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RepliSeq

This is an R package which aims at processing Repli-seq data. It takes raw counts (Bedgraph file format) as input and makes it then quick and easy to further analyze the DNA replication timing with a set of functions to manipulate and vizualize the data.
RepliSeq functions include loading multi-fraction Repli-seq assay data as count matrices (from 2 to N fractions depending on the experimental design) but also rescaling profiles to any resolution and calculting the Replication timing as the S50 (moment in the S-phase where a loci reaches 50% of its total replication on a scale from 0, early, to 1, late)

License: GPL v3

Installation:

You can install this package by entering the following within R:

# get the development version from GitHub using devtools :
# install.packages("devtools")
devtools::install_github("SamiLhll/RepliSeq",build_vignettes = TRUE)
# building the vignette makes the installation a bit longer but its mandatory so ou can access it by doing :   
vignette("RepliSeq")

Requirements:

The function writeBigWig() requires UCSC's wigToBigWig application to be installed on the computer.
It can be found at encodeproject

Usage :

Check out the vignette for extended documentation.

Loading repliseq data :

The function readRS(path_data,fractions) reads Repli-seq assays from multiple files (one file per fraction) and returns a dataframe.
It requires bedgraph inputs (see bedgraph specifications) with a one line header but no other comments such as:

track type=bedGraph name=NT_chr22-s1 description=50kb
chr22 0 50000 0
chr22 50000 100000 0


### args :
temp_paths <- c("../inst/extdata/NT_chr22-s1.bdg","../inst/extdata/NT_chr22-s2.bdg",
                "../inst/extdata/NT_chr22-s3.bdg","../inst/extdata/NT_chr22-s4.bdg",
                "../inst/extdata/NT_chr22-s5.bdg","../inst/extdata/NT_chr22-s6.bdg")
temp_fractions <- c("S1","S2","S3","S4","S5","S6")

### 2 fractions RepliSeq
# apply function :
RS_early <- readRS(temp_paths[1:2],temp_fractions[1:2])

### 6 fractions RepliSeq
# apply function :
RS_all <- readRS(temp_paths,temp_fractions)

### 1 fraction RepliSeq ( for S0 controls )
# apply function : 
RS_S0 <- readRS("../inst/extdata/NT_chr22-s0.bdg","S0")

### Result :

tail(RS_early)
chr start stop S1 S2
chr22 51000000 51050000 12.392 4.929
chr22 51050000 51100000 11.604 5.887
chr22 51100000 51150000 12.568 7.941
chr22 51150000 51200000 9.853 5.887
chr22 51200000 51250000 2.584 1.711
chr22 51250000 51300000 0.000 0.000

Compute the replication timing (S50) :

The function calculateS50(rs_assay) returns a dataframe composed of genomic coordinates associated with replication timing as an S50 (Chen et al. (2010)) value comprised within 0 (early replicating) and 1 (late replicating).


temp_rs <- data.frame(chr = rep("chr1",7),
                      start = seq(0,6000,1000),
                      stop = seq(1000,7000,1000),
                      S1 = c(0,0,0,1,1,1,1),
                      S2 = c(0,0,1,1,1,1,0),
                      S3 = c(0,1,1,1,1,0,0),
                      S4 = c(1,1,1,1,0,0,0))


temp_S50 <- RepliSeq::calculateS50(temp_rs)

# Result :

print(temp_S50)
chr start stop S50
chr1 0 1000 0.875
chr1 1000 2000 0.750
chr1 2000 3000 0.625
chr1 3000 4000 0.500
chr1 4000 5000 0.375
chr1 5000 6000 0.250
chr1 6000 7000 0.125

Compare the total replication among Repli-Seq assays :

As introduced in Brison,.O, El-Hilali,S. et al. (2019), Repli-Seq assays could be compared to quantitatively assess which parts of DNA were the most affected by Aphidicolin. The function calculateURI() calculates this Under Replication Index (URI) from two Repli-Seq assays loaded with readRS().

####### load second Repli-seq assay for comparison 
####### 6 fractions RepliSeq

# args :

aph_paths <- c("../inst/extdata/Aph_chr22-s1.bdg","../inst/extdata/Aph_chr22-s2.bdg",
               "../inst/extdata/Aph_chr22-s3.bdg","../inst/extdata/Aph_chr22-s4.bdg",
               "../inst/extdata/Aph_chr22-s5.bdg","../inst/extdata/Aph_chr22-s6.bdg")
aph_fractions <- temp_fractions

# read :

RS_aph_all <- readRS(aph_paths,aph_fractions)

# apply function :

aph_nt_uri <- calculateURI(RS_aph_all,RS_all)

# Result :

tail(aph_nt_uri)
chr start stop sum_x sum_y mean_xy URI
chr22 51000000 51050000 27.581 30.869 29.225 -1.37048107
chr22 51050000 51100000 30.556 31.274 30.915 -0.66372243
chr22 51100000 51150000 38.770 36.226 37.498 0.05718338
chr22 51150000 51200000 32.394 26.028 29.211 1.24529116
chr22 51200000 51250000 10.063 8.533 9.298 0.82273039
chr22 51250000 51300000 0.000 0.000 0.000 NaN

Getting help :

Need help, Identified a bug, or want to see other features implemented ?
Feel free to open an issue here or send an email to the authors :

Sami EL HILALI and Chunlong CHEN

References:

Brison O., El-Hilali S., Azar, D., Koundrioukoff1 S., Schmidt M., Naehse-Kumpf V., Jaszczyszyn Y., Lachages A.M., Dutrillaux B., Thermes C., Debatisse M. and Chen C.L. (2019) Transcription-Mediated Organization of the Replication Initiation Program Across Large Genes Sets Up Common Fragile Sites Genome-Wide. Nat. Commun. 10, 5693

Chen C.L., Rappailles A., Duquenne L., Huvet M., Guilbaud G., Farinelli L, Audit B, d'Aubenton-Carafa Y., Arneodo A., Hyrien O. and Thermes C. (2010) Impact of replication timing on non-CpG and CpG substitution rates in mammalian genomes. Genome. Res. 20, 447-457.