Sonal Muthyala

About

Sowmya (Sonal) Muthyala is a Bioinformatician with a Master of Science in Bioinformatics from Northeastern University with a strong foundation in computational genomics and functional transcriptomics. Her work centers on analyzing high-throughput sequencing data to uncover regulatory mechanisms governing RNA biology. She has extensive experience building scalable RNA-seq pipelines using STAR, Salmon, and DESeq2 for transcript quantification and differential expression analysis, as well as rMATS for alternative splicing studies. Her workflows have supported the processing of hundreds of samples on high-performance computing (HPC) systems, emphasizing efficiency, reproducibility, and rigorous statistical validation.

A core focus of her research is RNA-binding protein (RBP) motif discovery from eCLIP datasets. She applies and refines the PRIESSTESS framework alongside tools from the MEME Suite to identify sequence and structural motifs that mediate post-transcriptional regulation. By integrating computational modeling with biological interpretation, she aims to improve motif resolution, enhance reproducibility across datasets, and better characterize regulatory elements influencing gene expression. Her work combines algorithmic optimization, machine learning approaches, and careful data curation to extract biologically meaningful insights from complex genomic datasets.

Beyond transcriptomics, Sonal has applied machine learning techniques to predictive modeling in biomedical datasets, strengthening her ability to bridge computational methods with biological applications. She is proficient in Python (Biopython, Pandas, PyTorch), R/Bioconductor, shell scripting, and workflow management tools such as Docker, Git, and SLURM. Her long-term research goals lie at the intersection of RNA biology and machine learning, where she seeks to develop interpretable computational frameworks for understanding splicing regulation, RNA–protein interactions, and disease-associated transcriptomic variation.

Research Interests

RNA-Binding Proteins, Motif Discovery, eCLIP Data Analysis, Alternative Splicing, Transcriptomics, RNA-Seq Analysis, Functional Genomics, Machine Learning in Genomics, Regulatory Sequence Modeling, Computational Biology.

Education

M.S. in Bioinformatics, Northeastern University, 2024
B.Tech in Biotechnology, Sreenidhi Institute of Science and Technology, 2020