Genomic Scores

As we have seen earlier, genomic scores are often stored in wiggle format.

igv

Genomic Scores

A perhaps more common human readable format is bedGraph.

igv

Genomic Scores

But in many situations we would want a highly compressed format such as bigWig.

We used a bigWig from Encode in our last session.

igv

Genomic Scores

Genomic Scores are heavily used in Genomics and High throughput sequencing as they offer a simple mechanism to review a defined metric over the linear genome at a specified resolution.

RNA-seq, ChIP-seq, ATAC-seq signals (as well many other seq types).
Phylogenetic conservation.

Our Genomic Scores data.

In our last session Genomic Intervals In Bioconductor we reviewed some of the Myc ChIP-seq signal available to us on encode.

This was the data from Experiment ENCSR000ERN, containing information on Myc ChIP-seq in mouse genome version mm10 CH12 cell line.

If you have not already downloaded the bigWig file then download it from this link for our exercise later

Our Genomic Scores data.

From our last session we identified Myc peaks within the Igfbp2 locus and in IGV compared Myc ChIP-seq signal from Encode over our peaks.

igv

Our Genomic Scores data

For this course I have provided bedGraph and bigWig files from this data for the window with the IGV image.

We will demonstrate how to create this later in today’s session but for now data is available in

data/TSS_ENCFF940MBK.bedGraph - bedGraph of region.
data/TSS_ENCFF940MBK.bw - bigWig of region.

Genomic Scores in Bioconductor.

Two popular Bioconductor packages for dealing with Genomics Scores are:

rtracklayer – Importing/exporting genomic intervals into/out of R.
GenomicRanges – Handling genomic intervals in R.

Genomic Scores in Bioconductor.

Now we have the package installed, we can load the library rtracklayer which we will use to import and export from/to bedGraph and bigWig.

library(rtracklayer)

Genomic Scores in Bioconductor.

We will also be making use of the functions in the GenomicRanges package. We dont need to load GenomicRanges directly here because the rtracklayer does this for us.

Package dependencies and imports allow one package to make use of functions from another.

igv

Reading in a bedGraph.

The rtracklayer package provides functions to import genomic scores from a bedGraph using the import.bedGraph() function.

myBedG <- import.bedGraph("data/TSS_ENCFF940MBK.bedGraph")

Reading in a bedGraph.

The genomic scores by default are stored as a familiar GRanges object containing the original 4 columns of information, contig, start, end and score.

myBedG[1:3]

## GRanges object with 3 ranges and 1 metadata column:
##       seqnames            ranges strand |     score
##          <Rle>         <IRanges>  <Rle> | <numeric>
##   [1]     chr1        1-72811054      * |   0.00000
##   [2]     chr1 72811055-72811119      * |   0.69040
##   [3]     chr1 72811120-72811145      * |   0.57802
##   -------
##   seqinfo: 54 sequences from an unspecified genome; no seqlengths

Reading in a bedGraph.

Because we only have 4 columns in a bedGraph and no strand information, the GRanges intervals are unstranded with * in their strand column

strand(myBedG)

## factor-Rle of length 2161 with 1 run
##   Lengths: 2161
##   Values :    *
## Levels(3): + - *

Reading in a bigWig

Much of the genomic scores we will be working with are infact stored in the compressed bigWig format.

We can also import bigWigs into R using the import.bw function.

myBigWig <- import.bw("data/TSS_ENCFF940MBK.bw")

Reading in a bigWig

The import bigWig’s genomic scores are again imported as a GRanges object containing the same information as the imported bedGraph.

myBigWig[1:3]

## GRanges object with 3 ranges and 1 metadata column:
##       seqnames            ranges strand |     score
##          <Rle>         <IRanges>  <Rle> | <numeric>
##   [1]     chr1        1-72811054      * |   0.00000
##   [2]     chr1 72811055-72811119      * |   0.69040
##   [3]     chr1 72811120-72811145      * |   0.57802
##   -------
##   seqinfo: 54 sequences from an unspecified genome

GenomicScores as a GRanges

So far we have retrieved our genomic scores from bedGraphs and bigWigs as GRanges objects.

This allows us to use GRanges accessors and functions we have already seen in our last session.

myGRanges <- GRanges("chr1",IRanges(72823698,72824485))
filteredBigWig <- myBigWig[myBigWig %over% myGRanges]
filteredBigWig[1:3]

## GRanges object with 3 ranges and 1 metadata column:
##       seqnames            ranges strand |     score
##          <Rle>         <IRanges>  <Rle> | <numeric>
##   [1]     chr1 72823686-72823703      * |   1.34249
##   [2]     chr1 72823704-72823710      * |   1.80605
##   [3]     chr1 72823711-72823715      * |   1.68676
##   -------
##   seqinfo: 54 sequences from an unspecified genome

GenomicScores as a RLE

We can however specify the type of objects we would like to return from the import.bedGraph and import.bw functions.

Here we will import the bigWig as a object we have briefly seen, the Rle object (run length encoding). Here we have an Rlelist (a list of Rle objects)

myBigWig <- import.bw("data/TSS_ENCFF940MBK.bw",
                      as = "RleList")
class(myBigWig)

## [1] "SimpleRleList"
## attr(,"package")
## [1] "IRanges"

Rle in genomics

Run length encoding allows for a very efficient storage of long stretchs of repeated values.

We have already seen an rle in our cigar string from SAM files.

100M - 100 matches to reference for alignment
28M1D72M - 28 matches, 1 deletion and 72 matches for aligment

Rle in genomics

We have also seen Rle objects within the GRanges objects we reviewed last session.

Chromosome/contig names in GRanges objects are stored as Rle objects for instance.

mycPeaks <- import.bed("data/Myc_Ch12_1_withInput_Input_Ch12_summits.bed")
seqnames(mycPeaks)

## factor-Rle of length 13910 with 21 runs
##   Lengths:   891   737  1537   498   595 ...   796   646   714   227    10
##   Values : chr1  chr10 chr11 chr12 chr13 ... chr7  chr8  chr9  chrX  chrY 
## Levels(21): chr1 chr10 chr11 chr12 chr13 chr14 ... chr7 chr8 chr9 chrX chrY

Rle in genomics

For genomic scores we will be storing long stretchs of numbers as an Rle.

To store genomic scores across chromosomes/contig we will use an RleList.

Creating an Rle is straightforward. We can simply supply a numeric vector of numbers we wish to compress to the Rle() function.

myNumbers <- c(0,0,0,0,0,1,1,1,0,0,0,0,0)
Rle(myNumbers)

## numeric-Rle of length 13 with 3 runs
##   Lengths: 5 3 5
##   Values : 0 1 0

Rle in genomics

Now can construct a named RleList containing the Rle objects using the RleList() function.

myNumbers2 <- c(0,0,0,0,0,1,1,1,2,2,2,2,2)
chr1Scores <- Rle(myNumbers)
chr2Scores <- Rle(myNumbers2)
myRleList <- RleList(chr1=chr1Scores,chr2=chr2Scores)
myRleList

## RleList of length 2
## $chr1
## numeric-Rle of length 13 with 3 runs
##   Lengths: 5 3 5
##   Values : 0 1 0
## 
## $chr2
## numeric-Rle of length 13 with 3 runs
##   Lengths: 5 3 5
##   Values : 0 1 2

Rle in genomics

Rle allows for us to compress space needed to store genomic scores and so the Bioconductor Rle and RleList objects allows us make use of this in R.

We can see the compression of space in action by reviewing our imported genomic scores as an RleList objects.

myBigWig[1:2]

## RleList of length 2
## $chr1
## numeric-Rle of length 195471971 with 2108 runs
##   Lengths:  72811054        65        26 ...        10        15 122614997
##   Values :   0.00000   0.69040   0.57802 ...   0.21374   0.20965   0.00000
## 
## $chr10
## numeric-Rle of length 130694993 with 1 run
##   Lengths: 130694993
##   Values :         0

Indexing an RLElist

To access elements of a RleList we can use our regular accessors for lists $ and [[]].

Here we retrieve the Rle object named chr1 containing all the genomic scores information for chromosome 1.

chr1_rle <- myBigWig$chr1
# Or
chr1_rle <- myBigWig[["chr1"]]
chr1_rle

## numeric-Rle of length 195471971 with 2108 runs
##   Lengths:  72811054        65        26 ...        10        15 122614997
##   Values :   0.00000   0.69040   0.57802 ...   0.21374   0.20965   0.00000

Indexing an RLE

Now we have an Rle object of our genomic scores on chromosome 1 we can index it just like we have for standard vectors. Here i retrieve values for all basepairs between 1 and 10

chr1_rle[1:10]

## numeric-Rle of length 10 with 1 run
##   Lengths: 10
##   Values :  0

Replacement in an RLE.

We can also replace values in a Rle, just as we would with a vector.

Here I replace values for all basepairs between 1 and 10 to 100

chr1_rle[1:10] <- 100
chr1_rle

## numeric-Rle of length 195471971 with 2109 runs
##   Lengths:        10  72811044        65 ...        10        15 122614997
##   Values : 100.00000   0.00000   0.69040 ...   0.21374   0.20965   0.00000

Converting to other data types

We can convert Rle’s to standard vectors.

rleAsVector <- as.vector(chr1_rle[1:10])
rleAsVector

##  [1] 100 100 100 100 100 100 100 100 100 100

Converting to other data types

Or to a data.frame

rleAsDF <- as.data.frame(chr1_rle[1:10])
rleAsDF

##    value
## 1    100
## 2    100
## 3    100
## 4    100
## 5    100
## 6    100
## 7    100
## 8    100
## 9    100
## 10   100

Working with Rle

Many vector operations and function work with Rle objects.

These include

Arithmetic and Mathematical operations.
Logical operations and comparisons/
Summary statistics.

Operations on RLE

We can use many simple arithmetic operations such as +, -, / and *****

chr1_rle+1000

## numeric-Rle of length 195471971 with 2109 runs
##   Lengths:        10  72811044        65 ...        10        15 122614997
##   Values :   1100.00   1000.00   1000.69 ...   1000.21   1000.21   1000.00

(chr1_rle+1000)*10

## numeric-Rle of length 195471971 with 2109 runs
##   Lengths:        10  72811044        65 ...        10        15 122614997
##   Values :   11000.0   10000.0   10006.9 ...   10002.1   10002.1   10000.0

Operations on RLE

Logical operations can be used with Rle objects just as with vectors.

For Rle objects, logical opertations return a logical Rle instead of standard logical vector.

chr1_rle < 10

## logical-Rle of length 195471971 with 20 runs
##   Lengths:        10  72823081        55 ...        65         2 122627007
##   Values :     FALSE      TRUE     FALSE ...      TRUE     FALSE      TRUE

Operations on RLE

We use this logical Rle to replace values less than 10 with 0 as we would use a logical vector with standard vectors.

chr1_rle[chr1_rle < 10] <- 0
chr1_rle

## numeric-Rle of length 195471971 with 122 runs
##   Lengths:        10  72823081        14 ...        65         2 122627007
##   Values :  100.0000    0.0000   10.0802 ...    0.0000   10.2513    0.0000

Operations on RLE

Many functions providing summary statisitics are also available to us including mean(), max() and sum() functions.

mean(chr1_rle)

## [1] 4.181372e-05

max(chr1_rle)

## [1] 100

sum(chr1_rle)

## [1] 8173.411

Operations on RLELists

Very usefully, We can also apply arithmetic and mathematical operations to whole RleLists as imported from the bigWig file.

myBigWig <- import.bw("data/TSS_ENCFF940MBK.bw",as="RleList")
myBigWig+10

## RleList of length 54
## $chr1
## numeric-Rle of length 195471971 with 2108 runs
##   Lengths:  72811054        65        26 ...        10        15 122614997
##   Values :   10.0000   10.6904   10.5780 ...   10.2137   10.2096   10.0000
## 
## $chr10
## numeric-Rle of length 130694993 with 1 run
##   Lengths: 130694993
##   Values :        10
## 
## $chr11
## numeric-Rle of length 122082543 with 1 run
##   Lengths: 122082543
##   Values :        10
## 
## $chr12
## numeric-Rle of length 120129022 with 1 run
##   Lengths: 120129022
##   Values :        10
## 
## $chr13
## numeric-Rle of length 120421639 with 1 run
##   Lengths: 120421639
##   Values :        10
## 
## ...
## <49 more elements>

Summaries on RLELists

Summary functions return a summary for every Rle object (here for chromosomes) within the RleList.

chromosomeMax <- max(myBigWig)
chromosomeMax[1:4]

##     chr1    chr10    chr11    chr12 
## 23.09015  0.00000  0.00000  0.00000

Subsetting RleLists with a GRanges.

We can in fact use GRanges objects to index our RleList objects.The GRanges provides the intervals from which genomic scores are retrieved. The resulting RleList object contains an entry with the scores for each interval in the GRanges object.

To demonstrate first we can retrieve the Myc Peaks calls which overlap the region we are reviewing.

myRanges <- GRanges("chr1",ranges = IRanges(72811055,72856974))
mycPeaks <- import.bed("data/Myc_Ch12_1_withInput_Input_Ch12_summits.bed")
mycPeaks <- resize(mycPeaks,50,fix="center")
newMycPeaks <- mycPeaks[mycPeaks %over% myRanges]
newMycPeaks

## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames            ranges strand |                   name     score
##          <Rle>         <IRanges>  <Rle> |            <character> <numeric>
##   [1]     chr1 72824021-72824070      * | Myc_Ch12_1_withInput..  18.55062
##   [2]     chr1 72844876-72844925      * | Myc_Ch12_1_withInput..   9.27569
##   -------
##   seqinfo: 21 sequences from an unspecified genome; no seqlengths

RleLists and GRanges.

Now we can simply index our RleList object by providing our GRanges as an index in [] brackets.

rleOverGranges <- myBigWig[newMycPeaks]
rleOverGranges

## RleList of length 2
## $chr1
## numeric-Rle of length 50 with 14 runs
##   Lengths:       6       3       2       3 ...       2       1       1       1
##   Values : 16.1810 18.4153 20.7201 19.5592 ... 20.7201 21.8973 23.0902 21.8973
## 
## $chr1
## numeric-Rle of length 50 with 9 runs
##   Lengths:        1       10        6        3 ...        8        4        2
##   Values : 11.25152  9.79599 10.51570  9.79599 ...  9.09299  8.40735  7.09102

RleLists and GRanges.

With the RleList containing our scores over the Myc peaks we can now gather summary statistics as with all RleList objects

sum(rleOverGranges)

##      chr1      chr1 
## 1009.0643  480.4242

Exporting an RLElist

We may wish to export our RleList or Rle objects back to a bigWig file.

We would need to convert any Rle objects to a RleList first in order to provide some chromosome/contig names. We can do this with the RleList() function and providing a chromosome/contig name to our Rle object.

myRleList <- RleList(chr1=chr1_rle)
myRleList

## RleList of length 1
## $chr1
## numeric-Rle of length 195471971 with 122 runs
##   Lengths:        10  72823081        14 ...        65         2 122627007
##   Values :  100.0000    0.0000   10.0802 ...    0.0000   10.2513    0.0000

Exporting an RLElist

Now we have our RleList object we export this to a bigWig using the export.bw() function.

export.bw(myRleList,con="chr1_Myc.bw")

Importing large files

In some cases we will only wish to import portions of a BigWig file for use in our analysis. This will save us memory and time when loading in big files.

To do this we can take advantage of the selection parameter in the import.bw function and the BigWigSelection() function.

Importing large files

When importing only a portion of a bigWig we simply need to specify a GRanges of regions we wish to retrieve to the BigWigSelection() function.

Here we will use the Myc Peaks in our window as the GRanges of regions for selection.

newMycPeaks

## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames            ranges strand |                   name     score
##          <Rle>         <IRanges>  <Rle> |            <character> <numeric>
##   [1]     chr1 72824021-72824070      * | Myc_Ch12_1_withInput..  18.55062
##   [2]     chr1 72844876-72844925      * | Myc_Ch12_1_withInput..   9.27569
##   -------
##   seqinfo: 21 sequences from an unspecified genome; no seqlengths

Importing large files

Now we pass the Myc peak GRanges object to the BigWigSelection() function.

We then supply the resulting BigWigSelection selection object to the selection parameter of import.bw() function.

mySelection <- BigWigSelection(newMycPeaks)
import.bw("data/TSS_ENCFF940MBK.bw", 
          selection=mySelection, 
          as="RleList")

## RleList of length 54
## $chr1
## numeric-Rle of length 195471971 with 26 runs
##   Lengths:  72824020         6         3 ...         4         2 122627046
##   Values :   0.00000  16.18099  18.41527 ...   8.40735   7.09102   0.00000
## 
## $chr10
## numeric-Rle of length 130694993 with 1 run
##   Lengths: 130694993
##   Values :         0
## 
## $chr11
## numeric-Rle of length 122082543 with 1 run
##   Lengths: 122082543
##   Values :         0
## 
## $chr12
## numeric-Rle of length 120129022 with 1 run
##   Lengths: 120129022
##   Values :         0
## 
## $chr13
## numeric-Rle of length 120421639 with 1 run
##   Lengths: 120421639
##   Values :         0
## 
## ...
## <49 more elements>

Time for an exercise.

Link_to_exercises

Link_to_answers

Genomic Scores In Bioconductor

Rockefeller University, Bioinformatics Resource Centre

http://rockefelleruniversity.github.io/Bioconductor_Introduction/

Genomic Scores

Genomic Scores

Genomic Scores

Genomic Scores

Our Genomic Scores data.

Our Genomic Scores data.

Our Genomic Scores data

Genomic Scores in Bioconductor.

Genomic Scores in Bioconductor.

Genomic Scores in Bioconductor.

Reading in a bedGraph.

Reading in a bedGraph.

Reading in a bedGraph.

Reading in a bigWig

Reading in a bigWig

GenomicScores as a GRanges

GenomicScores as a RLE

Rle in genomics

Rle in genomics

Rle in genomics

Rle in genomics

Rle in genomics

Indexing an RLElist

Indexing an RLE

Replacement in an RLE.

Converting to other data types

Converting to other data types

Working with Rle

Operations on RLE

Operations on RLE

Operations on RLE

Operations on RLE

Operations on RLELists

Summaries on RLELists

Subsetting RleLists with a GRanges.

RleLists and GRanges.

RleLists and GRanges.

Exporting an RLElist

Exporting an RLElist

Importing large files

Importing large files

Importing large files

Time for an exercise.