Pranava Upparlapalli

I design scalable pipelines and analytical frameworks that transform complex genomic and transcriptomic data into actionable insights. Skilled in Python, R, Nextflow, Docker, and AWS, I build reproducible solutions that accelerate discovery and translational research across RNA-Seq, GWAS/TWAS, and variant analysis.

"Turning data into impact across genomics and computational biology."

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Areas of Expertise

Next-Generation Sequencing (NGS) Data Processing

Develop end-to-end pipelines for RNA-Seq, DNA-Seq, and targeted sequencing to transform raw reads into high-confidence, interpretable biological data.

Genomic Data Analysis and Interpretation

Analyze large-scale genomic datasets to interpret gene regulation, assess variant effects, and uncover clinically meaningful trait associations.

Bioinformatics and Statistical Data Analysis

Apply rigorous statistical methods and bioinformatics tools to generate accurate, reproducible insights from complex omics datasets.

Machine Learning Applications in Computational Biology

Design and deploy ML models for classification, prediction, and dimensionality reduction, adapted to the intricacies of biological data.

GWAS and TWAS Pipeline Development

Build integrative workflows combining genotype, expression, and chromatin interaction data for trait-gene mapping.

Workflow Automation and Scalable Pipeline Design

Develop modular, version-controlled pipelines using Snakemake, Nextflow, and shell scripting to automate and scale bioinformatics workflows.

Work Experience

Graduate Researcher – TWAS & Genomic Modeling

Dr. Xuan’s Lab, University of Texas at Dallas (Jan 2024 – Present)

Tested a GWAS-based machine learning algorithm based on PrediXcan using genomic data to accurately predict gene expression in different tissues.
Analyzed the role of trans SNPs using HI-C data, to decipher the relationship between genotype and phenotype using SNP variants.
Created pipelines and provided efficient data analysis using Bash scripting and HPC computing.

Undergraduate Researcher – Biochemical Assay Development

Sree Vidyanikethan Degree College, Tirupati (Aug 2020 – Mar 2021)

Researched and characterized the antioxidant properties of Biancaea sappan, employing advanced biochemical assays for compound analysis.
Designed and executed lab experiments that increased compound yield efficiency by 15%.
Optimized growth conditions by formulating specialized media and conducting antibacterial assays.

Projects

SeqMorph: Sequence Mutation Simulator

Engineered a versatile bioinformatics tool in Python to simulate diverse mutations in DNA, RNA, and protein sequences.

Python · Biopython · Pandas

Cancer RNA-Seq Expression Analysis

Built a complete R-based pipeline to perform QC, normalization, and differential expression analysis using DESeq2 across five major cancer types.

R · DESeq2 · ggplot2 · pheatmap

Nociception Study using Secondary Metabolites

Studied nociceptive effects of gut microbiota–derived secondary metabolites, linking microbial activity to pain signaling pathways.

AntiSMASH · PCR · Microbial techniques

Yeast Stress RNA-Seq Pipeline

Designed a reproducible RNA-Seq analysis pipeline using Nextflow and Docker, incorporating Fastp, HISAT2, and SAMtools.

Nextflow · DESeq2 · Docker · MultiQC

Gleason Score Classification (DL)

Designed a deep learning pipeline using ResNet-50 for prostate cancer classification, achieving 90% classification accuracy.

TensorFlow · Keras · Python

DDSEQ2 RNA-Seq Analysis

Differential gene expression pipeline in R using DESeq2 with custom visualization outputs.

R · DESeq2 · ggplot2

Skills & Proficiencies

Skilled in RNA-Seq data analysis, Python, R, and reproducible workflows using Nextflow, Docker, and AWS. Experienced in GWAS, TWAS, Hi-C integration, and applying machine learning (PyTorch, TensorFlow) to genomic datasets with a focus on scalability and high-performance computing.