FELIPE VAZ PERES

I am a data scientist with a M.Sc. in bioinformatics and over seven years of experience in computational biology and large-scale data analysis.

I am particularly interested in computational approaches to cancer research, omics, non-coding RNA, and developing efficient & reproducible workflows.

RESEARCH

sugarcane pan-omics

This section highlights my two contributions to sugarcane pan-omics research, conducted at the Computational, Evolutionary, and Systems Biology Laboratory. In our first study, we built the sugarcane pan-transcriptome and revealed the variability in protein-coding transcripts across 50 genotypes. However, a substantial portion of the transcriptome, the non-coding RNAs, remained unexplored.

Despite ncRNAs representing a significant fraction of eukaryotic transcriptomes, their abundance, diversity, and functional relevance in surgarcane are still poorly characterized.

During my master’s, I constructed a multi-genotype ncRNA catalog and analyzed their expression and co-expression networks. Together, these studies integrate coding and non-coding RNAs, expanding our understanding of transcriptomic diversity and highlighting potential functional roles of ncRNA across diverse genetic backgrounds.

"Look again at that dot. That's here. That's home. That's us." - Carl Sagan

The Pale Blue Dot, captured by Voyager 1 in 1990, showing Earth as a tiny speck in the vastness of space, a humbling reminder of our place in the cosmos.

sugarcane pan-RNAome

Characterization of sugarcane ncRNAs and lncRNAs, revealing variability, conservation, co-expression, and functional roles.

Thesis Code

sugarcane pan-transcriptome

Framework for pan-transcriptome assembly in complex polyploid crops, supporting sugarcane breeding programs.

ISMB Talk Code

computational reproducibility

Ensuring reproducibility remains one of the major challenges in computational biology and data science. Despite the growing availability of large-scale datasets, many published results are difficult to replicate due to incomplete documentation, non-standardized workflows, missing metadata, or dependence on specific computing environment. Whether in the context of gene expression studies or predictive modeling, transparent and reproducible practices are essential for building confidence in scientific results.

In my work I focus on developing robust, efficient, and reproducible workflows that adhere to the open science and FAIR principles (Findable, Accessible, Interoperable, and Reusable).

This section highlights my contributions to reproducible computational research, showcasing the tools, pipelines, and projects I have been involved in, each developed to enhance reproducibility and transparency in large-scale biological data analysis.

KAPT

Automated inference and annotation of the Kappaphycus alvarezii supertranscriptome.

Code

T-M integration

Transcriptome–microbiome cross-correlation and host–microbial interaction inference.

Code

R2C

Automated gene co-expression network construction and regulatory analysis.

Code

seabed symphony

Pipeline for novel Biosynthetic Gene Cluster discovery in marine sediment microbiomes.

Code

YAATAP

Snakemake pipeline for de novo transcriptome assembly and functional annotation.

Code

software development

My path into software development began in the biological sciences. Working with the massive datasets produced by modern sequencing technologies, I quickly realized that understanding biology in the twenty-first century requires far more than laboratory skills, it demands the ability to design, automate, and scale analyses across terabytes of information. What began as a necessity soon became a passion.

I am deeply committed to the principles of Free and Open Source Software (FOSS) and enjoy contributing to and learning from open-source communities. Several of my projects and tools are freely available online, reflecting my belief that scientific progress is best achieved through transparency and collaboration.

This section highlights a selection of projects I’ve built, including tools and applications developed beyond the scope of computational biology.