Breaking Good: Accounting for Fragility of Genomic Regions in Rearrangement Distance Estimation

Abstract

Models of evolution by genome rearrangements are prone to two types of flaws: One is to ignore the diversity of susceptibility to breakage across genomic regions, and the other is to suppose that susceptibility values are given. Without necessarily supposing their precise localization, we call “solid” the regions that are improbably broken by rearrangements and “fragile” the regions outside solid ones. We propose a model of evolution by inversions where breakage probabilities vary across fragile regions and over time. It contains as a particular case the uniform breakage model on the nucleotidic sequence, where breakage probabilities are proportional to fragile region lengths. This is very different from the frequently used pseudouniform model where all fragile regions have the same probability to break. Estimations of rearrangement distances based on the pseudouniform model completely fail on simulations with the truly uniform model. On pairs of amniote genomes, we show that identifying coding genes with solid regions yields incoherent distance estimations, especially with the pseudouniform model, and to a lesser extent with the truly uniform model. This incoherence is solved when we coestimate the number of fragile regions with the rearrangement distance. The estimated number of fragile regions is surprisingly small, suggesting that a minority of regions are recurrently used by rearrangements. Estimations for several pairs of genomes at different divergence times are in agreement with a slowly evolvable colocalization of active genomic regions in the cell.

Zombi: A phylogenetic simulator of trees, genomes and sequences that accounts for dead lineages

“…Simulations with Zombi are fast: with a starting genome of 500 genes and a species tree of 2000 taxa (extinct + extant), it takes around 1 minute on a 3.4Ghz laptop to simulate all the genomes ( Figure S6). We validated that the distribution of waiting times between successive events was following an exponential distribution ( Figure S7 and S8), that the distribution of intergene sizes at equilibrium was following a flat Dirichlet distribution, as expected from Biller et al 2016 ( Figure S9), that the number of events and their extension occurs with a frequency according to their respective rates ( Figure S10) and that the gene family size distribution followed a power-law when duplication rates are higher than loss rates and stretched-exponential in the opposite case ( Figure S11). We also checked by hand the validity of many simple scenarios to detect possible inconsistencies in the algorithm.…”

“…For example, it is possible to use a species tree input by the user, to generate species trees with variable extinction and speciation rates, or to control the number of living lineages at each unit of time ( Figure S5). At the genome level, Zombi can simulate genomes using branch-specific rates (Gu mode, allowing the user to simulate very specific scenarios such as one in which a certain lineage experiences a massive loss of genes), gene-family specific rates (Gm mode, which makes easier the process of using rates estimated from real datasets) and genomes accounting for intergenic regions (Gf mode) of variable length (drawn from a flat Dirichlet distribution (Biller et al 2016) . At the sequence level, finally, the user can fine-tune the substitution rates to make them branch specific.…”

Genome rearrangements with indels in intergenes restrict the scenario space

“…First, the definition of weighted genomes [1, 3] opens combinatorial questions, one of which being the transformation of a genome into another in a minimum number of steps. In a previous paper [3] we solved the strict version of this problem, where genomes were forced to have the same total intergene sizes and only wDCJs were allowed.…”

“…In a previous publication [1], we have argued that intergenic sizes were a crucial parameter to infer genome rearrangement distances. Indeed, ignoring this information, as all published distance estimations were doing so far [2], leads to strong biases in all estimations and validation procedures.…”

“…Indeed it is known that such a space is huge [4, 5], which makes it hard to analyze; several methods have thus been devised to add genomic or epigenomic constraints to restrict the search space [6–8]. So far, the potential of intergenic sizes has only been explored for distance computations [1, 3]. We show that it can also contain information on the scenarios, by characterizing categories of DCJs that can be used in optimal DCJs and indels scenarios.…”