A. von Haeseler

The paper develops a Fokker-Planck theory to model human cerebral organoid growth, showing it is a self-organized critical process using lineage tracing data.

ModelRevelator uses deep learning to select phylogenetic models from multiple-sequence-alignments, avoiding computationally expensive likelihood-based methods.

The paper sequences and analyzes the Arabidopsis thaliana NOR2 region, revealing its organization and tissue-specific expression of rRNA variants.

This study shows NMD regulates cell fate transitions by controlling Eif4a2 translation and revealing a hierarchy in Smg5, Smg6, and Smg7 functions.

IQ-TREE 2 introduces new models and efficient methods for phylogenetic inference, improving upon its previous version for genomic data analysis.

The paper studies the structure of Hamming graphs of taboo-free sequences and shows that bacterial taboo-free Hamming graphs are nearly always connected.

This paper uses neural networks to distinguish between long-branch attraction and repulsion in four-taxon alignments, improving tree reconstruction accuracy by selecting optimal methods.

The paper introduces the GHOST model to improve phylogenetic inference from heterotachously-evolved sequences, showing it accurately recovers tree topologies and provides new insights into historical signals in empirical data.

The paper introduces evolutionary traceability to improve the reconstruction of ancient gene sets by distinguishing between true absence and non-detection of orthologs.

UFBoot2 improves the ultrafast bootstrap approximation for phylogenetic analyses by increasing speed and reducing overestimation of branch supports.

The paper identifies adaptive genome evolution signals linked to A-to-I RNA editing in humans and Drosophila by analyzing population genomics signatures.

The paper shows that the invariable site plus discrete Gamma model's parameters are poorly estimated by current software and proposes an improved optimization heuristic.