rearrvisr: rearrvisr: A package to detect, classify, and visualize...
In dorolin/rearrvisr: Detect, Classify, and Visualize Genome Rearrangements

Description Functions to convert and verify input files Functions to identify and summarize rearrangements Functions to visualize rearrangements Data References Examples

The rearrvisr package provides functions to identify and visualize inter- and intrachromosomal rearrangements between a focal genome and an ancestral genome reconstruction, or two extant genomes. Rearrangements, breakpoints, and synteny blocks are identified along the focal genome and output in a tabular format. Rearrangements and synteny blocks can be visualized along the focal genome by two graphical functions.

convertPQtree converts linearly encoded PQ-trees of an ancestral genome reconstruction, for example as output by the software ANGES (Jones et al. 2012), into a two-dimensional PQ-structure (i.e., a data frame representing the compared genome).
orderGenomeMap orders a one-dimensional genome map by genome segments (i.e., chromosomes or scaffolds) and by the position of markers within genome segments.
genome2PQtree converts an ordered, one-dimensional genome map into a two-dimensional PQ-structure (i.e., a data frame representing the compared genome).
checkInfile checks input data to ensure that their file formats are correct.

computeRearrs detects and classifies rearrangements along a focal genome relative to an ancestral genome reconstruction or an extant genome. The focal genome has to be provided as an ordered, one-dimensional genome map, and the compared genome as a two-dimensional PQ-structure.
filterRearrs filters detected rearrangements by their size.
getBreakpoints extracts breakpoint coordinates.
summarizeBlocks summarizes rearrangements and information on the alignment between the focal genome and the compared genome for each synteny block.

genomeImagePlot generates a plot that shows different classes of rearrangements along a given set of focal genome segments.
genomeRearrPlot generates a plot that shows synteny blocks between a focal and a compared genome in columns, and information on their alignment and rearrangements in rows, for a given set of focal genome segments.

The package includes example data (MEL_markers, SIM_markers, YAK_markers, and MSSYE_PQTREE_HEUR) generated from 12 publicly available Drosophila genome assemblies downloaded from Ensemble Release 91 (http://dec2017.archive.ensembl.org) and Ensemble Metazoa Release 37 (http://oct2017-metazoa.ensembl.org). Orthologs were identified with OMA standalone v2.2.0 (Altenhoff et al. 2015). MEL_markers, SIM_markers, and YAK_markers are maps of extant genomes from D. melanogaster, D. simulans, and D. yakuba, respectively. MSSYE_PQTREE_HEUR is a genome reconstruction of the ancestor of the melanogaster subgroup (Drosophila 12 Genomes Consortium 2007), computed with the software ANGES (Jones et al. 2012). See the package vignette for details.

Altenhoff, A.M. et al. (2015). The OMA orthology database in 2015: function predictions, better plant support, synteny view and other improvements. Nucleic Acids Research, 43, D240–D249. doi: 10.1093/nar/gku1158.

Drosophila 12 Genomes Consortium (2007). Evolution of genes and genomes on the Drosophila phylogeny. Nature, 450, 203–218. doi: 10.1038/nature06341.

Jones, B. R. et al. (2012). ANGES: reconstructing ANcestral GEnomeS maps. Bioinformatics, 28, 2388–2390. doi: 10.1093/bioinformatics/bts457

## verify input format of focal genome:
checkInfile(MEL_markers, "focalgenome", checkorder = TRUE)
## convert ancestral genome reconstruction to PQ-structure:
Comp_genome <- convertPQtree(MSSYE_PQTREE_HEUR)
## alternatively, convert extant genome to PQ-stucture:
## (note that minor scaffolds need to be excluded)
Comp_genome <- genome2PQtree(SIM_markers[is.element(SIM_markers$scaff,
                                                    c("2L", "2R", "3L", "3R", "X")), ])
## verify input format of compared genome:
checkInfile(Comp_genome, "compgenome", checkorder = TRUE)

## identify and summarize rearrangements:
SYNT <- computeRearrs(MEL_markers, Comp_genome, doubled = TRUE)
BLOCKS <- summarizeBlocks(SYNT, MEL_markers, Comp_genome,
                          c("2L", "2R", "3L", "3R", "X"))

## visualize rearrangements:
genomeImagePlot(SYNT, MEL_markers, c("2L", "2R", "3L", "3R", "X"))
genomeRearrPlot(BLOCKS, Comp_genome, c("2L", "2R", "3L", "3R", "X"),
                blockwidth = 1.15, y0pad = 3)