Rakesh Kumar Yadav – Unlocking the Secrets of Genomics: The Rising Star in Bioinformatics

**RAKESH KUMAR YADAV**

**Summary:**
Rakesh Kumar Yadav is an expert in RNA/DNA genomic profiling, single-cell biology, transcriptomics, and proteomics. He possesses excellent communication skills and project management abilities, having served as a core member on genomics development teams involving direct management and collaboration with 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 applications.
– 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.

**Projects:**
– Transcriptomics and pathway analysis of the drought-resistant gene of Camellia sinesis.
– Variome analysis of smad4 in multiple cancer.

Institute of Bioinformatics Bangalore Jan 2020-July 2020
– Collected data from multiple cancer databases like cBioPortal, TCGA, ClinVar, Cosmic to identify mutations based on swift and PolyPhen score.
– Conducted ranking of the most deleterious variants using CADD score, followed by homology modeling of these mutations.

**Skills:**
– Statistics: Hypothesis testing, regression modeling, high-dimensional data analysis including dimensionality reduction methods.
– NGS assays: RNA-Seq, Single-cell RNA seq, WGS.
– Bioinformatics: Raw data QC, 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.

Education:
– **Central University of South Bihar** (2018-2020)
[hidden] Bioinformatics

Cover Letter:

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