)
 56 | intervals.gr <- intervals.gr[width(intervals.gr) == (up.ext+dn.ext)]
 57 | 
 58 | 
 59 | # Calculate bp-level coverage over the extended gene intervals.
 60 | # Currently this is not strand-specific as "bamCoverage" doesn't allow it, but filtering out overlapping genes should take care of most of the problems.  
 61 | # Alternatively, one could filter the bam file by strand prior to calculating coverage.
 62 | sigs.list <- list()
 63 | for (r in 1:nrow(drb.df)) {
 64 |     sigs <- bamCoverage(
 65 |         bampath                    = drb.df$bam[r],
 66 |         gr                         = intervals.gr,
 67 |         filteredFlag               = 1024,  # remove duplicates
 68 |         paired.end                 = "ignore",
 69 |         mapq                       = 20,
 70 |         verbose                    = FALSE)
 71 |     sigs.list[[drb.df$name[r]]] <- t(alignSignals(sigs))
 72 |     rownames(sigs.list[[drb.df$name[r]]]) <- names(intervals.gr)
 73 | }
 74 | 
 75 | 
 76 | # Scale to Read counts Per Million (RPM).
 77 | read.length <- 75
 78 | rpm.list   <- list()
 79 | for (n in 1:length(sigs.list)) {
 80 |     n.sig <- sigs.list[[n]] / read.length
 81 |     n.sum <- sum(n.sig)
 82 |     n.sf  <- n.sum / 1000000
 83 |     rpm.list[[names(sigs.list)[n]]] <- n.sig / n.sf
 84 | }
 85 | 
 86 | 
 87 | ### Meta-gene level wave peak calling.
 88 | 
 89 | # Create meta-profiles by taking a trimmed mean.
 90 | meta.dat <- sapply(rpm.list,function(x){ apply(x,2,function(y) { mean(y,na.rm=T,trim=0.01) }) })
 91 | 
 92 | 
 93 | # Fit a smoothing spline to each meta-profile.
 94 | # The spar parameter might need tweaking depending on the fit
 95 | spline.dat <- t(apply(meta.dat,2,function(x){smooth.spline(1:length(x),x,spar=0.9)$y }))
 96 | 
 97 | 
 98 | # Plot the meta-profiles.
 99 | 
100 | # Coverage
101 | cov.plot <- melt(meta.dat)
102 | colnames(cov.plot) <- c("position","name","RPM")
103 | cov.plot$position  <- cov.plot$position-up.ext-1
104 | cov.plot$time      <- drb.df$time[match(cov.plot$name,drb.df$name)]
105 | 
106 | # Spline
107 | spline.plot <- melt(t(spline.dat))
108 | colnames(spline.plot) <- c("position","name","RPM")
109 | spline.plot$position  <- spline.plot$position-up.ext-1
110 | spline.plot$time      <- drb.df$time[match(spline.plot$name,drb.df$name)]
111 | 
112 | P1 <- ggplot(cov.plot,aes(x=position,y=RPM,colour=name)) + geom_line(alpha=0.4)
113 | P1 <- P1 + geom_line(aes(x=position,y=RPM,colour=name),spline.plot,size=2)
114 | P1 <- P1 + xlab("Position relative to TSS (kb)") + ylab("RPM") + ggtitle("DRB/TT-seq coverage")
115 | P1 <- P1 + scale_x_continuous(breaks=c(0,40000,80000,120000),label=c("TSS","40kb","80kb","120kb"))
116 | #P1 <- P1 + scale_colour_manual(values=c("DRB.10min"="#231F20", "DRB.20min"="#58595B", "DRB.30min"="#A7A9AC", "DRB.40min"="#D1D3D4"))
117 | #ggsave(filename="results/DRB_metaprofile.png",plot=P1,device="png",height=5)
118 | P1
119 | 
120 | 
121 | # Calculate wave peaks from meta-profiles as the maximum point on the spline.
122 | # Maxima are only called after the preceeding timepoint's maximum position, forcing the wave to advance with time.
123 | wf.dat <- drb.df[,c("name","time")]
124 | wf.dat$wave <- 0
125 | for (r in 1:nrow(wf.dat)) {
126 |   present.sample <- wf.dat$name[r]
127 |   if (r==1) {
128 |     previous.wf <- 0
129 |     wf.dat$wave[r] <- which.max(spline.dat[present.sample,])
130 |   } else {
131 |     previous.wf    <- wf.dat$wave[r-1]
132 |     wf.dat$wave[r] <- which.max(spline.dat[present.sample,-1:-previous.wf])+previous.wf
133 |   }
134 | }
135 | wf.dat$wave <- wf.dat$wave - 2001
136 | wf.dat$wave <- wf.dat$wave / 1000
137 | 
138 | # Optionally add an additional time=0 datapoint which assumes a wave peak at position=0.
139 | wf.dat <- data.frame(rbind(c("DRB.0min",0,0),wf.dat),stringsAsFactors=F)
140 | wf.dat$time <- as.numeric(wf.dat$time)
141 | wf.dat$wave <- as.numeric(wf.dat$wave)
142 | 
143 | 
144 | # Wave peaks calculated from the meta-gene profiles.
145 | wf.dat
146 | #datatable(wf.dat,rownames=FALSE)
147 | 
148 | 
149 | # Fit a linear model to the wave peak positions as a function of time to determine the rate of elongation, kb/min.
150 | lm.fit <- lm(wf.dat$wave~wf.dat$time)
151 | elongation.rate <- lm.fit$coefficients[2]
152 | P2 <- ggplot(wf.dat,aes(x=time,y=wave)) + geom_point(size=4)
153 | P2 <- P2 + xlab("Time (min)") + ylab("Wave position (kb)")
154 | P2 <- P2 + geom_abline(intercept = lm.fit$coefficients[1], slope = lm.fit$coefficients[2])
155 | P2 <- P2 + annotate(geom="text", x=10, y=75, label=paste("y = ",round(lm.fit$coefficients[2],2),"x",round(lm.fit$coefficients[1],2),sep=''))
156 | #ggsave(filename="results/DRB_metaprofile_elongation_rate.png",plot=P2,device="png")
157 | P2
158 | 
159 | 
160 | ### Single gene level wave peak calling
161 | 
162 | # Generate a set of gene ids that pass an arbitrary expression threshold.
163 | expr.mat  <- sapply(rpm.list,function(x){apply(x,1,function(y){sum(y,na.rm=T)})})
164 | expr.plot <- ggplot(melt(expr.mat),aes(x=log2(value+0.1),fill=Var2)) + geom_histogram(bins=50)
165 | expr.plot <- expr.plot + geom_vline(aes(xintercept=log2(100+1)),colour="darkred") + facet_grid(Var2~.) + xlab("log2(RPM+0.1)") + ylab("frequency") + ggtitle("Expression filter")
166 | expr.gids <- rownames(expr.mat)[rowSums(expr.mat > 100) == ncol(expr.mat)]
167 | #ggsave(filename="results/DRB_expression_filter.png",plot=expr.plot ,device="png")
168 | expr.plot
169 | 
170 | 
171 | # Fit a smoothing spline over the RPM data for each gene for each sample.
172 | spline.list <- list()
173 | for (n in 1:length(rpm.list)) {
174 |     spline.dat <- t(apply(rpm.list[[n]],1,function(x){smooth.spline(1:length(x),x,spar=0.9)$y }))
175 |     spline.list[[names(rpm.list)[n]]] <- spline.dat
176 | }
177 | 
178 | 
179 | # Calculate wave peak for each gene as the maximum point on the spline.
180 | wf.genes <- sapply(spline.list,function(x){ apply(x,1,function(y){which.max(y)}) })
181 | rownames(wf.genes) <- rownames(spline.list[[1]])
182 | 
183 | 
184 | # Filter the gene level wave peak predictions.  Remove any genes that are lowly expressed, have missng values, have duplicate values or whose wave doesn't advance with time.  Select only genes with a wave-peak after the first 2 kb in the 10min sample.
185 | wf.genes.filt <- wf.genes[expr.gids,c("DRB.10min","DRB.20min","DRB.30min","DRB.40min")]
186 | wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(is.na(x))}),]
187 | wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(duplicated(x))}),]
188 | wf.genes.filt <- wf.genes.filt[apply(wf.genes.filt,1,function(x){all(order(x)==1:nrow(drb.df))}),]
189 | wf.genes.filt <- wf.genes.filt[wf.genes.filt[,"DRB.10min"]>2000,]
190 | 
191 | 
192 | # Sometimes it is necessary to disregard the final timepoint when generating the filter as transcription might have already reached the end of the gene at that point.  This version only uses the first three timepoints.
193 | # wf.genes.filt <- wf.genes[expr.gids,c("DRB.10min","DRB.20min","DRB.30min")]
194 | # wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(is.na(x))}),]
195 | # wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(duplicated(x))}),]
196 | # wf.genes.filt <- wf.genes.filt[apply(wf.genes.filt,1,function(x){all(order(x)==1:3)}),]
197 | # wf.genes.filt <- wf.genes.filt[wf.genes.filt[,"DRB.10min"]>2000,]
198 | 
199 | 
200 | # Compile results.
201 | wf.genes.dBase <- data.frame(
202 |     gene_id     = rownames(wf.genes),
203 |     gene_name   = intervals.gr[rownames(wf.genes),]$gene_id,
204 |     gene_width  = intervals.gr[rownames(wf.genes),]$gene.width,
205 |     mean.rpkm   = rowMeans(expr.mat[rownames(wf.genes),]),
206 |     wf.order    = apply(wf.genes[rownames(wf.genes),drb.df$name],1,function(x){paste(order(x),sep='',collapse='')}),
207 |     wf.genes,
208 |     filter      = rownames(wf.genes) %in% rownames(wf.genes.filt),
209 |     stringsAsFactors=F,
210 |     check.names=F)
211 | 
212 | 
213 | # Fit a linear model to the wave peak positions as a function of time to determine the rate of elongation, kb/min.
214 | lm.dat <- wf.genes.dBase[,drb.df$name]
215 | wf.genes.dBase$elongation.rate <- apply(lm.dat,1,function(x) {
216 |   time <- c(0,drb.df$time)
217 |   wf   <- c(0,x)/1000
218 |   lm(wf~time)$coef[2]
219 | })
220 | 
221 | 
222 | # Plot a distribution of elongation rates for genes passing the filter.
223 | single.elong_rate.dat <- data.frame(wf.genes.dBase[wf.genes.dBase$filter,c("gene_name","elongation.rate")],stringsAsFactors=F)
224 | P3 <- ggplot(single.elong_rate.dat,aes(x=elongation.rate)) + geom_histogram(alpha=0.5,colour="#7CAE00",fill="#7CAE00")
225 | P3 <- P3 + ylab("frequency") + xlab("Elongation rate (kb/min)") + ggtitle(paste("Elongation rate, n=",nrow(single.elong_rate.dat),sep=''))
226 | #ggsave(filename="results/DRB_elongation_rate_distribution.png",plot=P3,device="png")
227 | P3
228 | 


--------------------------------------------------------------------------------
/scripts/DRB-TTseq.Rmd:
--------------------------------------------------------------------------------
  1 | ---
  2 | title: "DRB/TT-seq analysis in R."
  3 | author: "Richard Mitter"
  4 | output:
  5 |   html_document:
  6 |     theme: united
  7 | date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
  8 | ---
  9 | 
 10 | <style>
 11 | body {
 12 | text-align: justify}
 13 | </style>
 14 | 
 15 | 
 16 | ```{r, include=FALSE}
 17 | knitr::opts_chunk$set(
 18 |   cache      = TRUE,
 19 |   cache.lazy = FALSE,
 20 |   tidy       = FALSE,
 21 |   include    = TRUE,
 22 |   fig.width  = 7,
 23 |   fig.height = 7,
 24 |   fig.align  = 'center',
 25 |   echo       = TRUE,
 26 |   warning    = FALSE,
 27 |   message    = FALSE
 28 | )
 29 | ```
 30 | 
 31 | 
 32 | ***
 33 | 
 34 | This is a companion script to the publication below.  It describes a pipeline for calling RNA Pol II transcription wave peak positions and elongation rates from DRB/TT-seq time-series data using R.  Instructions are given for calculating wave peaks at both the single-gene and meta-gene level.  
 35 |   
 36 | 
 37 | *Nascent transcriptome profiles and measurement of transcription elongation using TT-seq.*
 38 | *Lea H. Gregersen^1^ Richard Mitter^2^ and Jesper Q. Svejstrup^1^^\*^*
 39 | *^1^Mechanisms of Transcription Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK  *
 40 | *^2^Bioinformatics and Biostatistics, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK  *
 41 | 
 42 | ***
 43 | 
 44 | ### Define genic intervals and calculate coverage directly from BAM files.
 45 | 
 46 | ```{r library}
 47 | library(Rsamtools)
 48 | library(GenomicRanges)
 49 | library(rtracklayer)
 50 | library(GenomicFeatures)
 51 | library(bamsignals)
 52 | library(reshape2)
 53 | library(ggplot2)
 54 | library(DT)
 55 | ```
 56 | 
 57 | 
 58 | 
 59 | ```{r work_dir, include=FALSE}
 60 | work.dir <- "/path/to/my/working_directory/"
 61 | setwd(work.dir)
 62 | ```
 63 | 
 64 | 
 65 | Create a data.frame detailing BAM files and sample names and any pertinent meta-data such as DRB release time.
 66 | ```{r, drb.drb.df}
 67 | drb.df <- data.frame(
 68 |   name = c("DRB.10min","DRB.20min","DRB.30min","DRB.40min"),
 69 |   bam  = c("bam/DRB.10min.bam","bam/DRB.20min.bam","bam/DRB.30min.bam","bam/DRB.40min.bam"),
 70 |   time = c(10,20,30,40),
 71 |   stringsAsFactors=F)
 72 | drb.df <- drb.df[order(drb.df$time),]
 73 | ```
 74 | 
 75 | 
 76 | Genome transcriptome GTF files are available for download from [Ensembl](http://www.ensembl.org/index.html).  The GTF file may be indexed using [igvtools](https://software.broadinstitute.org/software/igv/igvtools_commandline) *index* command. Make sure that the assembly matches the one used for read alignments.
 77 | ```{r drb.reference_data}
 78 | gtf.file     <- "data/Homo_sapiens.GRCh38.86.gtf"
 79 | ```
 80 | 
 81 | 
 82 | Filters genes to remove any that are overly short, overly long, any non-coding genes any overlapping another gene and any that map to non-standard chromosomes.
 83 | ```{r annotation}
 84 | gtf.dat <- import(gtf.file)
 85 | gene.gr <- gtf.dat[gtf.dat$type %in% "gene",]
 86 | names(gene.gr) <- gene.gr$gene_id
 87 | gene.gr$gene.width <- width(gene.gr)
 88 | 
 89 | gene.gr <- gene.gr[width(gene.gr) >= 60000 & width(gene.gr) < 300000]
 90 | gene.gr <- gene.gr[gene.gr$gene_biotype %in% "protein_coding"]
 91 | gene.gr <- gene.gr[countOverlaps(gene.gr,gene.gr) == 1]
 92 | gene.gr <- keepSeqlevels(gene.gr,c(as.character(1:22),"X","Y"),pruning.mode="coarse")
 93 | ```
 94 | 
 95 | 
 96 | Create GRanges object representing filtered genes -2kb:+120kb around their promoters.
 97 | ```{r drb.intervals}
 98 | up.ext  <- 2000
 99 | dn.ext  <- 120000
100 | intervals.gr <- promoters(gene.gr, upstream=up.ext, downstream=dn.ext)
101 | intervals.gr <- trim(intervals.gr)
102 | intervals.gr <- intervals.gr[width(intervals.gr) == (up.ext+dn.ext)]
103 | ```
104 | 
105 | 
106 | Calculate bp-level coverage over the extended gene intervals.
107 | ```{r drb.coverage}
108 | # Currently this is not strand-specific as "bamCoverage" doesn't allow it, but filtering out overlapping genes should take care of most of the problems.  
109 | # Alternatively, one could filter the bam file by strand prior to calculating coverage.
110 | sigs.list <- list()
111 | for (r in 1:nrow(drb.df)) {
112 |     sigs <- bamCoverage(
113 |         bampath                    = drb.df$bam[r],
114 |         gr                         = intervals.gr,
115 |         filteredFlag               = 1024,  # remove duplicates
116 |         paired.end                 = "ignore",
117 |         mapq                       = 20,
118 |         verbose                    = FALSE)
119 |     sigs.list[[drb.df$name[r]]] <- t(alignSignals(sigs))
120 |     rownames(sigs.list[[drb.df$name[r]]]) <- names(intervals.gr)
121 | }
122 | ```
123 | 
124 | 
125 | Scale to Read counts Per Million (RPM).
126 | ```{r drb.RPM}
127 | read.length <- 75
128 | rpm.list   <- list()
129 | for (n in 1:length(sigs.list)) {
130 |     n.sig <- sigs.list[[n]] / read.length
131 |     n.sum <- sum(n.sig)
132 |     n.sf  <- n.sum / 1000000
133 |     rpm.list[[names(sigs.list)[n]]] <- n.sig / n.sf
134 | }
135 | ```
136 | 
137 | ***
138 | 
139 | ### Meta-gene level wave peak calling.
140 | 
141 | Create meta-profiles by taking a trimmed mean.
142 | ```{r drb.mean}
143 | meta.dat <- sapply(rpm.list,function(x){ apply(x,2,function(y) { mean(y,na.rm=T,trim=0.01) }) })
144 | ```
145 | 
146 | 
147 | Fit a smoothing spline to each meta-profile.
148 | ```{r drb.spline}
149 | # The spar parameter might need tweaking depending on the fit
150 | spline.dat <- t(apply(meta.dat,2,function(x){smooth.spline(1:length(x),x,spar=0.9)$y }))
151 | ```
152 | 
153 | 
154 | Plot the meta-profiles.
155 | ```{r drb.metaplot, fig.height=5, fig.width=10}
156 | # Coverage
157 | cov.plot <- melt(meta.dat)
158 | colnames(cov.plot) <- c("position","name","RPM")
159 | cov.plot$position  <- cov.plot$position-up.ext-1
160 | cov.plot$time      <- drb.df$time[match(cov.plot$name,drb.df$name)]
161 | 
162 | # Spline
163 | spline.plot <- melt(t(spline.dat))
164 | colnames(spline.plot) <- c("position","name","RPM")
165 | spline.plot$position  <- spline.plot$position-up.ext-1
166 | spline.plot$time      <- drb.df$time[match(spline.plot$name,drb.df$name)]
167 | 
168 | P1 <- ggplot(cov.plot,aes(x=position,y=RPM,colour=name)) + geom_line(alpha=0.4)
169 | P1 <- P1 + geom_line(aes(x=position,y=RPM,colour=name),spline.plot,size=2)
170 | P1 <- P1 + xlab("Position relative to TSS (kb)") + ylab("RPM") + ggtitle("DRB/TT-seq coverage")
171 | P1 <- P1 + scale_x_continuous(breaks=c(0,40000,80000,120000),label=c("TSS","40kb","80kb","120kb"))
172 | #P1 <- P1 + scale_colour_manual(values=c("DRB.10min"="#231F20", "DRB.20min"="#58595B", "DRB.30min"="#A7A9AC", "DRB.40min"="#D1D3D4"))
173 | #ggsave(filename="results/DRB_metaprofile.png",plot=P1,device="png",height=5)
174 | P1
175 | ```
176 | 
177 | 
178 | Calculate wave peaks from meta-profiles as the maximum point on the spline.
179 | Maxima are only called after the preceeding timepoint's maximum position, forcing the wave to advance with time.
180 | ```{r drb.meta_wave}
181 | wf.dat <- drb.df[,c("name","time")]
182 | wf.dat$wave <- 0
183 | for (r in 1:nrow(wf.dat)) {
184 |   present.sample <- wf.dat$name[r]
185 |   if (r==1) {
186 |     previous.wf <- 0
187 |     wf.dat$wave[r] <- which.max(spline.dat[present.sample,])
188 |   } else {
189 |     previous.wf    <- wf.dat$wave[r-1]
190 |     wf.dat$wave[r] <- which.max(spline.dat[present.sample,-1:-previous.wf])+previous.wf
191 |   }
192 | }
193 | wf.dat$wave <- wf.dat$wave - 2001
194 | wf.dat$wave <- wf.dat$wave / 1000
195 | 
196 | # Optionally add an additional time=0 datapoint which assumes a wave peak at position=0.
197 | wf.dat <- data.frame(rbind(c("DRB.0min",0,0),wf.dat),stringsAsFactors=F)
198 | wf.dat$time <- as.numeric(wf.dat$time)
199 | wf.dat$wave <- as.numeric(wf.dat$wave)
200 | ```
201 | 
202 | 
203 | Wave peaks calculated from the meta-gene profiles.
204 | ```{r, dt}
205 | datatable(wf.dat,rownames=FALSE)
206 | ```
207 | 
208 | 
209 | Fit a linear model to the wave peak positions as a function of time to determine the rate of elongation, kb/min.
210 | ```{r, drb.meta_elongationrate}
211 | lm.fit <- lm(wf.dat$wave~wf.dat$time)
212 | elongation.rate <- lm.fit$coefficients[2]
213 | P2 <- ggplot(wf.dat,aes(x=time,y=wave)) + geom_point(size=4)
214 | P2 <- P2 + xlab("Time (min)") + ylab("Wave position (kb)")
215 | P2 <- P2 + geom_abline(intercept = lm.fit$coefficients[1], slope = lm.fit$coefficients[2])
216 | P2 <- P2 + annotate(geom="text", x=10, y=75, label=paste("y = ",round(lm.fit$coefficients[2],2),"x",round(lm.fit$coefficients[1],2),sep=''))
217 | #ggsave(filename="results/DRB_metaprofile_elongation_rate.png",plot=P2,device="png")
218 | P2
219 | ```
220 | 
221 | 
222 | ***
223 | 
224 | ### Single gene level wave peak calling
225 | 
226 | Generate a set of gene ids that pass an arbitrary expression threshold.
227 | ```{r, drb.exprfilt}
228 | expr.mat  <- sapply(rpm.list,function(x){apply(x,1,function(y){sum(y,na.rm=T)})})
229 | expr.plot <- ggplot(melt(expr.mat),aes(x=log2(value+0.1),fill=Var2)) + geom_histogram(bins=50)
230 | expr.plot <- expr.plot + geom_vline(aes(xintercept=log2(100+1)),colour="darkred") + facet_grid(Var2~.) + xlab("log2(RPM+0.1)") + ylab("frequency") + ggtitle("Expression filter")
231 | expr.gids <- rownames(expr.mat)[rowSums(expr.mat > 100) == ncol(expr.mat)]
232 | #ggsave(filename="results/DRB_expression_filter.png",plot=expr.plot ,device="png")
233 | expr.plot
234 | ```
235 | 
236 | 
237 | Fit a smoothing spline over the RPM data for each gene for each sample.
238 | ```{r drb.single_splines}
239 | spline.list <- list()
240 | for (n in 1:length(rpm.list)) {
241 |     spline.dat <- t(apply(rpm.list[[n]],1,function(x){smooth.spline(1:length(x),x,spar=0.9)$y }))
242 |     spline.list[[names(rpm.list)[n]]] <- spline.dat
243 | }
244 | ```
245 | 
246 | 
247 | Calculate wave peak for each gene as the maximum point on the spline.
248 | ```{r drb.single_wave}
249 | wf.genes <- sapply(spline.list,function(x){ apply(x,1,function(y){which.max(y)}) })
250 | rownames(wf.genes) <- rownames(spline.list[[1]])
251 | ```
252 | 
253 | 
254 | Filter the gene level wave peak predictions.  Remove any genes that are lowly expressed, have missng values, have duplicate values or whose wave doesn't advance with time.  Select only genes with a wave-peak after the first 2 kb in the 10min sample.
255 | ```{r drb.single_filt}
256 | wf.genes.filt <- wf.genes[expr.gids,c("DRB.10min","DRB.20min","DRB.30min","DRB.40min")]
257 | wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(is.na(x))}),]
258 | wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(duplicated(x))}),]
259 | wf.genes.filt <- wf.genes.filt[apply(wf.genes.filt,1,function(x){all(order(x)==1:nrow(drb.df))}),]
260 | wf.genes.filt <- wf.genes.filt[wf.genes.filt[,"DRB.10min"]>2000,]
261 | 
262 | ```
263 | 
264 | 
265 | Sometimes it is necessary to disregard the final timepoint when generating the filter as transcription might have already reached the end of the gene at that point.  This version only uses the first three timepoints.
266 | ```{r drb.single_filt.alternative}
267 | # wf.genes.filt <- wf.genes[expr.gids,c("DRB.10min","DRB.20min","DRB.30min")]
268 | # wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(is.na(x))}),]
269 | # wf.genes.filt <- wf.genes.filt[!apply(wf.genes.filt,1,function(x){any(duplicated(x))}),]
270 | # wf.genes.filt <- wf.genes.filt[apply(wf.genes.filt,1,function(x){all(order(x)==1:3)}),]
271 | # wf.genes.filt <- wf.genes.filt[wf.genes.filt[,"DRB.10min"]>2000,]
272 | 
273 | ```
274 | 
275 | 
276 | Compile results.
277 | ```{r, spreadsheet}
278 | wf.genes.dBase <- data.frame(
279 |     gene_id     = rownames(wf.genes),
280 |     gene_name   = intervals.gr[rownames(wf.genes),]$gene_id,
281 |     gene_width  = intervals.gr[rownames(wf.genes),]$gene.width,
282 |     mean.rpkm   = rowMeans(expr.mat[rownames(wf.genes),]),
283 |     wf.order    = apply(wf.genes[rownames(wf.genes),drb.df$name],1,function(x){paste(order(x),sep='',collapse='')}),
284 |     wf.genes,
285 |     filter      = rownames(wf.genes) %in% rownames(wf.genes.filt),
286 |     stringsAsFactors=F,
287 |     check.names=F)
288 | ```
289 | 
290 | 
291 | Fit a linear model to the wave peak positions as a function of time to determine the rate of elongation, kb/min.
292 | ```{r, drb.linfit}
293 | lm.dat <- wf.genes.dBase[,drb.df$name]
294 | wf.genes.dBase$elongation.rate <- apply(lm.dat,1,function(x) {
295 |   time <- c(0,drb.df$time)
296 |   wf   <- c(0,x)/1000
297 |   lm(wf~time)$coef[2]
298 | })
299 | ```
300 | 
301 | 
302 | Plot a distribution of elongation rates for genes passing the filter.
303 | ```{r, drb.single_elongationrate}
304 | single.elong_rate.dat <- data.frame(wf.genes.dBase[wf.genes.dBase$filter,c("gene_name","elongation.rate")],stringsAsFactors=F)
305 | P3 <- ggplot(single.elong_rate.dat,aes(x=elongation.rate)) + geom_histogram(alpha=0.5,colour="#7CAE00",fill="#7CAE00")
306 | P3 <- P3 + ylab("frequency") + xlab("Elongation rate (kb/min)") + ggtitle(paste("Elongation rate, n=",nrow(single.elong_rate.dat),sep=''))
307 | #ggsave(filename="results/DRB_elongation_rate_distribution.png",plot=P3,device="png")
308 | P3
309 | ```
310 | 
311 | 
312 | ***
313 | 
314 | ```{r session.info}
315 | sessionInfo()
316 | ```
317 | 


--------------------------------------------------------------------------------
/scripts/align.sh:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/bash
 2 | 
 3 | # ## This bash script provides instruction for aligning paired Illumina sequence reads against a reference genome using STAR.
 4 | # 
 5 | # In this example a *S. cerevisiae* RNA spike-in was inserted into the human RNA sample prior to sequencing.  Spike-in abundance was estimated by a separate mapping of the sequence reads to the yeast genome using STAR.  However, creating a composite human/yeast genome and aligning to that or using an alignment free abundance estimator such as [kallisto](https://pachterlab.github.io/kallisto/) are also valid options.
 6 | # 
 7 | # The purpose of the spike-in is to generate a scale-factor to account for differences in library size between multiple samples.  A scale factor may be as simple as a ratio of mapped spike-in reads between two samples.  A more robust scale factor may be calculated across multiple samples using the "estimateSizeFactors" function from the [Bioconductor](https://bioconductor.org/) package [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) using sample specific spike-in gene count information.
 8 | # 
 9 | # ---
10 | # 
11 | # The output of this script are two sorted, duplicate marked and indexed BAM files - one for the target organism and one for the spike-in.  There are also gene level counts produced by STAR ("*.ReadsPerGene.out.tab") that may be may used to generate scale factors.
12 | # 
13 | # *It is assumed that the FASTQ reads have been checked for quality and any filtering, adapter trimming etc. that might be required has been done prior to running this script.*
14 | # 
15 | # If there are multiple sets of FASTQ files per sample, i.e. more than 1 set of paired reads, it is recommended to align these separately and merge the resultant BAM files using "samtools merge" before continuing with the downstream analysis.
16 | # 
17 | # Marking duplicate reads isn't strictly necessary but the calculated levels of duplication provide insight into sample quality.  Also, counts of unique mapped reads may be useful for scale factor normalisation.
18 | # 
19 | # Dependencies:<br>
20 | # SAMtools     http://samtools.sourceforge.net/<br>
21 | # Picard       https://broadinstitute.github.io/picard/<br>
22 | # STAR         https://github.com/alexdobin/STAR<br>
23 | 
24 | 
25 | #### Set working, temporary and results directories
26 | WORKDIR="/path/to/my/working_directory/"
27 | TMPDIR="${WORKDIR}tmp/"
28 | ALIGNDIR="${WORKDIR}alignments/"
29 | SPIKEDIR="${WORKDIR}alignments_spike/"
30 | mkdir -p $TMPDIR
31 | mkdir -p $ALIGNDIR
32 | mkdir -p $SPIKEDIR
33 | 
34 | 
35 | #### Sample information: sample name and location of paired FASTQ files.
36 | SAMPLE="WT";
37 | FQ1="${WORKDIR}FQ1.fastq.gz"
38 | FQ2="${WORKDIR}FQ2.fastq.gz"
39 | 
40 | 
41 | #### Threads - set to take advantage of multi-threading and speed things up.
42 | THREADS=8
43 | 
44 | 
45 | #### Path to STAR genome indices
46 | # These were created using GRCh38 Ensembl v86 (*Homo sapiens*) and R64-1-1 Ensembl v86 (*Saccharomyces cerevisiae*) genome sequences and GTF files downloaded from the [Ensembl](https://www.ensembl.org/index.html) database.  Please refer to the STAR manual for information on how to create your own genomes indices.
47 | HUMANIDX="/path/to/my/human_genome_index/"
48 | SPIKEIDX="/path/to/my/yeast_genome_index/"
49 | 
50 | 
51 | #### Align to the human genome.  Sort, mark duplicates and index the genome BAM.
52 | cd $ALIGNDIR
53 | STAR --runThreadN ${THREADS} --runMode alignReads --genomeDir ${HUMANIDX} --readFilesIn ${FQ1} ${FQ2} --readFilesCommand zcat --quantMode TranscriptomeSAM GeneCounts --twopassMode Basic --outSAMunmapped None --outSAMattrRGline ID:${SAMPLE} PU:${SAMPLE} SM:${SAMPLE} LB:unknown PL:illumina --outSAMtype BAM Unsorted --outTmpDir ${TMPDIR}${SAMPLE} --outFileNamePrefix ${ALIGNDIR}${SAMPLE}.
54 | samtools sort --threads ${THREADS} -o ${ALIGNDIR}${SAMPLE}.sorted.bam ${ALIGNDIR}${SAMPLE}.Aligned.out.bam
55 | java -jar picard.jar MarkDuplicates INPUT=${ALIGNDIR}${SAMPLE}.sorted.bam OUTPUT=${ALIGNDIR}${SAMPLE}.sorted.marked.bam METRICS_FILE=${ALIGNDIR}${SAMPLE}.sorted.marked.metrics REMOVE_DUPLICATES=false ASSUME_SORTED=true MAX_RECORDS_IN_RAM=2000000 VALIDATION_STRINGENCY=LENIENT TMP_DIR=${TMPDIR}${SAMPLE}
56 | samtools index ${ALIGNDIR}${SAMPLE}.sorted.marked.bam
57 | rm ${ALIGNDIR}${SAMPLE}.sorted.bam
58 | 
59 | #### Align to the yeast genome (spike-in).  Sort, mark duplicates and index the genome BAM.
60 | cd $SPIKEDIR
61 | STAR --runThreadN ${THREADS} --runMode alignReads --genomeDir ${SPIKEIDX} --readFilesIn ${FQ1} ${FQ2} --readFilesCommand zcat --quantMode TranscriptomeSAM GeneCounts --twopassMode Basic --outSAMunmapped None --outSAMattrRGline ID:${SAMPLE} PU:${SAMPLE} SM:${SAMPLE} LB:unknown PL:illumina --outSAMtype BAM Unsorted --outTmpDir ${TMPDIR}${SAMPLE}.spike --outFileNamePrefix ${ALIGNDIR}${SAMPLE}.
62 | samtools sort --threads ${THREADS} -o ${SPIKEDIR}${SAMPLE}.sorted.bam ${SPIKEDIR}${SAMPLE}.Aligned.out.bam
63 | java -jar picard.jar MarkDuplicates INPUT=${SPIKEDIR}${SAMPLE}.sorted.bam OUTPUT=${SPIKEDIR}${SAMPLE}.sorted.marked.bam METRICS_FILE=${SPIKEDIR}${SAMPLE}.sorted.marked.metrics REMOVE_DUPLICATES=false ASSUME_SORTED=true MAX_RECORDS_IN_RAM=2000000 VALIDATION_STRINGENCY=LENIENT TMP_DIR=${TMPDIR}${SAMPLE}
64 | samtools index ${SPIKEDIR}${SAMPLE}.sorted.marked.bam 
65 | rm ${SPIKEDIR}${SAMPLE}.sorted.bam
66 | 


--------------------------------------------------------------------------------
/scripts/bigwig.sh:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/bash
 2 | 
 3 | ## This bash script provides a means of generating scaled strand-specific BIGWIG files from a BAM file containing paired reads.
 4 | 
 5 | # Adapted from:<br>
 6 | # *Ramírez, Fidel, Devon P. Ryan, Björn Grüning, Vivek Bhardwaj, Fabian Kilpert, Andreas S. Richter, Steffen Heyne, Friederike Dündar, and Thomas Manke.*<br>
 7 | # *deepTools2: A next Generation Web Server for Deep-Sequencing Data Analysis. Nucleic Acids Research (2016). doi:10.1093/nar/gkw257.*<br>
 8 | # *https://deeptools.readthedocs.io/en/develop/content/tools/bamCoverage.html*<gr>
 9 | 
10 | # Dependencies:<br>
11 | #     SAMtools     http://samtools.sourceforge.net/<br>
12 | #     deepTools    https://deeptools.readthedocs.io/en/develop/index.html<br>
13 | 
14 | 
15 | #### Set working, temporary and results directories.
16 | WORKDIR="/path/to/my/working_directory/"
17 | TMPDIR="${WORKDIR}tmp/"
18 | BIGWIGDIR="${WORKDIR}bigwig/"
19 | mkdir -p $TMPDIR
20 | mkdir -p $BIGWIGDIR
21 | 
22 | 
23 | #### Sample information: sample name and BAM file location.
24 | SAMPLE="WT";
25 | BAM="${WORKDIR}WT.bam"
26 | 
27 | 
28 | #### Threads - set to take advantage of multi-threading and speed things up.
29 | THREADS=1
30 | 
31 | 
32 | #### Temporaty BAM files.
33 | BAMFOR="${TMPDIR}${SAMPLE}.fwd.bam"     # BAM file representing reads mapping to forward strand
34 | BAMREV="${TMPDIR}${SAMPLE}.rev.bam"     # BAM file representing reads mapping to reverse strand
35 | BAMFOR1="${TMPDIR}${SAMPLE}.fwd1.bam"
36 | BAMFOR2="${TMPDIR}${SAMPLE}.fwd2.bam"
37 | BAMREV1="${TMPDIR}${SAMPLE}.rev1.bam"
38 | BAMREV2="${TMPDIR}${SAMPLE}.rev2.bam"
39 | 
40 | 
41 | #### bigwig files.
42 | BIGWIG="${BIGWIGDIR}${SAMPLE}.bigwig"           # BIGWIG file representing all reads
43 | BIGWIGFOR="${BIGWIGDIR}${SAMPLE}.for.bigwig"    # BIGWIG file representing reads mapping to forward strand
44 | BIGWIGREV="${BIGWIGDIR}${SAMPLE}.rev.bigwig"    # BIGWIG file representing reads mapping to reverse strand
45 | 
46 | #### Scale factor.
47 | # A scale factor is used to normalise for differences in library sizes across samples.  There are many ways to generate such a factor, such as the ratio of reads between two samples of spike-ins.  A more robust scale factor may be calculated using the "estimateSizeFactors" function from the Bioconductor package DESeq2 using sample gene count information.  Setting this to 1 indicates no scaling.  Note that it is necessary to use the recipricol of the the scale factor returned by DESeq2 when passing to the bamCoverage function since coverage will be multiplied by this.
48 | SCALEFACTOR=1
49 | 
50 | 
51 | #### Create bigwig file for all reads.
52 | bamCoverage --scaleFactor $SCALEFACTOR -p $THREADS -b $BAM -o $BIGWIG
53 | 
54 | 
55 | #### Create bigwig file for the forward strand.
56 | # Get file for transcripts originating on the forward strand.<br>
57 | # Include reads that are 2nd in a pair (128).  Exclude reads that are mapped to the reverse strand (16)<br>
58 | # Exclude reads that are mapped to the reverse strand (16) and first in a pair (64): 64 + 16 = 80<br>
59 | samtools view -b -f 128 -F 16 --threads $THREADS $BAM > $BAMFOR1
60 | samtools view -b -f 80  --threads $THREADS $BAM > $BAMFOR2
61 | samtools merge --threads $THREADS -f $BAMFOR $BAMFOR1 $BAMFOR2
62 | samtools index $BAMFOR
63 | bamCoverage --scaleFactor $SCALEFACTOR -p $THREADS -b $BAMFOR -o $BIGWIGFOR
64 | 
65 | 
66 | #### Create bigwig file for the reverse strand.
67 | # Get the file for transcripts that originated from the reverse strand:<br>
68 | # Include reads that map to the reverse strand (128) and are second in a pair (16): 128 + 16 = 144<br>
69 | # Include reads that are first in a pair (64), but exclude those ones that map to the reverse strand (16)<br>
70 | samtools view -b -f 144 --threads $THREADS $BAM > $BAMREV1
71 | samtools view -b -f 64 -F 16 --threads $THREADS $BAM > $BAMREV2
72 | samtools merge --threads $THREADS -f $BAMREV $BAMREV1 $BAMREV2
73 | samtools index $BAMREV
74 | bamCoverage --scaleFactor $SCALEFACTOR -p $THREADS -b $BAMREV -o $BIGWIGREV
75 | 
76 | 
77 | #### Remove temporary files.
78 | rm $BAMFOR $BAMFFOR1 $BAMFFOR2 $BAMREV $BAMREV1 $BAMREV2
79 | 


--------------------------------------------------------------------------------
/scripts/bigwig_bedtools.sh:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/bash
 2 | 
 3 | ## This bash script provides a means of generating scaled strand-specific BIGWIG files from a BAM file containing paired reads.
 4 | 
 5 | # This alternative to "bigwig.sh" uses bedtools (Quinlan, *et al* 2014) rather than deeptools to generate bedgraph files which are in turn converted to bigwig format via Jim Kent's BigWig and BigBed tools (kent, *et al*, 2010).  It is better able to deal with large bam files.
 6 | 
 7 | # Dependencies:<br>
 8 | #     SAMtools     http://samtools.sourceforge.net/<br>
 9 | #     bedtools     https://bedtools.readthedocs.io/en/latest/<br>
10 | #     kentUtils    http://bioinformatics.oxfordjournals.org/content/26/17/2204.long</br>
11 | 
12 | 
13 | #### Set working, temporary and results directories.
14 | WORKDIR="/path/to/my/working_directory/"
15 | TMPDIR="${WORKDIR}tmp/"
16 | BIGWIGDIR="${WORKDIR}bigwig/"
17 | mkdir -p $TMPDIR
18 | mkdir -p $BIGWIGDIR
19 | 
20 | #### Sample information: sample name and BAM file location.
21 | SAMPLE="WT";
22 | BAM="${WORKDIR}${SAMPLE}.sorted.marked.bam"
23 | 
24 | #### Chromosome size information.  An unheadered, two-column tab-delimited text file: <chromosome name> <size in bases>
25 | CHRSIZE="${WORKDIR}chrom.sizes.txt"
26 | 
27 | #### Threads - set to take advantage of multi-threading and speed things up.
28 | THREADS=8
29 | 
30 | #### Temporaty BAM files.
31 | BAMFOR="${TMPDIR}${SAMPLE}.fwd.bam"     # BAM file representing reads mapping to forward strand
32 | BAMREV="${TMPDIR}${SAMPLE}.rev.bam"     # BAM file representing reads mapping to reverse strand
33 | BAMFOR1="${TMPDIR}${SAMPLE}.fwd1.bam"
34 | BAMFOR2="${TMPDIR}${SAMPLE}.fwd2.bam"
35 | BAMREV1="${TMPDIR}${SAMPLE}.rev1.bam"
36 | BAMREV2="${TMPDIR}${SAMPLE}.rev2.bam"
37 | 
38 | #### bedgraph files.
39 | BEDGRAPH="${TMPDIR}${SAMPLE}.bedgraph"           # BEDGRAPH file representing all reads
40 | BEDGRAPHFOR="${TMPDIR}${SAMPLE}.for.bedgraph"    # BEDGRAPH file representing reads mapping to forward strand
41 | BEDGRAPHREV="${TMPDIR}${SAMPLE}.rev.bedgraph"    # BEDGRAPH file representing reads mapping to reverse strand
42 | 
43 | #### Sorted bedgraph files.
44 | BEDGRAPHSORTED="${TMPDIR}${SAMPLE}.sorted.bedgraph"           # Sorted BEDGRAPH file representing all reads
45 | BEDGRAPHFORSORTED="${TMPDIR}${SAMPLE}.for.sorted.bedgraph"    # Sorted BEDGRAPH file representing reads mapping to forward strand
46 | BEDGRAPHREVSORTED="${TMPDIR}${SAMPLE}.rev.sorted.bedgraph"    # Sorted BEDGRAPH file representing reads mapping to reverse strand
47 | 
48 | #### bigwig files.
49 | BIGWIG="${BIGWIGDIR}${SAMPLE}.bigwig"           # BIGWIG file representing all reads
50 | BIGWIGFOR="${BIGWIGDIR}${SAMPLE}.for.bigwig"    # BIGWIG file representing reads mapping to forward strand
51 | BIGWIGREV="${BIGWIGDIR}${SAMPLE}.rev.bigwig"    # BIGWIG file representing reads mapping to reverse strand
52 | 
53 | #### Scale factor.
54 | SCALEFACTOR=1
55 | 
56 | #### Create bigwig file for all reads.
57 | bedtools genomecov -ibam $BAM -bg -split -scale $SCALEFACTOR > $BEDGRAPH
58 | sort -k1,1 -k2,2 $BEDGRAPH > $BEDGRAPHSORTED
59 | bedGraphToBigWig $BEDGRAPHSORTED $CHRSIZE $BIGWIG
60 | 
61 | #### Create bigwig file for the forward strand.
62 | samtools view -b -f 128 -F 16 --threads $THREADS $BAM > $BAMFOR1
63 | samtools view -b -f 80  --threads $THREADS $BAM > $BAMFOR2
64 | samtools merge --threads $THREADS -f $BAMFOR $BAMFOR1 $BAMFOR2
65 | samtools index $BAMFOR
66 | bedtools genomecov -ibam $BAMFOR -bg -split -strand + -scale $SCALEFACTOR > $BEDGRAPHFOR
67 | sort -k1,1 -k2,2 $BEDGRAPHFOR > $BEDGRAPHFORSORTED
68 | bedGraphToBigWig $BEDGRAPHFORSORTED $CHRSIZE $BIGWIGFOR
69 | 
70 | #### Create bigwig file for the reverse strand.
71 | samtools view -b -f 144 --threads $THREADS $BAM > $BAMREV1
72 | samtools view -b -f 64 -F 16 --threads $THREADS $BAM > $BAMREV2
73 | samtools merge --threads $THREADS -f $BAMREV $BAMREV1 $BAMREV2
74 | samtools index $BAMREV
75 | bedtools genomecov -ibam $BAMREV -bg -split -strand - -scale $SCALEFACTOR > $BEDGRAPHREV
76 | sort -k1,1 -k2,2 $BEDGRAPHREV > $BEDGRAPHREVSORTED
77 | bedGraphToBigWig $BEDGRAPHREVSORTED $CHRSIZE $BIGWIGREV
78 | 
79 | #### Remove temporary files.
80 | rm $BAMFOR $BAMFFOR1 $BAMFFOR2 $BAMREV $BAMREV1 $BAMREV2 $BEDGRAPH $BEDGRAPHFOR $BEDGRAPHREV $BEDGRAPHSORTED $BEDGRAPHFORSORTED $BEDGRAPHREVSORTED
81 | 


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/scripts/metaprofiles.sh:
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 1 | #!/usr/bin/bash
 2 | 
 3 | 
 4 | ## This bash script provides a means of generating strand-specific metagene, TSS and TES profiles from a BAM file using "ngs.plot".
 5 | 
 6 | # *Of course, this will only work if your libraries were created in a strand-specific fashion.*
 7 | 
 8 | # Shen, L.*, Shao, N., Liu, X. and Nestler, E. (2014).<br>
 9 | # ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases, BMC Genomics, 15, 284.<br>
10 | # https://github.com/shenlab-sinai/ngsplot<br>
11 | 
12 | # Dependencies:<br>
13 | # SAMtools     http://samtools.sourceforge.net/<br>
14 | # ngs.plot     https://github.com/shenlab-sinai/ngsplot<br>
15 | 
16 | 
17 | #### Set working, temporary and results directories.
18 | WORKDIR="/path/to/my/working_directory/"
19 | TMPDIR="${WORKDIR}tmp/"
20 | PROFDIR="${WORKDIR}metaprofiles/"
21 | mkdir -p $TMPDIR
22 | mkdir -p $PROFDIR
23 | 
24 | 
25 | #### Sample information: sample name and BAM file location.
26 | SAMPLE="WT";
27 | BAM="${WORKDIR}WT.bam"
28 | MATE1="${WORKDIR}WT.mate1.bam"
29 | 
30 | 
31 | #### Threads - set to take advantage of multi-threading and speed things up.
32 | THREADS=1
33 | 
34 | #### Restict to first mate reads.
35 | # If the BAM file contains paired reads, create a new version containing only the first mate reads.<br>
36 | # This step may be skipped if your reads are not paired.<br>
37 | samtools view --threads $THREADS -h -b -f 64 $BAM -o $MATE1
38 | samtools index $MATE1
39 | 
40 | 
41 | #### BAM header compliance.
42 | # It might be necessary to re-header the BAM file so that the chromosome names match those in the ngs.plot database, e.g. standard chromosomes preceeded with "chr" for hg38.  This is most easily achieved using SAMtools "reheader" function.<br>
43 | # For human hg38 Ensembl alignments the following steps should do the job.<br>
44 | # This step may be skipped if your header is already compliant.<br>
45 | MATE1REHEADER="${WORKDIR}WT.mate1.reheader.bam"
46 | samtools view --threads $THREADS -H ${MATE1} | sed -e 's/SN:\([0-9XY]*\)/SN:chr\1/' -e 's/SN:MT/SN:chrM/' | samtools reheader - ${MATE1} > ${MATE1REHEADER}
47 | samtools index ${MATE1REHEADER}
48 | 
49 | 
50 | #### Run ngs.plot.
51 | # Finally, use ngs.plot to create sense and anti-sense profiles for your regions of interest using the correct BAM file.
52 | # Note that if your libraries were produced such that mate2 represents the forward strand the sense/anti-sense profiles will be reversed.
53 | ## Genebody
54 | REGION="genebody"
55 | for STRAND in both same opposite
56 | do
57 |     OUTPUT="${PROFDIR}${SAMPLE}.${REGION}.${STRAND}"
58 |     ngs.plot.r -G hg38 -R $REGION -C ${MATE1REHEADER} -O $OUTPUT -P $THREADS -SS $STRAND -SE 1 -L 5000 -F chipseq -D ensembl
59 | done
60 | 
61 | ## TSS
62 | REGION="tss"
63 | for STRAND in both same opposite
64 | do
65 |     OUTPUT="${PROFDIR}${SAMPLE}.${REGION}.${STRAND}"
66 |     ngs.plot.r -G hg38 -R $REGION -C ${MATE1REHEADER} -O $OUTPUT -P $THREADS -SS $STRAND -SE 1 -L 5000 -F chipseq -D ensembl
67 | done
68 | 
69 | ## TES
70 | REGION="tes"
71 | for STRAND in both same opposite
72 | do
73 |     OUTPUT="${PROFDIR}${SAMPLE}.${REGION}.${STRAND}"
74 |     ngs.plot.r -G hg38 -R $REGION -C ${MATE1REHEADER} -O $OUTPUT -P $THREADS -SS $STRAND -SE 1 -L 5000 -F chipseq -D ensembl
75 | done
76 | 
77 | 
78 | #### Combining sense and anti-sense profiles
79 | # By default ngs.plot produces average profiles in .pdf format.  There is also a zip file containing the underlying data,  This may be used to combine the sense and anti-sense profiles on a single set of axes.  This is best achieved using R.
80 | 
81 | 
82 | #### Scaling the profiles
83 | # A scale factor may be used to normalise for differences in library sizes across samples.  There are many ways to generate such a factor, such as the ratio of reads between two samples of spike-ins.  A more robust scale factor may be calculated using the "estimateSizeFactors" function from the Bioconductor package DESeq2 using sample gene count information.<br>
84 | 
85 | # The scale factor may be used to modify the number of valid reads discovered in the BAM file given in the resulting ".cnt" file.  Edit this file to multiply read count by your scale factor and re-run ngs.plot using the same parameters.
86 | 
87 | 
88 | #### Custom genes / intervals
89 | # Profiles may be restricted to specific gene sets and also to user-defined intervals.  See the ngs.plot documentation for further details.
90 | # https://github.com/shenlab-sinai/ngsplot
91 | # https://github.com/shenlab-sinai/ngsplot/wiki/ProgramArguments101
92 | 


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