Technical Achievements:​
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Optimized data analysis pipelines to handle extensive genetic datasets, achieving reproducible and scalable results across multiple computing environments.
Real-World Impact:
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The findings from this project have paved the way for potential diagnostic tools and therapeutic targets, marking a significant step forward in precision medicine for MD.
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Demonstrated the capability of using whole genome data to inform clinical strategies and research into complex, polygenic diseases.
Relevance to Clients: This project exemplifies the comprehensive bioinformatics solutions provided by Mana Genomics, including:
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Custom development of data analysis pipelines for large-scale genomic studies.
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Expertise in integrating genetic data with biological network analysis to identify disease mechanisms.
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Support for developing targeted diagnostics and therapeutic research based on genomic data.
Project Overview: As part of a groundbreaking study published in BMC Genomics, I led the development of the compute infrastructure and bioinformatics pipeline for analyzing whole genome sequencing data in a cohort of 511 individuals diagnosed with Ménière’s disease (MD). This large-scale study aimed to uncover genetic factors contributing to MD and identify potential therapeutic targets.
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Key Contributions:
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Bioinformatics Infrastructure: Designed and implemented robust computational pipelines tailored for high-throughput whole genome sequencing (WGS). Leveraged tools such as GATK, bcbio, and maftools to ensure efficient variant calling, filtering, and visualization.
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Data Processing: Oversaw the development of workflows for processing 150 bp paired-end reads from the Illumina NovaSeq platform, resulting in 30X average genome coverage.
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Variant Analysis: Applied stringent quality control measures to identify rare damaging variants, revealing 481 high-priority genes associated with hearing loss and balance, key symptoms of MD.
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Project Overview: In a pivotal study published in Cell Reports, I developed the compute infrastructure and comprehensive RNA editing analysis workflow from raw FASTQ data to differential editing results. This work explored the role of inflammation-driven ADAR1-mediated RNA editing in the evolution of pre-leukemia stem cells (pre-LSCs) to leukemia stem cells (LSCs) in myeloproliferative neoplasms (MPNs).
Key Contributions:
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Compute Infrastructure: Established scalable and efficient computational systems to manage high-throughput RNA-seq data.
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RNA Editing Pipeline: Created an automated pipeline for detecting A-to-I editing events, integrating quality control, alignment, and variant calling.
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Differential Analysis: Developed robust methods for comparing RNA editing between samples, uncovering significant editing events linked to leukemia progression.
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Impact on Research and Applications: This work revealed critical insights into how inflammation-induced RNA editing contributes to malignancy, opening pathways for therapeutic exploration. The methods developed can support clients aiming to perform RNA editing studies for disease research and treatment development.
Project Overview: In a study published in Genes & Development, I executed the CNV (copy number variation) calling and developed an innovative algorithm for identifying and quantifying significant aneuploidy. This research focused on understanding how transient chromosome instability (CIN) can drive tumor development through clonal evolution.
Key Contributions:
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CNV Analysis Execution: Conducted comprehensive CNV analysis using whole-genome sequencing data from thymic tumor samples, leading to the identification of significant chromosomal gains and losses associated with aggressive cancer phenotypes.
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Algorithm Development: Created a robust algorithm designed to determine and quantify the most impactful aneuploid events, aiding in the precise mapping of chromosomal abnormalities that contribute to tumorigenesis.​
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Data Interpretation: Analyzed complex genomic data to reveal that specific aneuploidy profiles, including gains of chromosomes 4, 5, 14, and 15, are selected early in tumor development and are linked to increased cancer-initiating potential.
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Impact on Research and Industry:
This work demonstrated the relationship between transient CIN and the evolution of distinct karyotypes that drive tumor formation, providing valuable insights for cancer research. It highlights the importance of early detection of aneuploidy and the development of targeted cancer therapies.
Applications for Clients:
Mana Genomics utilizes similar approaches to help clients:
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Implement precise CNV analysis pipelines for cancer research.
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Develop algorithms for genomic data interpretation and variant analysis.
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Enhance understanding of genetic drivers of disease for improved diagnostics.