A quick guide to crossover identification using RTIGER

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Introduction

Accurate identification of meiotic crossing-over sites (COs) is essential for correct genotyping of recombining samples. RTIGER is a method for predicting genome-wide COs using allele-counts at pre-defined SNP marker positions. RTIGER trains a rigid Hidden Markov Model (rHMM¹) where each genomic state (homozygous parent 1, homozygous parent 2 or heterozygous) corresponds to respectively one hidden state and the allele-counts as the observed variable. COs are identified as transitions in the HMM state.

To account for sparse sampling of individual marker positions in the sequencing data, RTIGER uses Viterbi Path Algorithm and the rigidity parameter. This parameter defines the minimum number of SNP markers required to support a state-transition. This filters out low-confidence state-transitions, improving COs identification performance.

Installation

Pre-Requisites

R: Version > 3.6
Julia: Julia needs to be installed and available in the environment². Tested for Julia v1.0.5.
Some bioconductor libraries require manual installation. Use the following code in the R console to install them:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(version = "3.14")

BiocManager::install(c("GenomicRanges", "GenomeInfoDb", "TailRank", "IRanges", "Gviz"))

Install

RTIGER can be installed directly from CRAN using:

install.packages("RTIGER")

Additionally, the development version (with the most recent features and bug-fixes) can be downloaded from GitHub using the following:

install.packages("devtools")
library(devtools)
install_github("rfael0cm/RTIGER", ref = 'main')

Preparing input data

RTIGER uses the allele-count information at the SNP marker positions. The SNP markers correspond to differences between the two genotypes (i.e. parent_1 vs parent_2). RTIGER requires as input one allele-count file for each sample. The allele-count file should be in tab-separated value format, where each row corresponds to a SNP marker. For example:

Example allele frequency file
Chr1	37388	C	0	T	2
Chr1	71348	T	1	G	3
Chr2	18057	A	0	C	1
Chr2	38554	G	0	A	2
Chr2	75348	A	1	T	0
Chr3	32210	T	2	G	0

Here the columns are:

Chromosome ID
Position of the SNP marker
Reference allele
Reference allele read-count
Alternate allele
Alternate allele read-count

The SNPs can be identified using any generic SNP identification pipeline³.

SNPs in repetitive regions should be filtered out. Further, as crossing-over usually takes place in syntenic regions between the two genomes, for best results, only SNPs in syntenic regions should be selected as markers. If whole genome assemblies are present for both genomes, then this can be easily achieved using methods like SyRI⁴.

Notes

RTIGER assumes that all samples have similar sequencing coverage and, hence, similar distribution of the allele-count values. It does not check or normalise for sequencing coverage variation arising due to strong differences in sequencing coverage. Cases with extremely low coverage may result in suboptimal results and should be filtered out.
Cossing-over resolution depends on sequenced marker density. Low sequencing coverage could result in few informative markers, which in turn could decrease resolution CO prediction.
Although RTIGER is designed to be robust against individual outliers, the user should check for “bad” markers, i.e. marker positions that are prone to mismapping. These markers result in high allele-count at that position as well as in a high, artificial agreement in this position across samples.

Using RTIGER

Setting up Julia environment

RTIGER uses Julia to perform computationally intensive model training. All Julia packages that are used by RTIGER can be installed using using:

library(RTIGER)
setupJulia()

This step is necessary when using RTIGER for the first time, but can be skipped for later analysis as all required Julia packages would already be installed.

The Julia functions need to be loaded in the R environment using:

sourceJulia()

This step is required every time when using RTIGER.

Creating input objects

The primary input for RTIGER is a data-frame termed expDesign. The first column of expDesign should have paths to allele-count files for all samples and the second column should have unique samples IDs⁵.

# Get paths to example allele count files originating from a
# cross between Col-0 and Ler accession of the A.thaliana
file_paths = list.files(system.file("extdata",  package = "RTIGER"), full.names = TRUE)

# Get sample names
sampleIDs <- basename(file_paths)

# Create the expDesign object
expDesign = data.frame(files=file_paths, name=sampleIDs)

print(expDesign)
#>                                                           files         name
#> 1 /tmp/RtmpOQjtyL/Rinst79bd6801ed99/RTIGER/extdata/sampleAA.txt sampleAA.txt
#> 2 /tmp/RtmpOQjtyL/Rinst79bd6801ed99/RTIGER/extdata/sampleAC.txt sampleAC.txt
#> 3  /tmp/RtmpOQjtyL/Rinst79bd6801ed99/RTIGER/extdata/sampleB.txt  sampleB.txt

RTIGER also requires chromosome lengths for the parent 1. These need to be provided as a named vector where the values are chromosome lengths and the names are chromosome ids.

# Get chromosome lengths for the example data included in the package
chr_len <- RTIGER::ATseqlengths
names(chr_len) <- c('Chr1' , 'Chr2', 'Chr3', 'Chr4', 'Chr5')
print(chr_len)
#>     Chr1     Chr2     Chr3     Chr4     Chr5 
#> 34964571 22037565 25499034 20862711 31270811

Finding crossing-over sites using RTIGER

RTIGER does model training, CO identification, and creates summary plots using the RTIGER function.

myres = RTIGER(expDesign = expDesign,
               outputdir = "/PATH/TO/OUTPUT/DIRECTORY",
               seqlengths = chr_len,
               rigidity = 200)

The rigidity parameter defines the required minimum number of continuous markers that together support a state change of the HMM model (Figure 1). Smaller rigidity values increase the sensitivity in detecting COs that are close to each other, but may result in false-positive CO identification because of variation in sequencing coverage. Larger rigidity values improve precision but COs that are close to each other might not be identified. Users are supposed to test and adjust rigidity based on their specific experimental setup.

Effect of different R values on CO identificaion. The top panel shows the allele count for parent 1 (red) and parent 2 (blue) at the marker positions on a chormosomes. Remaining panels show the annotation of genomic regions by RTIGER for different values of R. Here, red blocks are regions that are homozygous for parent 1, purple blocks are for heterozygous regions, and blue blocks are for regions homozygous for parent 2.

RTIGER Output

RTIGER identifies COs for each sample level and provides summary plots and statistics for each sample as well as for the entire population.

Per sample output

RTIGER creates a folder for each sample in the outputdir. This folder contains:

GenotypePlot.pdf: Graphical representation of the allele-counts, allele-count ratio, and genotypes
CompleteBlock.bed: BED file providing genomic regions corresponding to different genotypes
P1/P2/Het.bed: BED files containing the markers present in genomic regions having genotype: homozygous parent 1, homozygous parent 2, or heterozygous, respectively
P1/P2.bw: BigWig file containing the number of reads per marker position supporting parent 1 and parent 2, respectively
CountRatio.bw: BigWig file containing the ratio of number of reads supporting parent 1 to number of reads supporting number 2 at the marker positions

Summary plots

In addition to per sample output, RTIGER also generates three summary plots for the samples.

COs-per-Chromosome.pdf: Distribution of number of cross-overs per chromosome
CO-count-perSample.pdf: Number of cross-overs in each sample
Goodness-Of-fit.pdf: The goodness of fit file contains the histograms of the count ratio distribution for each state. In other words, the count ratio (counts of reference allele/total number of allele count) for all the positions decoded as state i will be used to plot the histogram of the state i. For example, in the case of three states (paternal, heterozygous, and maternal), the first histogram is the paternal state for which the distribution of count ratio is close to one (since the reference allele is the allele inherited from the paternal line). On the other hand, the maternal state histogram has a distribution close to zero. Reasonably, the heterozygous state has a distribution close to 0.5 since the count ratio for the observations heterozygous regions should be half and half. Real-life experiments are more complex and might deviate from the theoretical values, i.e the heterozygous count ratio is skewed to 0 or 1 indicating preference in the annotation by one of the parents, contamination in some samples, the model did not converge adequately, or whether the number of states is correct. These plots help the user to decide if the model fitted for the data they have is adequate or needs to change the parameters.

Analysing backcrossed populations

Backcrossed populations are formed by crossing a hybrid organism with one of its parent. These populations are different from the populations based on outcrossing as only two genomic states are possible (homozygous for the backrossed parent and heterozygous for both parents). To identify COs in such population, set nstates=2 in the RTIGER command.

myres = RTIGER(expDesign = expDesign, 
               outputdir = "PATH/TO/OUTPUT/DIR",
               seqlengths = chr_len,
               rigidity = 200, 
               nstates=2)

Citation

Campos-Martin R, et al., Reliable genotyping of recombinant genomes using a robust hidden Markov Model, 2022

Session info

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#> loaded via a namespace (and not attached):
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#> [16] jquerylib_0.1.4        pillar_1.8.1           TailRank_3.2.2        
#> [19] glue_1.6.2             BiocGenerics_0.42.0    GenomeInfoDbData_1.2.8
#> [22] lifecycle_1.0.3        plyr_1.8.8             stringr_1.5.0         
#> [25] zlibbioc_1.42.0        munsell_0.5.0          gtable_0.3.1          
#> [28] evaluate_0.20          Biobase_2.56.0         IRanges_2.30.1        
#> [31] fastmap_1.1.1          GenomeInfoDb_1.32.4    class_7.3-21          
#> [34] fansi_1.0.4            highr_0.10             Rcpp_1.0.10           
#> [37] scales_1.2.1           RTIGER_2.1.0           cachem_1.0.7          
#> [40] S4Vectors_0.34.0       jsonlite_1.8.4         XVector_0.36.0        
#> [43] askpass_1.1            ggplot2_3.4.1          digest_0.6.31         
#> [46] stringi_1.7.12         dplyr_1.1.0            oompaBase_3.2.9       
#> [49] GenomicRanges_1.48.0   grid_4.2.2             cli_3.6.0             
#> [52] tools_4.2.2            bitops_1.0-7           magrittr_2.0.3        
#> [55] sass_0.4.5             RCurl_1.98-1.10        proxy_0.4-27          
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#> [61] rmarkdown_2.20         extraDistr_1.9.1       rstudioapi_0.14       
#> [64] JuliaCall_0.17.5       R6_2.5.1               compiler_4.2.2

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