Comparative Methods for Reconstructing Ancient Genome Organization

Abstract

Comparative genomics considers the detection of similarities and differences between extant genomes, and, based on more or less formalized hypotheses regarding the involved evolutionary processes, inferring ancestral states explaining the similarities and an evolutionary history explaining the differences. In this chapter, we focus on the reconstruction of the organization of ancient genomes into chromosomes. We review different methodological approaches and software, applied to a wide range of datasets from different kingdoms of life and at different evolutionary depths. We discuss relations with genome assembly, and potential approaches to validate computational predictions on ancient genomes that are almost always only accessible through these predictions.

Analysis of local genome rearrangement improves resolution of ancestral genomic maps in plants

“…Most popular methods for reconstructing the ancestral genome architecture follow one of two strategies: either they make use of a genome rearrangement model to derive parsimonious rearrangement scenarios that explain the observed differences in modern genome architectures, or they infer syntenic blocks. These constitute conserved neighborhoods of individual pairs of markers, also denoted adjacencies, or neighborhoods of marker sets comprising more than two markers [6].…”

“…Alternative methods such as PMAG [15] and DeCoStar [16] use likelihood estimation to infer ancestral gene orders, yet are limited to process adjacencies only. Nevertheless, DeCoStar infers evolutionary trees of marker adjacencies and therefore can handle evolutionary events such as duplication, insertion, and loss [6].…”

“…Independent of the strategy, the outcome of both approaches are contiguous ancestral regions (CARs), that detail the composition of ancestral chromosomes (or parts thereof ) as well as the relative order of their contained genomic markers. Such order may not be entirely fixedthe resolution of ancestral marker orders depends on the input data [17], the method of choice [6], and its alacrity to proclaim neighborship relations derived from the analysis of extant genomes as ancestral. Many methods output multiple candidates for ancestral gene order, either because their strategy is based on sampling, or because it is subject to optimization criteria that give rise to many co-optimal solutions.…”

Biological computation and computational biology: survey, challenges, and discussion

“…These models have been used in many other elds of computational biology, such as scaolding [2], reference guided genome assembly [183], phylogenomics [279], and cancer genomics [384]. Comprehensive reviews of genome rearrangement models and its applications can be found in [113,142,383,15,144,311].…”

“…Up to now, biological computation techniques have been developed to solve a variety of problems, but when it comes to solve specic problems in computational biology, these techniques are mainly designed as sequential methods, handling manageable data sizes, or as distributed methods, handling big biological data while making use of the emerging new parallel infrastructures. On the other hand, only a limited number of bioinformatics software (that we also call software biology tools") are making use of the parallel frameworks, while properly 15 https://cme.h-its.org/exelixis/software.html/ 16 to a new challenge in biological computation that requires further attention from computer scientists, statisticians, and mathematicians. The sought biological software tools, that we can call a second generation of biological software tools , need to be reusable and portable.…”