Search bioRxivSearch

Biology subjects

Pulicani, S.

Publications and source records attributed to Pulicani, S..

2 recordsLinked to original sources

Accounting for ambiguity in ancestral sequence reconstruction

The reconstruction of ancestral genetic sequences from the analysis of contemporaneous data is a powerful tool to improve our understanding of molecular evolution. Various statistical criteria defined in a phylogenetic framework can be used to infer nucleotide, aminoa-cid or codon states at internal nodes of the tree, for every position along the sequence. These criteria generally select the state that maximises (or minimises) a given criterion. Although it is perfectly sensible from a statistical perspective, that strategy fails to convey useful information about the level of uncertainty associated to the inference. The present study introduces a new criterion for ancestral nucleotide reconstruction that selects a single state whenever the signal conveyed by the data is strong, and a combination of multiple states otherwise. Simulations demonstrate the benefit of this approach with a substantial increase in the accuracy of ancestral sequence reconstruction without significantly compromising on the precision of the solutions returned.

evolutionary biology

Rearrangement Scenarios Guided By Chromatin Structure

Genome architecture can be drastically modified through a succession of large-scale rearrangements. In the quest to infer accurate ancestral rearrangement scenarios, it is often the case that parsimony principal alone does not impose enough constraints. Thus, the current challenge is to consider more biological information in the inference process. In previous work, we introduced a model for such a task, based on a partition into equivalence classes of the adjacencies between genes. Such a partition is amenable to the representation of spacial constraints as given by Hi-C data. A major open question is the validity of such a model. In this note, we show that the quality of a clustering of the adjacencies based on Hi-C data is directly correlated to the quality of a rearrangement scenario that we compute between Drosophila melanogaster and D. yakuba.

genomics