Biology subjects

Oliver, J. L.

Publications and source records attributed to Oliver, J. L..

5 recordsLinked to original sources

bioRxiv2024

An accelerating, decreasing phylogenetic trend in SARS-CoV-2 genome compositional heterogeneity during the pandemic

The rapid evolution of SARS-CoV-2 during the pandemic, driven by a plethora of mutations, many of which enable the virus to evade host resistance, has likely altered its genomes compositional structure (i.e. the arrangement of compositional domains of varying lengths and nucleotide frequencies within the genome). To explore this hypothesis, we summarize the evolutionary effects of these mutations by computing the Sequence Compositional Complexity (SCC) in random datasets of fully sequenced genomes. Phylogenetic ridge regression of SCC against time reveals a striking downward evolutionary trend, as well as an increasing rate of change, suggesting the ongoing adaptation of the viruss genome structure to the human host. Other genomic features, such as strand asymmetry, the effective number of K-mers, and the depletion of CpG dinucleotides, each linked to the viruss adaptation to its human host, also exhibit decreasing phylogenetic trends over the course of the pandemic, along with strong phylogenetic correlations to SCC. Overall, our findings suggest an accelerated, genome-wide evolutionary trend toward a more symmetric and homogeneous genome compositional structure in SARS-CoV-2.

evolutionary biology↗

bioRxiv2022

The emergence of variants with increased fitness accelerates the slowdown of genome sequence heterogeneity in the SARS CoV 2 coronavirus

Since the outbreak of the COVID-19 pandemic, the SARS-CoV-2 coronavirus has accumulated an important amount of genetic and genomic variability through mutation and recombination events. To test evolutionary trends that could inform us on the adaptive process of the virus to its human host, we summarize all this sequence variability by computing the Sequence Compositional Complexity (SCC) in more than 23,000 high-quality coronavirus genome sequences from across the globe, covering the period spanning from the start of the pandemic in December 2019 to March 2022. In early samples, we found no statistical support for any trend in SCC values over time, although the virus as a whole appears to evolve faster than Brownian Motion expectation. However, in samples taken after the first Variant of Concern (VoC) with higher transmissibility (Alpha) emerges, and controlling for phylogenetic and sampling effects, we were able to detect a statistically significant trend for decreased SCC values over time. SARS-CoV-2 evolution towards lower values of genome heterogeneity is further intensified by the emergence of successive, widespread VoCs. Concomitantly to the temporal reduction in SCC, its absolute evolutionary rate kept increasing toward the present, meaning that the SCC decrease itself accelerated over time. As compared to Alpha or Delta variants, the currently dominant VoC, Omicron, shows much stronger trends in both SCC values and rates over time. These results indicate that the increases in fitness of variant genomes associated to a higher transmissibility leads to a reduction of their genome sequence heterogeneity, thus explaining the general slowdown of SCC along with the pandemic course.

genomics↗

bioRxiv2021

Accelerated decline of genome heterogeneity in the SARS-CoV-2 coronavirus

During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to search for adaptive evolutionary trends in real-time. The mutation rate in coronavirus is known to be lower than expected for neutral evolution, thus suggesting a role for natural selection. We summarize the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To study the full range of SCC diversity, random samples of high-quality coronavirus genomes covering pandemic time span were analyzed. We then search for evolutionary trends that could inform on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC, and an increasing one for its absolute evolutionary rate, are detected, pointing to a role for selection in the evolution of SCC in coronavirus genomes. We conclude that the higher fitness of variant genomes leads to adaptive trends of SCC over pandemic time in the coronavirus.

genomics↗

bioRxiv2020

Tracking evolutionary trends towards increasing complexity: a case study in Cyanobacteria

Progressive evolution, the tendency towards increasing complexity, is a controversial issue in Biology, whose resolution requires a proper measurement of complexity. Genomes are the best entities to address this challenge, as they record the history and information gaining of organisms in their ongoing biotic and environmental interactions. Using six metrics of genome complexity, none of which is primarily associated to biological function, we measure genome complexity in 91 genomes from the phylum Cyanobacteria. Several phylogenetic analyses reveal the existence of progressive evolution towards higher genome complexity: 1) all the metrics detect strong phylogenetic signals; 2) ridge regressions detect positive trends towards higher complexity; and 3) classical proofs for progressive evolution (the minimum, the ancestor-descendent and the sub-clade tests), show that some of these positive trends are driven, being mainly due to natural selection. These findings support the existence of progressive genome evolution in this ancient and diverse group of organisms.

evolutionary biology↗

bioRxiv2017

miRNAgFree: prediction and profiling of novel microRNAs without genome assembly

The prediction of novel miRNA genes generally requires the availability of genome sequences in order to assess important properties such as the characteristic hairpin-shaped secondary structure. However, although the sequencing costs have decreased over the last years, still many important species lack an assembled genome of certain quality. We implemented an algorithm which for the first time exploits characteristic biogenesis features like the 5 homogeneity that can be assessed without genome sequences. We used a phylogenetically broad spectrum of well annotated animal genomes for benchmarking. We found that between 90-100% of the most expressed miRNA candidates (top quartile) corresponded to known miRNA sequences.

bioinformatics↗