Anna Paola Muntoni

DCAlign v1.0 improves sequence alignment by incorporating co-evolution models and informative priors to reduce computational demands.

The paper introduces DCAlign, a method for sequence alignment that incorporates coevolution and second-order interactions, improving upon profile models for proteins and RNA.

This paper introduces an unbiased method for metabolic flux inference by integrating thermodynamic constraints and 13C labeling data, revealing novel flux patterns in budding yeast.

adabmDCA 2.0 provides a flexible and multi-language implementation of Direct Coupling Analysis for protein and RNA sequence data, enabling tasks like contact prediction and sequence design.