Rodolfo Aramayo

Rodolfo Aramayo is an Associate Professor at Texas A&M University, affiliated with the Faculty of Genetics and the Master of Biotechnology Program. He joined the university in 1997 and maintains an active research laboratory focused on computational biology and genomics. His work bridges biological information systems, computational algorithm development, and experimental genetics using model organisms.

Dr. Aramayo holds a B.Sc. (1982) and M.Sc. (1986) in Molecular Biology from the University of Brasilia, Brazil, and a Ph.D. in Genetics (1992) from the University of Georgia. He completed postdoctoral training at the University of Wisconsin and Stanford University.

His research program investigates fundamental questions in biological information processing:

• Developing computational algorithms for genome data analysis, particularly addressing challenges in transcriptional profiling and sequence variation detection
• Digitalization of genomic information to enable novel computational comparisons and pattern recognition across organisms
• Regulatory roles of non-coding RNAs in ribosomal function and translational control
• Molecular mechanisms of meiotic silencing and sequence comparison in fungal meiosis using Neurospora crassa

Recent publications show strong emphasis on bioinformatics tool development (2024-2025) for genomic data processing and visualization. His peer-reviewed research spans fungal epigenetics, translational regulation, neurobiology, and microbial genomics. Article keywords predominantly include computational biology, genomics, and molecular genetics methodologies.

Dr. Aramayo has secured substantial research funding including NSF ACCESS allocations (2021-2025), NIH grants (R01 GM123139-01), and institutional awards supporting genomics and materials science initiatives. He directs the Aramayo Lab, which develops open-source bioinformatics tools available through GitHub and Zenodo. Current projects include digital biology approaches to simplify genomic analysis and reveal novel biological patterns.