Biology subjects

Marco, A.

Publications and source records attributed to Marco, A..

3 recordsLinked to original sources

bioRxiv2018

Pervasive selection against microRNA target sites in human populations

MicroRNA target sites are often conserved during evolution and purifying selection to maintain such sites is expected. On the other hand, comparative analyses identified a paucity of microRNA target sites in co-expressed transcripts, and novel target sites can potentially be deleterious. We proposed that selection against novel target sites pervasive. The analysis of derived allele frequencies revealed that, when the derived allele is a target site, the proportion of non-target sites is higher than expected, particularly for highly expressed microRNAs. Thus, new alleles generating novel microRNA target sites can be deleterious and selected against. When we analysed ancestral target sites the derived (non-target) allele frequency does not show statistical support for microRNA target allele conservation. We investigated the joint effects of microRNA conservation and expression and found that selection against microRNA target sites depends mostly on the expression level of the microRNA. We identified microRNA target sites with relatively high levels of population differentiation. However, when we analyse separately target sites in which the target allele is ancestral to the population, the proportion of SNPs with high Fst significantly increases. These findings support population differentiation is more likely in target sites that are lost than in the gain of new target sites. Our results indicate that selection against novel microRNA target sites is prevalent and, although individual sites may have a weak selective pressure, the overall effect across untranslated regions is not negligible and should be accounted when studying the evolution of genomic sequences.

evolutionary biology↗

bioRxiv2018

No evidence of functional co-adaptation between clustered microRNAs

A significant fraction of microRNA loci are organized in genomic clusters. The origin and evolutionary dynamics of these clusters have been extensively studied, although different authors have come to different conclusions. In a recent paper, it has been suggested that microRNAs in the same clusters evolve to target overlapping sets of genes. The authors interpret this as functional co- adaptation between clustered microRNAs. Here I reanalyze their results and I show that the observed overlap is mostly due to two factors: similarity between two seed sequences of a pair of clustered microRNAs, and the expected high number of common targets between pairs of microRNAs that have a large number of targets each. After correcting for these factors, I observed that clustered microRNAs from different microRNA families do not share more targets than expected by chance. During an exchange of correspondence and manuscripts, the authors of the original report acknowledged that the permutation methods they performed was not the method they described in their original paper. Here I show that the new permutation test proposed is biased and leads to systematic errors of the first kind, which will explain why the p-values reported were extremely (and unrealistically) low. I also discuss how to investigate the evolutionary dynamics of clustered microRNAs and their targets. In conclusion, there is no evidence of widespread functional co-adaptation between clustered microRNAs.

evolutionary biology↗

bioRxiv2017

SeedVicious: analysis of microRNA target and near-target sites

Here I describe seedVicious, a versatile microRNA target site prediction software that can be easily fitted into annotation pipelines and run over custom datasets. SeedVicious finds microRNA canonical sites plus other, less efficient, target sites. The program also detects near-target sites, which have one nucleotide different from a canonical site. Near-target sites are important to study population variation in microRNA regulation. Here I show that near-target sites can also be functional sites. Among other features, seedVicious can also compute evolutionary gains/losses of target sites using maximum parsimony. SeedVicious does not aim to outperform but to complement existing microRNA prediction tools. For instance, the precision of TargetScan is doubled (from 11% to ~22%) when we filter predictions by the distance between target sites using our program. The software is written in Perl and runs on 64-bit Unix computers (Linux and MacOS X). Users can also try the program in a dedicated web-server by uploading custom data, or browsing pre-computed predictions. SeedVicious and its associated web-server and database (SeedBank) are distributed under the GPL/GNU license.

bioinformatics↗