agplus is a simple command-line tool that enables rapid and flexible production of text tables tailored for aggregation plots from which users can easily design multiple groups based on user-definitions such as regulatory regions or transcription initiation sites.
Ruby and BEDTools are required to run agplus.
Place the agplus file and the supporting shell scripts anywhere you like. You will need the below software to use our supporting shell scripts (bam2bwshifted, agpdraw-line and assignExprGroup) and to follow our Tutorial section in this README.
- wigToBigWig: required by bam2bwshifted
- bigWigToWig: required in the Tutorial
- SAMtools: required by bam2bwshifted
- R: required by agpdraw-line and assignExprGroup
- RColorBrewer package of R: required by agpdraw-line
The wigToBigWig and bigWigToWig are available at http://hgdownload.cse.ucsc.edu/admin/exe/
You will also need chrom.size file to run bam2bwshifted. This file is a two-column text file of chromosome sizes. Please prepare the file if you have not yet. The example of generating hg19.chrom.sizes is in our Tutorial.
For OSX users, we recommend to install Homebrew. It is the easiest way to setup the environment to run agplus and we assume homebrew is already installed on your system. After installing homebrew, please "tap" homebrew/science repository, which contains hundreds of bioinformatics tools.
brew tap homebrew/science
Here we also assume that the user is under the bash shell environment and all tools are installed under the user's home directory (~/bin). Please set up the PATH environment as below.
mkdir ~/bin
echo 'export PATH=$HOME/bin:$PATH' >> ~/.bashrc
source ~/.bashrc
We recommend Ruby version > 1.9. The version 2.x is preferable for computation speed. There are several way to install Ruby but we recommend the instructions below.
https://www.ruby-lang.org/en/documentation/installation/
For RedHat-based Linux users,
sudo yum install R
For Ubuntu users, please follow the instruction below.
http://cran.r-project.org/bin/linux/ubuntu/README
For OSX users after installing homebrew, just type:
brew install r
After installing R, please type the below command in R to install the RColorBrewer package.
install.packages("RColorBrewer")
For Ubuntu users,
apt-get install samtools
For OSX users,
brew install samtools
For the Redhat-based system,
yum install samtools
Please note that samtools is available at the EPEL repository.
Debian/Ubuntu:
apt-get install bedtools
RedHat - Fedora/Centos:
yum install bedtools
OSX:
brew install bedtools
Please download bigWigToWig, wigToBigWig and fetchChromSizes from
Linux: http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/
OSX: http://hgdownload.cse.ucsc.edu/admin/exe/macOSX.x86_64/
and place them into ~/bin.
chmod +x fetchChromSizes bigWigToWig wigToBigWig
mv fetchChromSizes bigWigToWig wigToBigWig ~/bin
The installation process of agplus simply requires copying the files into ~/bin.
git clone https://github.com/kazumits/agplus.git
cd agplus
cp agplus agpdraw-line assignExprGroup bam2bwshifted ~/bin
If you don't feel like using the git version, please download the release version of agplus and copy the scripts into ~/bin.
generates a text table for the rapid and flexible creation of aggregation plots.
agplus [options] -b reference.bed [WIG/BEDGRAPH file(it expects STDIN if ommited)]
-b, --reference=file reference BED, requires strand(6th column) field (default: none, required)
-d, --distance-from=origin distance from start/center/end of the references is used (default: start)
-o, --out=file output file name (default: STDOUT)
-r, --range=from,to aggregating range in base-pairs (default:-5000,5000)
-a, --assignment=file name->group assignment table, 2-column and tab-separated
converts BAM files into a coverage track (bigWig) of N bp shifted positions of the reads (fragment-midpoint). The midpoint counts are normalized as RPM (#reads per million reads).
bam2bwshifted [-o outfile.bw] [-s shiftsize (default: 73)] -g chrom.sizes BAM
generates assignment file of expression level groups (given -i percentile expression level interval) from the genes.fpkm_tracking file of cufflinks (or cuffdiff).
assignExprGroup [-i interval] [-o outputdir] [-c column-name-of-gene] genes.fpkm_tracking
draws smoothed (Gaussian filtered) curves from the agplus output file. -s sdev sets the smoothing parameter of the Gaussian kernel (the bigger the sdev, the smoother the curve).
agpdraw-line [-o outfile.pdf] [-s sdev] [-r from,to] [-t title] agplus-output.txt
The target of your ChIP-Seq analysis should be in wiggle or bedgraph format.
Example:
chr1 9947 9948 0.052102
chr1 9952 9953 0.052102
chr1 9978 9979 0.052102
chr1 10003 10004 0.052102
chr1 10015 10016 0.052102
(...)
The reference where you will aggregate target should be in BED6 format (6 columns BED). Typically, this file can be taken from a public database such as UCSC's table browser.
An example reference file of a gene locus looks like the following:
chr1 11873 14409 DDX11L1 0 +
chr1 14361 29370 WASH7P 0 -
chr1 34610 36081 FAM138F 0 -
chr1 34610 36081 FAM138A 0 -
chr1 69090 70008 OR4F5 0 +
(...)
The group assignment should be a two-column, tab-delimited text file. Typically, you write the assignment definitions, such as "gene-name[tab]group-name", per line in this file. Note that you do not need to write out all the names defined in reference here.
An example of assignment file of Up/Down/Stay genes groups looks like the following:
WASH7P Upregulated
OR4F6 Upregulated
DDX11L1 Downregulated
FAM138A Downregulated
FAM138F Stay
(...)
The output file of agplus is simple tab-delimited table of average signal intensities in 1 bp per line. The table can be easily handled by subsequent analysis using gnuplot, R, Matlab, MS Excel, etc.
Example:
distance whole
-5000 0.0006606468404588093
-4999 0.0006476077580813323
-4998 0.0006345686757038559
(...)
4997 0.000534602377476536
4998 0.0004911387695516146
4999 0.0005128705735140753
5000 0.000554161001042751
This tutorial aims to visualize H3K27ac ChIP-Seq signal distributions at transcription start sites (TSSs) grouped by the gene expression levels. The example data are H3K27ac ChIP-Seq of HeLa-S3 cells provided by the ENCODE/Broad Institute (GSE29611). The expression levels of HeLa-S3 were determined by ENCODE/Caltech RNA-Seq data (GSE33480).
You need to download the BAM file of H3K27ac ChIP-Seq from UCSC.
We recommend using UDR for faster downloading of ENCODE data from UCSC.
To define expression groups using assignExprGroup, you need the genes.fpkm_tracking file of cufflinks (or cuffdiff) output using this bam. For convenience, we prepared a pre-calculated genes.fpkm_tracking file of HeLa-S3. Please download the file and decompress it.
Additionally, you need a gene locus definition file in BED6 format as reference. We prepared a parsed file from UCSC's hg19 refFlat definition for agplus (available from the table browser). Please download the file and decompress it.
Please prepare the hg19.chrom.sizes to follow this tutorial.
fetchChromSizes hg19 > hg19.chrom.sizes
Step 1: generate a coverage track (bigWig) file of fragment midpoints (-s 100; half-size of 200 bp) from the target BAM file
bam2bwshifted -s 100 -g hg19.chrom.sizes -o HeLaS3_H3K27ac.bw wgEncodeBroadHistoneHelas3H3k27acStdAlnRep1.bam
Step 2: decompress the bigWig file to treat with agplus
bigWigToWig HeLaS3_H3K27ac.bw HeLaS3_H3K27ac.wig
Step 3: aggregate target signals at all TSSs defined in reference
agplus -b refFlat_hg19_simple.bed -d start -o aggr_HeLaS3_H3K27ac.txt HeLaS3_H3K27ac.wig
The aggregation process takes about 2 minutes assuming the use of Intel(R) Xeon(R) CPU E5-2687W @ 3.10GHz on x86_64 GNU/Linux)
89.98s user 0.29s system 63% cpu 2:23.18 total
One can also combine Steps 2 & 3 to reduce the number of intermediate files.
bigWigToWig HeLaS3_H3K27ac.bw stdout | agplus -b refFlat_hg19_simple.bed -d start -o aggr_HeLaS3_H3K27ac.txt
Step4: draw the aggregation plot
agpdraw-line -t "H3K27ac at all TSSs" -r -2000,2000 -o H3K27acAllTSS.pdf aggr_HeLaS3_H3K27ac.txt
The result of a typical PDF file is shown below.
Please note that the aggregation is done against all TSSs when you omit the -a assignment file option, i.e. all gene TSSs are assigned to "whole" group.
If you would like to see the distribution by expression levels, incorporate the following procedure after Step 2.
Create an assignment file from the genes.fpkm_tracking file.
assignExprGroup -i 10 genes.fpkm_tracking
The output file is produced under the assignedPer10 directory. Please check the file egroup_ePer10.txt that is automatically generated as the assignment file. The assignment file is the group definition of genes per 10%ile interval gene expression levels. (The output will be a multiple text file of different samples when the file is from cuffdiff.)
Then, Run agplus with the assignment file.
bigWigToWig HeLaS3_H3K27ac.bw stdout | agplus -b refFlat_hg19_simple.bed -d start -a assignedPer10/egroup_ePer10.txt -o aggr_HeLaS3_H3K27ac_Expr10pt.txt
93.64s user 0.32s system 65% cpu 2:24.28 total
Finally, run agpdraw-line to get the PDF output.
agpdraw-line -t "H3K27ac by expression groups" -r -2000,2000 -o H3K27acByExpression.pdf aggr_HeLaS3_H3K27ac_Expr10pt.txt
The result:
This example aims to visualize the distributions of H3K4me1 signal, which is related to enhancer regions on each genomic segment predicted by ChromHMM.
Download and unzip chromHMM bed for K562 cells.
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeBroadHmm/wgEncodeBroadHmmK562HMM.bed.gz
gunzip wgEncodeBroadHmmK562HMM.bed.gz
Reformat the file into 6-column BED with strand so that agplus can handle it as reference.
awk -v OFS="\t" '{print $1,$2,$3,$4,0,"+"}' wgEncodeBroadHmmK562HMM.bed > chromHMM_K562_reformatted.bed
The strands were all fixed at "+" because there is no infromation of orientation in the file.
Create a tab-delimited assignment file manually as below with any text editor or MS Excel and then save it as assign_chromHMM.txt. You can copy & paste this example but you should be aware that the delimiters should be tabs (tabs are often transformed into multiple white spaces when pasting).
1_Active_Promoter ActivePromoter
2_Weak_Promoter WeakPromoter
3_Poised_Promoter PoisedPromoter
4_Strong_Enhancer StrongEnhancer
5_Strong_Enhancer StrongEnhancer
6_Weak_Enhancer WeakEnhancer
7_Weak_Enhancer WeakEnhancer
8_Insulator Insulator
9_Txn_Transition TxnTransition
10_Txn_Elongation TxnElongation
11_Weak_Txn WeakTxn
12_Repressed Repressed
13_Heterochrom/lo Heterochrom_Lo
The names of the first column were taken from ChromHMM definition (4th column in original file). We merged some categories (the second column; StrongEnhancer and WeakEnhancer) to reduce the use of colors in the plot of this example.
Download K562 H3K4me1 ChIP-Seq data (mapped bam file) using udr.
udr rsync -avP hgdownload-sd.sdsc.edu::goldenPath/hg19/encodeDCC/wgEncodeBroadHistone/wgEncodeBroadHistoneK562H3k4me1StdAlnRep1.bam .
Create mid-points count file (bigWig) from the bam.
bam2bwshifted -s 100 -g hg19.chrom.sizes -o K562_H3K4me1.bw wgEncodeBroadHistoneK562H3k4me1StdAlnRep1.bam
Run agplus using the prepared files above.
bigWigToWig K562_H3K4me1.bw stdout | agplus -b chromHMM_K562_reformatted.bed -d center -a assign_chromHMM.txt -o aggr_K562_H3K4me1_chromHMM.txt
The caluclation takes about 30 min (30 times longer than Tutorial) because chromHMM reference is about 30 times larger than gene definition (23,975 vs. 622,257 lines).
Finally, you will get the plot PDF shown below.
agpdraw-line -t "K562 H3K4me1" aggr_K562_H3K4me1_chromHMM.txt
Q. What is "average density" that agpdraw-line reports?
A. The area under the curve in plot makes group-averaged read/peak number. The "density" was defined so that the area under the (smoothed) curve makes total aggregated read/peak numbers. agplus reports the averaged densities over group members which is defined in the reference and assignment files. Please note that the read count is RPM (Read Per Million) normalized when you use bam2bwshifted.
Q. Can agplus aggregate peaks (not raw reads)?
A. Yes. agplus can accepts peak-called file (BED or BED-like format) after reformatting. For example, peaks were recorded in peaks.bed file:
awk -v OFS="\t" '{print $1,$2,$3,1}' peaks.bed > peaks.wig
and give "peaks.wig" as the target file of agplus. In this case, "average density" of the plot can be regarded as "average peak detection rate".
If the peaks file contains a score column (tag-count, p-value, fold-enrichment, etc.) and you would like to calculate average of the scores, you can replace {print $1,$2,$3,1} to {print $1,$2,$3,$7} if you want to use the 7th column of the original BED file as the score.
We recommend using the peak summits file as target/reference file if available. Using peak summits could improve accuracy/resolution of signal source positioning.
Q. How to change the look of the plot (colors, line widths, drawing range of y-axis).
A. We recommend using your own line drawing software to change the looks freely. The output file of agplus is a simple text table and thus can be easily imported into any software, e.g. MS Excel, gnuplot, R, Matlab (or you can directly edit the output PDF by vector graphics software like Adobe's Illustrator).
Please note that the line-smoothing function is part of agpdraw-line and thus is not done by agplus. We provided agpdraw-line as a visual example of agplus output and consider it is sufficient in most cases.
Plese cite the following reference:
- Kazumitsu Maehara and Yasuyuki Ohkawa, agplus: a rapid and flexible tool for aggregation plots, Bioinformatics, 2015, doi: 10.1093/bioinformatics/btv322
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