Claudio Casola

Genomes2Genes converts Progressive Cactus alignments into validated gene-level alignments, identifying de novo genes and enabling evolutionary analysis.

The paper identifies 22,377 de novo genes in 21 Brassicaceae genomes, showing variation in birth/death rates and factors influencing their turnover.

The paper expands and refines a reference catalog of human non-canonical open reading frames (ncORFs) by incorporating more datasets and introducing a translation signature score framework to prioritize high-quality ncORFs.

The paper presents a chromosome-level genome assembly of the southern pine beetle and identifies gene content reduction and annotation biases in the Dendroctonus clade.

The paper analyzes degradation determinants in canonical and noncanonical human proteins, finding noncanonical proteins are enriched for degradation features and that C-terminal hydrophobicity correlates with degradation propensity.

This study uses machine learning to identify de novo genes in plant genomes, showing high accuracy within species and reduced false positives compared to traditional methods.

SECNVs simulates copy number variants and whole-exome sequences from reference genomes to improve comparison of detection methods.

The paper re-analyzes de novo gene findings using phylostratigraphy and finds most putative de novo genes are either old genes or recent duplicates, with a low rate of true de novo genes.

SimulateCNVs is a software tool that simulates copy number variations in whole exome and whole genome sequencing data, producing rearranged genomes, short reads, and BAM files.

The paper analyzes gene duplication and loss rates in Pinaceae genomes, finding higher turnover rates compared to angiosperms despite incomplete gene annotations.