Rakesh Kumar Yadav – Unleashing the Secrets of Genomics: Meet the Mastermind Behind Breakthrough Analyses!

RAKESH KUMAR YADAV

Bengaluru

Summary:

Expertise in RNA/DNA genomic profiling, single cell biology, transcriptomic, proteomics. Excellent communication and project management skills and service as core member on genomics development teams involving direct management and scientists.

Experience:

Jr. Scientist
Shriram Institute for Industrial Research, Bengaluru
2021 – Present

• Analyzed large genomic datasets using Python and R to identify potential biomarkers.
• Collaborated with cross-functional teams to integrate bioinformatics analyses into drug discovery projects. Automated data preprocessing and analysis workflows using Python.
• Developed pipelines for high-throughput genomics analyses: single-cell RNAseq, bulk RNAseq.
• Conducted statistical analyses and data visualization using Python to support clinical trial data interpretation.
• Maintained and optimized Linux-based server environments for bioinformatics application.
• Processed and analyzed next-generation sequencing (NGS) data using bioinformatics tools and custom Python scripts.
• Provided bioinformatics support for various research projects, including cancer genomics and RNA Seq Analysis.

Project:

Transcriptomics and pathway analysis of the drought-resistant gene of Camellia sinensis, Institute of Bioinformatics Bangalore, Jan 2020 – July 2020.

Project: Variome analysis of smad4 in multiple cancer. Collecting data from multiple cancer databases like cBioPortal, TCGA, clinVar, Cosmic and identifying mutations based on swift and polyphen score. Ranking the most deleterious variant using CADD score out of 1797 mutations, where only 20 deleterious mutations were found. After that, constructing the homology modeling of those deleterious mutations.

Skills:

• Statistics: Hypothesis testing, regression modeling, high-dimensional data analysis including dimensionality reduction methods.
• NGS assays: RNA-Seq, Single-cell RNAseq, WGS.
• Bioinformatics: RawdataQC, sequence alignment, batch effect/correction/integration, differential gene expression/enrichment detection, pathway analysis.
• Bioinformatics tools/Biological Databases: BWA/bowtie/STAR, Sam, Limma/DESeq2, CellRanger, Seurat, GSEA, GTEx, TCGA, GEO.
• Data visualization: Seaborn and Matplotlib.
• Data Analytics: Python, Pandas, NumPy for data analysis.

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Cover Letter:

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