TE: Insertion/Deletion Dynamics for Transposable Elements

Description

TE package for analyzing insertion/deletion dynamics for transposable elements

Details

Provides functions to estimate the insertion and deletion rates of transposable element (TE) families. The estimation of insertion rate consists of an improved estimate of the age distribution that takes into account random mutations, and an adjustment by the deletion rate. This package includes functions EstDynamics and EstDynamics2 for analyzing the TE divergence, and visualization functions such as PlotFamilies and SensitivityPlot. This package implements the methods proposed in Dai et al (2018+).

Author(s)

Xiongtao Dai xdai@iastate.edu, Hao Wang Jan Dvorak Jeffrey Bennetzen Hans-Georg Mueller

Maintainer: Xiongtao Dai xdai@iastate.edu

References

Luo, Ming-Cheng, et al. (2017) "Genome sequence of the progenitor of the wheat D genome Aegilops tauschii." Nature 551.7681.

Dai, X., Wang, H., Dvorak, J., Bennetzen, J., Mueller, H.-G. (2018). "Birth and Death of LTR Retrotransposons in Aegilops tauschii". Genetics

LTR retrotransposons in Aegilops tauschii

Description

This data file contains the LTR retrotransposons in Ae. tauschii.

Format

A data frame with 18024 rows and 12 columns. Each row corresponds to a unique LTR retrotransposon, and each column corresponds to a feature of the LTR-RT. The columns are:

SeqID

LTR retrotransposon sequence ID

UngapedLen

Length of each LTR

Mismatch

Number of mismatches

Distance

Divergence, as defined by (# of mismatches) / (LTR length)

Chr

Chromosome number

Start

Start location in bp

Stop

Ending location in bp

GroupID

LTR retrotransposon Family ID

sup

Super family membership

recRt5

Recombination rate

nearOld

Whether the LTR-RT is near a gene that is colinear with wild emmer (TRUE) or not (FALSE)

cCodon

Whether the LTR-RT is near the start codon (1) or not (-1)

logDist

Log distance to the nearest gene in bp

distToGene

Distance to the nearest gene in bp

References

Luo, Ming-Cheng, et al. (2017) "Genome sequence of the progenitor of the wheat D genome Aegilops tauschii." Nature 551.7681.

Dvorak, J., L. Wang, T. Zhu, C. M. Jorgensen, K. R. Deal et al., (2018) "Structural variation and rates of genome evolution in the grass family seen through comparison of sequences of genomes greatly differing in size". The Plant Journal 95: 487-503.

Dai, X., Wang, H., Dvorak, J., Bennetzen, J., Mueller, H.-G. (2018). "Birth and Death of LTR Retrotransposons in Aegilops tauschii". Genetics

LTR retrotransposons in Arabidopsis lyrata

Description

This data file contains the LTR retrotransposons in Arabidopsis lyrata.

Format

A data frame with 397 rows and 7 columns. Each row corresponds to a unique LTR retrotransposon, and each column corresponds to a feature of the LTR-RT. The columns are:

SeqID

LTR retrotransposon sequence ID

UngapedLen

Length of each LTR

Mismatch

Number of mismatches

Distance

Divergence, as defined by (# of mismatches) / (LTR length)

sup

Super family membership

GroupID

LTR retrotransposon Family ID

thaID

Family name matched in the LTR-RT families of A. thaliana

References

Lamesch, Philippe, Tanya Z. Berardini, Donghui Li, David Swarbreck, Christopher Wilks, Rajkumar Sasidharan, Robert Muller et al. "The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools." Nucleic acids research 40, no. D1 (2011): D1202-D1210.

Dai, X., Wang, H., Dvorak, J., Bennetzen, J., Mueller, H.-G. (2018+). "Birth and Death of LTR Retrotransposons in Aegilops tauschii"

Estimate TE dynamics using mismatch data

Description

Given the number of mismatches and element lengths for an LTR retrotransposon family, estimate the age distribution, insertion rate, and deletion rates.

Usage

EstDynamics(mismatch, len, r = 0.013, perturb = 2, rateRange = NULL,
  plotFit = FALSE, plotSensitivity = FALSE, pause = plotFit &&
  plotSensitivity, main = sprintf("n = %d", n))

EstDynamics2(mismatch, len, r = 0.013, nTrial = 10L, perturb = 2,
  rateRange = NULL, plotFit = FALSE, plotSensitivity = FALSE,
  pause = plotFit && plotSensitivity, ...)

Arguments

Details

EstDynamics estimates the TE dynamics through fitting a negative binomial fit to the mismatch data, while EstDynamics2 uses a mixture model. For detailed implementation see References.

Value

EstDynamics returns a TEfit object, containing the following fields, where the unit for time is million years ago (Mya):

This function returns a TEfit2 object, containing all the above fields for TEfit and the following:

References

Dai, X., Wang, H., Dvorak, J., Bennetzen, J., Mueller, H.-G. (2018). "Birth and Death of LTR Retrotransposons in Aegilops tauschii". Genetics

Examples

# Analyze Gypsy family 24 (Nusif)
data(AetLTR)
dat <- subset(AetLTR, GroupID == 24 & !is.na(Chr))
set.seed(1)
res1 <- EstDynamics(dat$Mismatch, dat$UngapedLen, plotFit=TRUE, plotSensitivity=FALSE, pause=FALSE)

# p-value for testing a uniform insertion rate
res1$pvalue


# Use a mixture distribution to improve fit
res2 <- EstDynamics2(dat$Mismatch, dat$UngapedLen, plotFit=TRUE)

# A larger number of trials is recommended to achieve the global MLE
## Not run: 
res3 <- EstDynamics2(dat$Mismatch, dat$UngapedLen, plotFit=TRUE, nTrial=1000L)

## End(Not run)

Implements the master gene model in Marchani et al (2009)

Description

Implements the master gene model in Marchani et al (2009)

Usage

MasterGene(mismatch, len, r = 0.013, plotFit = FALSE,
  main = sprintf("n = %d", n))

Arguments

Details

For the method implemented see References.

Value

This function returns various parameter estimates described in Marchani et al (2009), containing the following fields. The unit for time is million years ago (mya):

References

Marchani, Elizabeth E., Jinchuan Xing, David J. Witherspoon, Lynn B. Jorde, and Alan R. Rogers. "Estimating the age of retrotransposon subfamilies using maximum likelihood." Genomics 94, no. 1 (2009): 78-82.

Examples

# Analyze Gypsy family 24 (Nusif)
data(AetLTR)
dat <- subset(AetLTR, GroupID == 24 & !is.na(Chr))
res2 <- MasterGene(dat$Mismatch, dat$UngapedLen, plotFit=TRUE)

Implements the matrix model in Promislow et al (1999)

Description

Implements the matrix model in Promislow et al (1999)

Usage

MatrixModel(mismatch, len, nsolo, r = 0.013, plotFit = FALSE,
  main = sprintf("n = %d", n))

Arguments

Details

For the method implemented see References.

Value

This function returns various parameter estimates described in Promislow et al. (1999), containing the following fields. The unit for time is million years ago (Mya):

References

Promislow, D., Jordan, K. and McDonald, J. "Genomic demography: a life-history analysis of transposable element evolution." Proceedings of the Royal Society of London B: Biological Sciences 266, no. 1428 (1999): 1555-1560.

Examples

# Analyze Gypsy family 24 (Nusif)
data(AetLTR)
dat <- subset(AetLTR, GroupID == 24 & !is.na(Chr))
res1 <- MatrixModel(dat$Mismatch, dat$UngapedLen, nsolo=450, plotFit=TRUE)

Plot the age distributions or insertion rates for multiple families.

Description

Plot the age distributions or insertion rates for multiple families.

Usage

PlotFamilies(resList, type = c("insRt", "ageDist"), ...)

Arguments

Value

A list of line data (plotDat) and peak locations (peakDat).

Examples

data(AetLTR)
copia3 <- subset(AetLTR, GroupID == 3 & !is.na(Chr))
gypsy24 <- subset(AetLTR, GroupID == 24 & !is.na(Chr))
res3 <- EstDynamics(copia3$Mismatch, copia3$UngapedLen)
res24 <- EstDynamics2(gypsy24$Mismatch, gypsy24$UngapedLen)

# Plot insertion rates
PlotFamilies(list(`Copia 3`=res3, `Gypsy 24`=res24))

# Plot age distributions
PlotFamilies(list(`Copia 3`=res3, `Gypsy 24`=res24), type='ageDist')

Generate sensitivity plots

Description

Create sensitivity plots of a few families to investigate different death rate scenarios

Usage

SensitivityPlot(resList, col, xMax, markHalfPeak = FALSE,
  famLegend = TRUE, rLegend = names(resList), ...)

Arguments

Examples

data(AetLTR)
copia3 <- subset(AetLTR, GroupID == 3 & !is.na(Chr))
copia9 <- subset(AetLTR, GroupID == 9 & !is.na(Chr))
res3 <- EstDynamics(copia3$Mismatch, copia3$UngapedLen)
res9 <- EstDynamics(copia9$Mismatch, copia9$UngapedLen)
SensitivityPlot(list(`Copia 3`=res3, `Copia 9`=res9))

Calcualte the KL divergence of a negative binomial fit to the mismatch data.

Description

Calcualte the KL divergence of a negative binomial fit to the mismatch data.

Usage

nbLackOfFitKL(res)

Arguments

Examples

# Analyze Gypsy family 24 (Nusif)
data(AetLTR)
dat <- subset(AetLTR, GroupID == 24 & !is.na(Chr))
set.seed(1)
res1 <- EstDynamics(dat$Mismatch, dat$UngapedLen, plotFit=TRUE, plotSensitivity=FALSE, pause=FALSE)
nbLackOfFitKL(res1)

Print a TEfit or TEfit2 object

Description

Print a TEfit or TEfit2 object

Usage

## S3 method for class 'TEfit'
print(x, ...)

## S3 method for class 'TEfit2'
print(x, ...)

Arguments