Prathiksha Ramesh

Overview:
As a Remote Research Trainee at the University of Pennsylvania, I focused on gaining specialized knowledge in RNA sequencing (RNA-Seq) analysis. The training involved hands-on experience with advanced bioinformatics tools and methodologies to analyze large-scale RNA-seq datasets, improving my understanding of gene expression and transcriptomics.

Key Learnings:

• Acquired expertise in RNA sequencing analysis, including bulk RNA-seq and single-cell RNA-seq, utilizing bioinformatics tools such as Kallisto, DESeq2, and Seurat.
• Developed skills in preprocessing RNA-seq data, including quality control using FastQC and MultiQC, as well as transcript quantification and differential gene expression analysis.
• Gained practical experience in using R and Python for RNA-seq data manipulation and visualization, enhancing my ability to interpret complex gene expression data.
• Collaborated with remote mentors and peers, learning to apply cutting-edge methodologies to gene expression studies and integrating results with functional genomics analysis.

Skills, Tools, and Libraries Gained:

• RNA-Seq Tools: Kallisto, DESeq2, Seurat, FastQC, MultiQC
• Programming Languages: Python, R
• RNA-Seq Analysis: Differential Gene Expression, Single-cell RNA-seq, Gene Expression Profiling
• Data Visualization: ggplot2, Tidyverse, Matplotlib
• Other Tools: Jupyter Notebook, R Studio, GitHub
• Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Bioconductor

Project Overview:
As part of a prestigious DAAD scholarship, I developed my own research proposal to tackle the challenge of missing data in gene expression datasets, which was selected for execution at RWTH Aachen University. The research focused on solving the bioinformatics problem of missing data imputation in large-scale gene expression datasets, leveraging machine learning (ML) and deep learning (DL) techniques.

Key Contributions:

• Gathered and organized data for research purposes, designing and developing ML and DL models to handle missing data in gene expression datasets.
• Conducted thorough literature reviews to identify gaps in existing methodologies and inform future research directions.
• Collaborated with interdisciplinary teams, including experts in computational biology, machine learning, and data science, to execute comprehensive studies and generate valuable insights.
• Applied ethical considerations throughout all stages of the research process, ensuring data integrity and safeguarding participant welfare.
• Successfully developed and optimized imputation methods, leading to more reliable and accurate gene expression analyses.
• The project significantly enhanced the reliability of biological data analysis and contributed to more robust gene expression insights.

• Computational Biology: Developed a deep understanding of gene expression datasets, particularly the challenges posed by missing data, and how to leverage computational techniques to address these issues.
• Machine Learning & Deep Learning: Designed and implemented ML and DL models for data imputation, enhancing the accuracy of gene expression analyses.
• Data Imputation Techniques: Acquired proficiency in various data imputation methods, optimizing approaches to handle missing data in large-scale genomic datasets.
• Research & Analysis: Conducted extensive literature reviews to identify methodological gaps and used this information to inform future research directions.
• Interdisciplinary Collaboration: Worked with experts from computational biology, machine learning, and data science, improving teamwork and knowledge integration across domains.
• Problem Solving: Developed and optimized imputation models, solving the critical problem of missing data in gene expression datasets, leading to more accurate and reliable results.
• Technical Skills: Gained experience with bioinformatics tools and libraries (e.g., Python, R, ML/DL frameworks) and advanced data science techniques for analyzing biological datasets.

Overview:
While pursuing my master’s degree, I served as a Teaching Assistant at IIT Gandhinagar, where I supported over 50 students in courses such as Journal Writing, Energy & Matter Laboratory, and World of Engineering. My role involved enhancing students' research and data analysis skills, as well as leading them through practical engineering projects.

Key Contributions:

• Assisted students in technical writing and critical thinking.
• Led students through hands-on engineering projects, contributing to improved academic performance and successful project completions.
• Provided academic support in laboratory sessions, helping students apply complex concepts in practical settings.
• Collaborated with faculty to design and evaluate coursework and assessments, ensuring alignment with course objectives.

Skills Gained:

• Research & Analysis: Helped students refine their research methods and data interpretation.
• Project Management: Led students through project development and execution, managing timelines and outcomes.
• Mentorship: Offered personalized academic guidance and feedback to improve student performance.
• Collaboration: Worked with faculty to ensure the seamless execution of course content and objectives.