Research

My research focuses on understanding how information is organized, distributed, and compared in biological systems. Our work encompasses two key levels of investigation:

Computational Analysis:

Developing computational algorithms to address the complexities of genome data

We are currently studying different ways to correct Transcriptional Profiling estimation errors due to the presence of repetitive sequences and identifying genomic differences between normal and cancer cells.

Exploring the digitization of the information present in DNA, RNA, and proteins, and how this can be used at detecting reciprocal mutations and sequence differences between related genomes.

We are also developing novel computational pipelines dedicated to detecting sequence variations within related genomes. We are particularly intrigued by the prospect of simplifying (i.e., digitizing) the information present in DNA, RNA, and Proteins so as to simplify its manipulation and analysis. We think that digitizing emerging genomic data will not only enable us to use this data effectively but also to integrate it into Artificial Intelligence, Data Clustering, and Image Recognition Algorithms, in ways not done before. We posit that this process of converting biological features into digital equivalents has the potential to simplify genomic information, making it easier to uncover previously unnoticed patterns through complex computational comparisons. This approach has already yielded promising results by revealing unexpected informational patterns across various organisms' chromosomes. We believe that it will streamline and enhance our ability to comprehend different cellular and organismal states. Moreover, it holds significant promise in revolutionizing our understanding of diseases, particularly Cancer and Metagenomics. This informational perspective also contributes to our comprehension of genome evolution, especially in the field of comparative genomics and microbial metagenomics.

Molecular Genetics:

Using Neurospora crassa as a model organism, we are identifying molecular players involved in sequence-based comparison between homologous chromosomes.

We want to understand the intricate molecular components responsible for sequence-based comparisons between homologous chromosomes, leading to the initiation of Meiotic Silencing, a phenomenon driven by RNA-mediated processes. Currently, our primary focus centers on the exploration of whether genes recognized for their significance in Meiotic Transvection/Silencing also contribute to the occurrence of Repeat Induced Point Mutation (RIP) phenomena.