Sruthi Chappidi

Professional Preparation

Research Areas

Research Interests

Undergraduate Research Faculty Mentor — Bioinformatics August 2024 – Present

• Designed and led sessions providing a foundational biological background to help students understand the scientific questions and interpret biological datasets accurately.
• Guided students in identifying meaningful research questions, conducting structured literature reviews on recent advances in bioinformatics and ML-based diagnostics, and effectively presenting scientific findings.
• Supported skill development in programming (Python/R), critical thinking, and scientific communication.
• Introduced students to bioinformatics datasets, preprocessing techniques, and evaluation metrics relevant to ML applications in genomics and oncology.
• Facilitated the progressive review of three research papers, each narrowing from general ML diagnostic frameworks to models focused on specific cancer subtypes.

Computational Biologist II (former Senior Research Associate) – Dr. Dustin Hancks Lab, Department of Immunology June 2022 – 2024

•  The yeast mitochondrial stress (MISTR) response factor MIN8 shapes the stress response: 
  • Developing RNAseq pipeline to analyze MIN8 in yeast, which is homolog of NDUFA4 in human, role in metabolic adaptation via diauxic shift. 
  • The aim of the project identify functional aspects of uncharacterized MIN8 and its roles under stress especially cell death. 
  • Bioinformatics Methods: Alignment and analysis software such as STAR, SAMtools, IGV, DESeq2, Gene set enrichment analysis (GSEA). Programming Languages: Python and R 
•   Metabolic rewiring exposes a novel antiviral response that restricts diverse DNA viruses: 
  •  RNAseq analysis to understand if glucose promotes anti-VACV (vaccinia virus) response induced by IFN-γ.
  •  Novel approach to identify gene regulated by IFN-γ and characterize their function. 
  • Bioinformatics Methods: Differential expression analysis using DESeq2, and downstream Gene set enrichment analysis (GSEA). Programming Languages: Python 
  • Integration of data from public databases such as GTeX. 
•  Viral genome topology during vaccinia virus infection. 
  •  To determine the topology of genomes for large DNA viruses (~200kb DNA) assaying DNA accessibility and looping interactions during infection, which have not been applied to viruses, using state of the art methodologies. 
  • To develop novel computational framework to integrate ATACseq, RNAseq, ChIP-seq for viral genomes to define viral genome architecture and its role in gene regulation and immune recognition. 
  • Optimizing ATACseq pipeline approaches for analyzing samples with combined host genome and viral genome. 
  • Bioinformatics Methods: Alignment and analysis software such as STAR/Bowtie2, SAMtools, IGV, DESeq2. Programming Languages : Python and R 
•  Viral Genomic Analysis: Genomic data analysis to uncover new virus-host interfaces 
  •  Analysis of available big data from both viral and host (human) genomes to discover novel cellular functions. 
  •  v-mitoORFs: curation and functional analysis of viral genes without host homolog localized to mitochondria. 
  • Development of a database and user-interface to serve as a community resource. The web server contains various filters to prioritize viral genes using available gene expression information for the host homologs including interferon-regulation, cellular localization, conservation, and disease-associations. 
  • Bioinformatics Methods : BLAST, MitoFates and DeepLoc localization methods. Programming Languages: Python and SQL 
•  Chromatin accessibility in MHV68 genome in response to infection and reactivation: 
  • ATACseq and RNAseq analysis to understand IL4 treatment stimuli may induce chromatin accessibility changes in the host cell, resulting in the enhanced replication of MHV68. 

Post Doctoral Researcher – Dr. Dustin Hancks Lab, Department of Immunology November 2019 – May 2022

• Viral Genomic Analysis: Genomic data analysis to uncover new virus-host interfaces 
  •  Analysis of available big data from both viral and host (human) genomes to discover novel cellular functions. 
  • Development of a database and user-interface to serve as a community resource. The web server contains various filters to prioritize viral genes using available gene expression information for the host homologs including interferon-regulation, cellular localization, conservation, and disease-associations. 
•  Viral Genomic Analysis and Cancer 
  •  Explore and analyze the filtered gene list from viral genomic analysis to identify the viral genes that may play a crucial role in cancer development.
• Viral genome topology during vaccinia virus infection
  •  To determine the topology of genomes for large DNA viruses (~200kb DNA) assaying DNA accessibility and looping interactions during infection, which have not been applied to viruses, using state of the art methodologies. 
  • Bioinformatics Methods : BLAST, Alignment with STAR,  Gene expression analysis with Deseq2. Programming Languages: Python and R 
  • Integration of data from public databases such NCBI Virus, MitoCarta, TCGA, GEO. 

Student Research Intern – Dr. Dustin Hancks Lab, Department of Immunology June 2018 – Dec 2018

• Viral Genomic Analysis: 
  •  Performed data acquisition and global analysis of viral genome sequence database for the discovery of mimics 
  •  Designed an approach for development of database and user-interface to serve as a community resource. 
    
    
    

Collaborator – Dr. Brandi Cantarel, Department of Bioinformatics January 2018 - 2021

• NoRef: Reference Free Sequence Analysis 
  • Developed a reference-free sequence analysis comparison tool to perform comparison of samples from NGS sequencing without prior knowledge or bias of reference genome using  k-mers, k-length substrings of sample reads. 
  •  Integrated additional features as a part of NoRef to provide exact distance measure for pairwise comparisons at a computationally comparable or faster rate than existing programs. 
    
    

Computational Biology and Bioinformatics Lab – Dr. Sergey Bereg August 2015 – December 2019

• Genomic Copy Number Variation (CNV) analysis using Hidden Markov Model to assist with cancer research 
  •  Modified Viterbi Algorithm to predict the CNV path for a large amount of genomic data. 
  • Worked on modifying a version of Baum-Welch Algorithm to predict the parameters of HMM to support the CNV model. 
•  DCJVis: Developed tool for visualization of Double Cut Join (DCJ) genome rearrangement operation with adjacency and genome graph to clearly distinguish the operations needed to transform source genome into the target genome
•  DCJ- Duplications Model: Worked on developing algorithms to provide an improved model of DCJ operation for genomes with duplicated genes. 

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