The genomation package is an R toolkit designed to summarize, annotate, and visualize genomic intervals effectively. Key features include annotation of genomic intervals, summarization of genomic scores, and visualization options such as meta-region plots and heatmaps, accommodating various file formats like BAM and BED. The package aims to streamline the analysis of genomic data, making it accessible while emphasizing ease of use and flexibility.

1. genomation
package
genomation
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
a toolkit to summarize, annotate and visualize genomic intervals
Using
genomation
Altuna Akalın1
More
information
February 24, 2014
1*
presented by. Package developed by Altuna Akalın and Vedran Franke

2. Quick introduction
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
The genomation is an R package that expedites genomic
interval summary and annotation. It has the following features
1
Annotation of genomic intervals: e.g. see what % of your
intervals overlap with exon/intron/promoters

3. Quick introduction
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
The genomation is an R package that expedites genomic
interval summary and annotation. It has the following features
1
2
Annotation of genomic intervals: e.g. see what % of your
intervals overlap with exon/intron/promoters
Summary of genomic scores or read coverages over pre-defined
regions
e.g. extract the conservation profile over ChIP-seq binding sites
(equi-width regions) or CpG islands (nonequi-width regions)

4. Quick introduction
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
The genomation is an R package that expedites genomic
interval summary and annotation. It has the following features
1
2
More
information
Annotation of genomic intervals: e.g. see what % of your
intervals overlap with exon/intron/promoters
Summary of genomic scores or read coverages over pre-defined
regions
e.g. extract the conservation profile over ChIP-seq binding sites
(equi-width regions) or CpG islands (nonequi-width regions)
3
Visualize genomic interval summaries as meta-region plots or
heatmaps.

5. Quick introduction
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
The genomation is an R package that expedites genomic
interval summary and annotation. It has the following features
1
2
More
information
Annotation of genomic intervals: e.g. see what % of your
intervals overlap with exon/intron/promoters
Summary of genomic scores or read coverages over pre-defined
regions
e.g. extract the conservation profile over ChIP-seq binding sites
(equi-width regions) or CpG islands (nonequi-width regions)
3
4
Visualize genomic interval summaries as meta-region plots or
heatmaps.
Work with multiple file formats
e.g. BAM, BED, bigWig, GFF and generic tabular text files
containing chromosome location information.

6. Quick introduction
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
The genomation is an R package that expedites genomic
interval summary and annotation. It has the following features
1
2
More
information
Annotation of genomic intervals: e.g. see what % of your
intervals overlap with exon/intron/promoters
Summary of genomic scores or read coverages over pre-defined
regions
e.g. extract the conservation profile over ChIP-seq binding sites
(equi-width regions) or CpG islands (nonequi-width regions)
3
4
Visualize genomic interval summaries as meta-region plots or
heatmaps.
Work with multiple file formats
e.g. BAM, BED, bigWig, GFF and generic tabular text files
containing chromosome location information.
5
do all these in R :)

7. Genomic interval summaries are widely used
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
Summaries of genomic intervals are one of the useful ways to
communicate high-dimensional data
Traditionally, regions of interest are picked and distribution of
genomic intervals are summarized on those regions

8. Genomic interval summaries are widely used:
Examples from literature
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
Figure : Erkek, S., et al. (2013). Molecular determinants of nucleosome
retention at CpG-rich sequences in mouse spermatozoa. Nature Structural
& Molecular Biology

9. Genomic interval summaries are widely used:
Examples from literature
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
Figure : Stadler, M., Murr, R., Burger, L., et al. (2011). DNA-binding
factors shape the mouse methylome at distal regulatory regions. Nature

10. Utility and futility of average profiles
genomation
package
average profile around anchor
4.5
average score
More
information
5.0
Using
genomation
4.0
Usage and
ubiquity of
genomic
interval
summaries
Does this mean all of the windows (viewpoints) have a similar
enrichment profile?
3.5
Altuna Akalın
−100
0
bases
50
100

11. Utility and futility of average profiles
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Only 1/3 of windows have such enrichment. Be careful when you are
interpreting the average profiles.
21
Using
genomation
0
5.2
10
16
More
information
−100
0
50 100

12. Genomic interval summaries are widely used:
Examples from literature
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
Figure : Lister, R., et al. (2009). Human DNA methylomes at base
resolution show widespread epigenomic differences. Nature

13. Genomic interval summaries are widely used:
Examples from literature
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
Figure : Feng, S. et al. (2010). Conservation and divergence of
methylation patterning in plants and animals. PNAS

14. Issues to keep in mind when developing summary
methods
genomation
package
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Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
Genomic data comes in many formats, we need a method that is
able to work with multiple flat file formats
We need a method that is not specialized on one type of data
set such as read counts, it should also work on other scoring
schemes(e.g. conservation scores) easily.
Regions of interest are not always equi-width, you should be able
to normalize for length differences by binning.
Multiple visualization options and fast heatmap generation
should be available
Clustering of regions based on multiple summaries (e.g.
binding for different TFs on the same set of regions) on the
heatmap
Ease of use, it should not take hours of coding to generate and
visualize summaries.

15. Overview of genomation features
genomation
package
Altuna Akalın
region 2
region 3
region 4
...
region m
1.1
1.0
0.8
0.86
TF4
TF3
0.6
0.6
TF2
0.34
TF1
0.072
.
.
.
.
. . . . . .
.
TF1
0
0.2
region 1
TF3
TF2
0.4
read per million
1 2 3 4 ... n
0
500
1000
base-pairs around anchor
heatmaps for genomic interval sets
TF 4
TF 3
TF 2
TF 1
Visualize
0 0.5 1 1.5 2 2.5
0
100
0
0
0
0 0.5 1 1.5 2 2.5
500
500
100
0
0
100
0
0 0.5 1 1.5 2
0
Annotate
500
Annotation
BED
GFF
Tab txt
GRanges
500
1000
base-pairs around anchor
500
More
information
Genomic Intervals
BAM
BigWig
BED
GFF
Summarize
Tab txt
GRanges
meta-region heatmaps
TF4
100
Using
genomation
meta-region plots
ScoreMatrix/ScoreMatrixList object
Base-pairs/ bins
0.0
Usage and
ubiquity of
genomic
interval
summaries
0 0.5 1 1.5 2 2.5
Piecharts for annotation
25.7
21.8
11.6
40.9
Intergenic
Intron
Exon
Promoter

16. installation of the package and the example data
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
We can install the package and the data using install_github()
function from the devtools package.
#install dependencies
install.packages( c("data.table","plyr","reshape2","ggplot2",
"gridBase","devtools"))
source("http://bioconductor.org/biocLite.R")
biocLite(c("GenomicRanges","rtracklayer","impute","Rsamtools"))
# install the packages
library(devtools)
install_github("genomation", username = "al2na")
# install the data package
# needed for examples
install_github("genomationData", username = "al2na")

17. Data import
genomation
package
Altuna Akalın
Various file formats can be used in genomation. You can read in
annotation or your genomic intervals of interest.
Usage and
ubiquity of
genomic
interval
summaries
library(genomation)
tab.file1 <- system.file("extdata/tab1.bed", package = "genomation")
readGeneric(tab.file1)
Using
genomation
More
information
## GRanges with 6
##
seqnames
##
<Rle>
##
[1]
chr21
##
[2]
chr21
##
[3]
chr21
##
[4]
chr21
##
[5]
chr21
##
[6]
chr21
##
--##
seqlengths:
##
chr21
##
NA
ranges and 0 metadata columns:
ranges strand
<IRanges> <Rle>
[9437272, 9439473]
*
[9483485, 9484663]
*
[9647866, 9648116]
*
[9708935, 9709231]
*
[9825442, 9826296]
*
[9909011, 9909218]
*

18. Extraction of data over pre-defined genomic regions
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
ScoreMatrix() and ScoreMatrixBin() are functions used to extract
data over predefined windows.
ScoreMatrix is used when all of the windows have the same
width (e.g. region around TSS)
Using
genomation
ScoreMatrixBin is designed for use with windows of unequal
width (e.g. enrichment of methylation over exons).
More
information
data(cage)
data(promoters)
sm <- ScoreMatrix(target = cage, windows = promoters)
sm
##
scoreMatrix with dims:
1055 2001

19. Visualizing ScoreMatrix: summary of genomic
invervals over pre-defined regions
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
plotMeta(),heatMeta(), heatMatrix() and multiHeatMatrix()
are the visualization functions.
oldmar <- par()$mar
par(oma = c(0, 0, 0, 0))
heatMatrix(sm, xcoords = c(-1000, 1000))
plotMeta(sm, xcoords = c(-1000, 1000),line.col="blue")
par(oma = oldmar)
0.15
average score
0.10
0.05
0.00
0
0.75
1.5
2.2
3
0.20
0.25
More
information
−1000
−500
0
500
1000
−1000
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0
bases
500
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20. Working with BAM files
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
BAM files can also be used in ScoreMatrix() and ScoreMatrixBin()
functions
bam.file = system.file('tests/test.bam', package='genomation')
windows = GRanges(rep(c(1,2),each=2),
IRanges(rep(c(1,2), times=2), width=5))
scores3 = ScoreMatrix(target=bam.file,windows=windows, type='bam')

21. Working with bigWig files
DHS around TSS
14
More
information
8 10
Using
genomation
my.bed12.file=system.file("extdata/chr21.refseq.hg19.bed",
package = "genomation")
feats=readTranscriptFeatures(my.bed12.file,up.flank=500,down.flank=500)
sm=ScoreMatrix(target="wgEncodeUwDnaseA549RawRep1.bw",
windows=feats$promoters,type='bigWig',strand.aware=TRUE)
plotMeta(sm,xcoords=c(-500,500),main="DHS around TSS",line.col="blue")
average score
Usage and
ubiquity of
genomic
interval
summaries
6
Altuna Akalın
ScoreMatrix() and ScoreMatrixBin() are functions can handle
bigWig files. Here we use ENCODE DHS scores, downloaded from
http://goo.gl/fEVu0g
4
genomation
package
−400
0
200

22. Multiple profiles
0
0
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25
0
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−2
50
50
−5 0
00
0
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50
−5 0
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0
P300 Suz12 Rad21 Znf143
−2
50
0
0
−5
00
CTCF
50
−5 0
00
More
information
25
Using
genomation
ctcf.peaks=readRDS("ctcf.peaks.rds")
dataPath = system.file("extdata", package = "genomationData")
bam.files = list.files(dataPath, full= T,pattern = "bam$")[c(1:4,6)]
sml = ScoreMatrixList(bam.files, ctcf.peaks, bin.num = 50,type = "bam")
names(sml)=c("CTCF","P300","Suz12","Rad21","Znf143")
multiHeatMatrix(sml, xcoords = c(-500, 500),cex.axis=0.35,common.scale = T,
col = c("lightgray", "blue"),winsorize=c(0,95))
−2
50
Usage and
ubiquity of
genomic
interval
summaries
50
−5 0
00
Altuna Akalın
Multiple heatmap profiles can be plotted using multiHeatMatrix()
which takes in a ScoreMatrixList object. Here we used CTCF , P300
, Suz12 ,Rad21, Znf143 BAM files from genomationData package.
−2
50
genomation
package
02468 02468 02468 02468 02468

23. Multiple profiles
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
multiHeatMatrix() can also apply K-means clustering. Extreme
values are trimmed using with “winsorize” argument
multiHeatMatrix(sml, xcoords = c(-500, 500),kmeans=TRUE,k=3,common.scale = T,
cex.axis=0.4,col = c("lightgray", "blue"),winsorize=c(0,95))
CTCF
P300 Suz12 Rad21 Znf143
1
More
information
2
0
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25
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02468 02468 02468 02468 02468

24. Multiple profiles
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
Multiple average profiles can be visualized with heatMeta(). Here,
we also apply a scaling function to all the matrices.
# take log2 of all matrices
sml2=scaleScoreMatrixList(sml,scalefun=function(x) log2(x+1))
heatMeta(sml2,legend.name="average profiles",xcoords=c(-500, 500),
xlab="bp around peaks")
More
information
1.8
CTCF
average profiles
0.61
1
1.4
P300
Suz12
0.21
Rad21
Znf143
−400
−200
0
bp around peaks
200
400

25. Multiple profiles
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Multiple average profiles can also be visualized with plotMeta()
plotMeta(sml2,profile.names=names(sml2),
xcoords=c(-500, 500),
main="mult. profiles")
Using
genomation
mult. profiles
More
information
1.0
0.5
average score
1.5
CTCF
P300
Suz12
Rad21
Znf143
−400
−200
0
bases
200
400

26. Future work...
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
Explore overlap statistics between two genomic data sets: Does
TF1 binding site locations overlap with TF2 sites more than
expected?
This is previously explored with GenometriCorr package. These
functionality can be included in the form of a dependency.
Performance improvement on certain functions, faster is always
better...

27. Further information
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
The genomation package is available at
http:/al2na.github.io/genomation. You can find the link
to the vignette on the webpage as well.
Code that generated this presentation is available at
http://github.com/al2na/genomation_presentation
Questions and bug reports
You can view/open issues in github
https://github.com/al2na/genomation/issues?state=open
You can ask questions by sending an e-mail to
genomation@googlegroups.com or using the web interface to
google groups
Developed by Altuna Akalın and Vedran Franke

28. Session Info
genomation
package
Altuna Akalın
Usage and
ubiquity of
genomic
interval
summaries
Using
genomation
More
information
sessionInfo()
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)
locale:
[1] C
attached base packages:
[1] methods
grid
stats
[8] base
graphics
grDevices utils
other attached packages:
[1] genomation_0.99.0.2 knitr_1.5
loaded via a namespace (and not attached):
[1] BSgenome_1.30.0
BiocGenerics_0.8.0
[4] GenomicRanges_1.14.3 IRanges_1.20.5
[7] RColorBrewer_1.0-5
RCurl_1.95-4.1
[10] XML_3.95-0.2
XVector_0.2.0
[13] colorspace_1.2-4
data.table_1.8.10
[16] digest_0.6.3
evaluate_0.5.1
[19] ggplot2_0.9.3.1
gridBase_0.4-6
Biostrings_2.30.0
MASS_7.3-29
Rsamtools_1.14.1
bitops_1.0-6
dichromat_2.0-0
formatR_0.10
gtable_0.1.2
datas