Sequence read coverage ratios for S.cerevisiae Dbf4-9myc sample.

Description

Sequence read coverage ratios for Dbf4-9myc sample (T9394 strain). The cells were stained with DNA dye and sorted based on DNA content into S or G2/M phase fractions. Extracted DNA was sequenced and mapped to sacCer3 genome. Unique reads for replicating (S) and non-replicating (G2/M) samples were calculated in 1 kb genomic bins. The ratio was created by dividing 'score' values from replicating sample by non- replicating sample 'score' values, adjusted by total number of reads. The ratio values were further adjusted by multiplying them by 1.402 to put the values onto biologically relevant relative copy number scale from 1 to 2.

Usage

data(Dbf4myc)

Format

data frame with 11356 rows and 7 variables:

chrom

short chromosome name

chromStart

left chromosome coordinate

chromEnd

right chromosome coordinate

name.rep

replicating sample name

name.nonRep

non-replicating sample name

ratio

ratio value in the current bin

ratioFactor

adjustment factor used for the current ratio

Source

S phase sample: SRA; G2 sample: SRA

References

Natsume et al. (2013) Mol Cell 50(5):661-74 (PubMed)

Examples

data(Dbf4myc)

Replication profile for wild type DS2 H.volcanii

Description

Replication profile for H.volcanii wild isolate DS2. Genomic DNA for deep sequencing was isolated from 100 ml culture in stationary phase (A650 > 1, DS2_stat sample) or 1 litre in exponential phase (A650 0.1, DS2_exp sample). Unique reads for the two samples were calculated in 1 kb genomic bins using ASM2568v1 genome assembly. The ratio was created by dividing 'score' values from replicating sample by non- replicating sample 'score' values, adjusted by total number of reads. The ratio values were further adjusted by multiplying them by 1.12 to put the values onto biologically relevant relative copy number scale from 1 to 2.

Usage

data(MFAseq)

Format

data frame with 3887 rows and 7 variables:

chrom

short chromosome name

chromStart

left chromosome coordinate

chromEnd

right chromosome coordinate

name.rep

replicating sample name

name.nonRep

non-replicating sample name

ratio

ratio value in the current bin

ratioFactor

adjustment factor used for the current ratio

Source

DS2_exp exponential phase sample: SRA; DS2_stat stationary sample: SRA

References

Hawkins et al. (2013) Nature 503(7477):544-547 (PubMed)

Examples

data(MFAseq)

Trep data calculated from syncSeq[["data"]]

Description

Trep is median replication time, expressed in minutes after release G1 arrest. It is calculated from multiple relative copy number datapoints across timeseries of a cell cycle experiment. For every genomic bin, a sigmoid function is fitted and its midpoint is reported.

Usage

data(TrepDF)

Format

data frame with 11341 rows and 5 variables:

chrom

short chromosome name

chromStart

left chromosome coordinate

chromEnd

right chromosome coordinate

Trep

calculated Trep value

TrepErr

error from sigmoid function fitting

References

Müller et al. (2014) NAR 42(1):e3 (PubMed)

Examples

data(TrepDF)

Sequence read coverage ratios for wild type S.cerevisiae W303

Description

Sequence read coverage ratios for wild type sample (T7107 strain). The cells were stained with DNA dye and sorted based on DNA content into S or G2/M phase fractions. Extracted DNA was sequenced and mapped to sacCer3 genome. Unique reads for replicating (S) and non-replicating (G2/M) samples were calculated in 1 kb genomic bins. The ratio was created by dividing 'score' values from replicating sample by non- replicating sample 'score' values, adjusted by total number of reads.

Usage

data(W303)

Format

data frame with 11350 rows and 7 variables:

chrom

short chromosome name

chromStart

left chromosome coordinate

chromEnd

right chromosome coordinate

name.rep

replicating sample name

name.nonRep

non-replicating sample name

ratio

ratio value in the current bin

ratioFactor

adjustment factor used for the current ratio

Source

S phase sample: SRA; G2 sample: SRA

References

Natsume et al. (2013) Mol Cell 50(5):661-74 (PubMed)

Examples

data(W303)

Sequence read coverage for wild type S.cerevisiae W303 non-replicating sample.

Description

Sequence read coverage for wild type non-replicating sample (T7107 strain). The cells were stained with DNA dye and sorted based on DNA content into G2/M phase fraction. Extracted DNA was sequenced and mapped to sacCer3 genome. Unique reads were calculated in 1 kb genomic bins.

Usage

data(W303_G2)

Format

data frame with 11350 rows and 5 variables:

chrom

short chromosome name

chromStart

left chromosome coordinate

chromEnd

right chromosome coordinate

name

sample name

score

read number in current bin

Source

SRA

References

Natsume et al. (2013) Mol Cell 50(5):661-74 (PubMed)

Examples

data(W303_G2)

Sequence read coverage for wild type S.cerevisiae W303 replicating sample

Description

Sequence read coverage for wild type replicating sample (T7107 strain). The cells were stained with DNA dye and sorted based on DNA content into S phase fraction. Extracted DNA was sequenced and mapped to sacCer3 genome. Unique reads were calculated in 1 kb genomic bins.

Usage

data(W303_S)

Format

data frame with 11820 rows and 5 variables:

chrom

short chromosome name

chromStart

left chromosome coordinate

chromEnd

right chromosome coordinate

name

sample name

score

read number in current bin

Source

SRA

References

Natsume et al. (2013) Mol Cell 50(5):661-74 (PubMed)

Examples

data(W303_S)

Normalised sequence read coverage ratios for wild type S.cerevisiae W303

Description

Sequence read coverage ratios for wild type sample (T7107 strain). The cells were stained with DNA dye and sorted based on DNA content into S or G2/M phase fractions. Extracted DNA was sequenced and mapped to sacCer3 genome. Unique reads for replicating (S) and non-replicating (G2/M) samples were calculated in 1 kb genomic bins. The ratio was created by dividing 'score' values from replicating sample by non- replicating sample 'score' values, adjusted by total number of reads. The ratio values were further adjusted by multiplying them by 1.41 to put the values onto biologically relevant relative copy number scale from 1 to 2.

Usage

data(W303norm)

Format

data frame with 11340 rows and 7 variables:

chrom

short chromosome name

chromStart

left chromosome coordinate

chromEnd

right chromosome coordinate

name.rep

replicating sample name

name.nonRep

non-replicating sample name

ratio

ratio value in the current bin

ratioFactor

adjustment factor used for the current ratio

Source

S phase sample: SRA; G2 sample: SRA

References

Natsume et al. (2013) Mol Cell 50(5):661-74 (PubMed)

Examples

data(W303norm)

A function to calculate Trep values from a sync-seq experiment calcTrep function fits a Boltzman sigmoid function into relative copy number datapoints for every genomic bin of the provided sync-seq merged dataframe. It then extracts time at which half of the cells have this genomic bin replicated (Trep). The output of the function is a dataframe containing Trep and TrepErr data for every genomic bin in a BED-like format.

Description

A function to calculate Trep values from a sync-seq experiment calcTrep function fits a Boltzman sigmoid function into relative copy number datapoints for every genomic bin of the provided sync-seq merged dataframe. It then extracts time at which half of the cells have this genomic bin replicated (Trep). The output of the function is a dataframe containing Trep and TrepErr data for every genomic bin in a BED-like format.

Usage

calcTrep(ratioDFs, times)

Arguments

Examples

TrepDF <- calcTrep(subset(syncSeq[["data"]],chrom=="chrI"),times=c(25,30,35,40,45,50,90))

A function to compare two replication profiles

Description

compareRatios takes two ratio dataframes that were binned the same way and uses z-score statistics to find p-values of their differences. The function outputs a combined dataframe containing the two input ratio dataframes in a long format with added 'p.value' column.

Usage

compareRatios(ratio1, ratio2)

Arguments

Examples

ratioDFs <- compareRatios(W303norm,Dbf4myc)

Guide dataframe for plotting smoothed sortSeq data

Description

Guide dataframe for plotting smoothed sortSeq data

Usage

data(guide)

Format

Dataframe with 2 rows and 6 variables:

order

Order to plot data in

name.rep

Name of replicating sample

name.nonRep

Name of non-replicating sample

raw

Should raw data be plotted?

smooth

Should smooth data be plotted?

color

Color to plot the profile in

Examples

data(guide)

Load a BED formatted file.

Description

The function reads BED formatted files. The BED file format defined by UCSC: http://genome.ucsc.edu/FAQ/FAQformat. First three columns ("chrom", "chromStart", "chromEnd") are mandatory. The file fields may be separated by tabs, spaces or commas. If the BED file contains a header, it will be ignored. If a genome mask dataframe is provided, only data intersected with the mask will be retained. Resulting data is ordered by "chromStart" columns.

Usage

loadBed(file, genome = NULL, name = NULL)

Arguments

Examples

W303_G2 <- loadBed(system.file("extdata/W303_G2.bed",package="Repliscope"), name='W303_G2')
W303_G2_chrI <- loadBed(system.file("extdata/W303_G2.bed",package="Repliscope"),
                       name='W303_G2',genome=sacCer3[["genome"]])

A helper function to create a gemome dataframe

Description

makeGenome is called by plotGenome() and plotCoverage() functions if a genome dataframe is not provided. It creates a BED-like dataframe containing unique chromosome names, their start coordinates (assumed 0), their ends (highest value in the corresponding 'chromEnd' of the BED dataframe) and 'midY' column containing half the max value of the "score" or "ratio" columns per chromosome. This later serves as y coordinate to add chromosome names. Axis name is passed via comment to the output dataframe. Extract it with 'attributes(genome)$axisName'.

Usage

makeGenome(DF, region = FALSE)

Arguments

Examples

genomeDF <- makeGenome(W303_G2)

A helper function to create axis ticks and human readable labels.

Description

makeLabels is called by plotGenome() and plotCoverage() functions. It creates a dataframe containing two columns: 'ticks' and 'labels'. 'Ticks' contains axis ticks coordinates, 'labels' will contain human readable lables for the ticks (using prefixes and optional units).

Usage

makeLabels(theMin, theMax, unit = "")

Arguments

Examples

labels <- makeLabels(0,1200000,"b")

A function to calculate 'score' ratio between two bed dataframes makeRatio merges two supplied bed dataframes, calculates ratio of their "score" values normalises the ratio by the 'score' sums.

Description

A function to calculate 'score' ratio between two bed dataframes makeRatio merges two supplied bed dataframes, calculates ratio of their "score" values normalises the ratio by the 'score' sums.

Usage

makeRatio(bedRep, bedNonRep)

Arguments

Examples

ratioDF <- makeRatio(W303_S,W303_G2)

A function to normalise ratio values from 'ratio' column of the provided dataframe to fit biologically-relevant scale. It scales values either using supplied 'rFactor' value or automatically to best fit 1 to 2 scale (the upper limit of the scale may be adjusted with the upperLimit parameter). Normalisation factor used is stored in 'ratioFactor' column and also passed as the dataframe comment. To extract it, use 'attributes(mergedBed)$comment'

Description

A function to normalise ratio values from 'ratio' column of the provided dataframe to fit biologically-relevant scale. It scales values either using supplied 'rFactor' value or automatically to best fit 1 to 2 scale (the upper limit of the scale may be adjusted with the upperLimit parameter). Normalisation factor used is stored in 'ratioFactor' column and also passed as the dataframe comment. To extract it, use 'attributes(mergedBed)$comment'

Usage

normaliseRatio(ratioDF, rFactor = NULL, upperLimit = 2, replace = TRUE)

Arguments

Examples

ratioDF <- normaliseRatio(W303) ## scales to 1 to 2 range, replaces original values.
ratioDF <- normaliseRatio(W303,rFactor=1.41,replace=FALSE)
# (multiplies score values by 1.41 and keeps the original values)

A function to boxplot 'score' column of a BED dataframe, per unique chromosome name in the 'chrom' column. The resulting plot also highlights outliers based on the inter quartile range (IQR). The genome wide median is plotted as a pink line through the boxplots.

Description

A function to boxplot 'score' column of a BED dataframe, per unique chromosome name in the 'chrom' column. The resulting plot also highlights outliers based on the inter quartile range (IQR). The genome wide median is plotted as a pink line through the boxplots.

Usage

plotBed(bed, plotting = TRUE)

Arguments

Examples

plotBed(W303_S)
plotObject <- plotBed(W303_G2,plotting=FALSE)

A function to scatterplot 'score' column of a BED dataframe plotCoverage function plots values in the 'score' column of the supplied bed dataframe as a function of chromosome coordinates. The genome wide median is plotted as a pink line.

Description

A function to scatterplot 'score' column of a BED dataframe plotCoverage function plots values in the 'score' column of the supplied bed dataframe as a function of chromosome coordinates. The genome wide median is plotted as a pink line.

Usage

plotCoverage(bed, region = FALSE, plotting = TRUE)

Arguments

Examples

plotCoverage(W303_G2)
plotObject <- plotCoverage(W303_S,plotting=FALSE)

plotGenome: plot replication profile.

Description

plotGenome plots scatterplot/barplot/polygon of 'score' and/or 'splineSmooth' columns values by genomic coordinates, either for the whole genome or a region. It also allows annotation with additional BED-formatted data. Ggplot2 package is used for plotting.

Usage

plotGenome(
  ratioDFs,
  geom = "geom_point",
  ylims = c(1, 2),
  plotting = TRUE,
  genome = NULL,
  region = FALSE,
  guide = NULL,
  lines = NULL,
  circles = NULL,
  rectangles = NULL,
  pointers = NULL,
  colourLines = "#00FF00",
  colourCircles = "#FFFFFF",
  colourRectangles = "#FF0000",
  colourPointers = "#FF7F00"
)

Arguments

Examples

plotGenome(sortSeq,geom="geom_ribbon",guide=guide,region="chrIX:250000-439885",
    lines=sacCer3[["cen"]],circles=sacCer3[["ori"]])
# plot data as polygon for the specified region of chromosome 9

plotGenome(syncSeq[["data"]],geom="geom_segment",guide=syncSeq[["guide"]],
    region="chrVII:0-1090944",genome=sacCer3[["genome"]],lines=sacCer3[["cen"]],
    circles=sacCer3[["ori"]],colourLines="black")

plotGenome(MFAseq,region='chr1:0-2848000')
# plot marker frequency analysis for H.volcanii isolate DS2

A function to plot a histogram of supplied ratio vector plotRatio plots histogram of values in a supplied vector using ggplot2 and highlights interval between 1 and 2 in green.

Description

A function to plot a histogram of supplied ratio vector plotRatio plots histogram of values in a supplied vector using ggplot2 and highlights interval between 1 and 2 in green.

Usage

plotRatio(ratio, plotting = TRUE)

Arguments

Examples

plotRatio(W303$ratio)
plotObject <- plotRatio(W303$ratio,plotting=FALSE)

A function to scatterplot 'Trep' column of a Trep dataframe plotTrep function plots values in the 'Trep' column of the supplied dataframe as a function of chromosome coordinates. The genome wide median is plotted as a pink line.

Description

A function to scatterplot 'Trep' column of a Trep dataframe plotTrep function plots values in the 'Trep' column of the supplied dataframe as a function of chromosome coordinates. The genome wide median is plotted as a pink line.

Usage

plotTrep(TrepDF, region = FALSE, plotting = TRUE)

Arguments

Examples

plotTrep(TrepDF,region="chrI")

A function to remove single chromosome data from a bed dataframe

Description

A function to remove single chromosome data from a bed dataframe

Usage

rmChr(bed, chr)

Arguments

Examples

bedDF <- rmChr(W303_S,"chrM") ## removes mitochondria

A function to remove outliers from the "score" column of a supplied bed dataframe There are three methods: max, IQR and median. Max is used to remove 1 or more maximum values; IQR uses interquartile range to detect outliers, while median method can be used to remove data based on genome-wide median.

Description

A function to remove outliers from the "score" column of a supplied bed dataframe There are three methods: max, IQR and median. Max is used to remove 1 or more maximum values; IQR uses interquartile range to detect outliers, while median method can be used to remove data based on genome-wide median.

Usage

rmOutliers(bed, method, n = 1, range = 3, loLim = 0.25, hiLim = NULL)

Arguments

Examples

bedDF <- rmOutliers(W303_S,method="max",n=2) ## removes 2 rows of data containing 3 top values
bedDF <- rmOutliers(W303_S,method="IQR",range=3) ## removes datapoints outside 3 x IQR above the 3rd
 # and below the 1st IQR.
bedDF <- rmOutliers(W303_S,method="median",loLim=0.25,hiLim=2) # removes datapoints that are lower
 # than 0.25 x genome median or above 2 x genome median.

A function to launch Repliscope in interactive mode (Shiny app).

Description

A function to launch Repliscope in interactive mode (Shiny app).

Usage

runGUI()

S.cerevisiae genome information

Description

sacCer3 meta information: chromosome sizes, centromere and replication origin positions.

Usage

data(sacCer3)

Format

List containing three dataframes

genome

Chromosome information dataframe

cen

Centromere information dataframe

ori

Replication origin information dataframe

Source

Replication origin information: (OriDB)

References

Siow et al. (2011) NAR 40(Database issue):D682-6 (PubMed)

Examples

data(sacCer3)

A function to smooth ratio values using cubic smoothing spline smoothRatio function splits values from 'ratio' column by chromosome and based the supplied 'groupMin' and 'split' parameters and then applies smooth.spline() function from R stats package. The supplied dataframe may contain multiple ratios, i.e. ratios produced using multiple replicating samples and/or multiple non-replicating samples. This must be reflected in 'name.rep' and 'name.nonRep' columns. In other words, different ratio dataframes may be merged using rbind() function before calling smoothRatio() function.

Description

A function to smooth ratio values using cubic smoothing spline smoothRatio function splits values from 'ratio' column by chromosome and based the supplied 'groupMin' and 'split' parameters and then applies smooth.spline() function from R stats package. The supplied dataframe may contain multiple ratios, i.e. ratios produced using multiple replicating samples and/or multiple non-replicating samples. This must be reflected in 'name.rep' and 'name.nonRep' columns. In other words, different ratio dataframes may be merged using rbind() function before calling smoothRatio() function.

Usage

smoothRatio(ratioDF, groupMin = 5, splitNum = 5)

Arguments

Examples

ratioDF <- smoothRatio(W303norm)

Replication profiles for wild type and Dbf4-9myc S.cerevisiae samples

Description

Replication profiles for wild type and Dbf4-9myc samples (T7107 and T9394 strains). The cells were stained with DNA dye and sorted based on DNA content into S or G2/M phase fractions. Extracted DNA was sequenced and mapped to sacCer3 genome. Unique reads for replicating (S) and non-replicating (G2/M) samples were calculated in 1 kb genomic bins. The ratio was created by dividing 'score' values from replicating sample by non- replicating sample 'score' values, adjusted by total number of reads. The ratio values were further adjusted by multiplying them by 1.41 and 1.402 for wild type and Dbf4-9myc samples, respectively, to put the values onto biologically relevant relative copy number scale from 1 to 2. The relative copy number values were smoothed using cubic spline and compared using z score statistics.

Usage

data(sortSeq)

Format

data frame with 22696 rows and 10 variables:

chrom

short chromosome name

chromStart

left chromosome coordinate

chromEnd

right chromosome coordinate

name.rep

replicating sample name

name.nonRep

non-replicating sample name

ratio

ratio value in the current bin

ratioFactor

adjustment factor used for the current ratio

group

Group number of the current bin

splineSmooth

Smoothed ratio value

p.value

Significance of ratio difference between Dbf4myc and W303 samples

Source

Dbf4myc S phase sample: SRA; Dbf4myc G2 sample: SRA; W303 S sample: SRA; W303 G2 sample: SRA

References

Natsume et al. (2013) Mol Cell 50(5):661-74 (PubMed)

Examples

data(sortSeq)

Replication profiles budding yeast arrest-release samples

Description

Replication profiles of wild type S. cerevisiae arrest-release samples (AUY077 strain). The cells were arrested in G1 with alpha-factor followed by release using pronase. The samples were collected before the release (aFactor) and various time intervals after the release (25min,30min,35min,40min,45min,50min and 90min) Extracted DNA was sequenced and mapped to sacCer3 genome. Unique reads for replicating (post-release) and non-replicating (aFactor) samples were calculated in 1 kb genomic bins. The ratios were created by dividing 'score' values from replicating samples by non-replicating sample 'score' values, adjusted by total number of reads. The ratio values were further adjusted based on bulk genome replication (as determined by flow cytometry), to put the values onto biologically relevant relative copy number scale from 1 to 2. The relative copy number values were smoothed using cubic spline.

Usage

data(syncSeq)

Format

List containing two data frames

data

syncSeq replication profiles data. Columns: chrom (short chromosome name), chromStart (left chromosome coordinate), chromEnd (right chromosome coordinate), name.rep (replicating sample name), name.nonRep (non-replicating sample name), ratio (ratio value in the current bin), ratioFactor (adjustment factor used for the current ratio), group (Group number of the current bin), splineSmooth (Smoothed ratio value)

guide

Guide dataframe for plotting the syncSeq data order (Order to plot data in), name.rep (Name of replicating sample), name.nonRep (Name of non-replicating sample), raw (Should raw data be plotted?), smooth (Should smooth data be plotted?), color (Color to plot the profile in)

Source

GEO

References

Müller et al. (2014) NAR 42(1):e3 (PubMed)

Examples

data(syncSeq)

A function to remove outliers from the "ratio" column of a supplied ratio dataframe trimRatio is applied to the calculated ratio of read counts from a replicating to a non-replicating samples.

Description

A function to remove outliers from the "ratio" column of a supplied ratio dataframe trimRatio is applied to the calculated ratio of read counts from a replicating to a non-replicating samples.

Usage

trimRatio(ratioDF, loLim, hiLim)

Arguments

Examples

W303 <- trimRatio(W303,0.5,1.5)