Hi I am Abu Saadat 
,
Bioinformatician specializing in multi-omics data analysis, including methylation arrays, RNA-seq, WES/WGS, and omics
data integration. With four years of experience, I use advanced statistical methods to uncover novel insights from complex datasets. Known for my problem-solving skills, adaptability, and rapid learning, I actively contribute to open-source projects on Git and have served as a Nextflow Ambassador, advocating for reproducible workflows. My ultimate goal is
to drive meaningful contributions to translational research.
R
90%
Python
50%
C++
30%
HTML
30%
Bio-Statistic Analysis
90%
Methylome/Transcriptome/WES-WGS analysis
90%
Multi-Omics Data Integration
80%
I hold a Bachelor of Science degree in Life Sciences from University of Lucknow, where I delved into the realms of Zoology, Botany, and Chemistry. This foundational education provided me with a comprehensive understanding of the natural world, sparking my curiosity for the complex interplay of biology and chemistry.
I earned a Master’s degree in Biotechnology from the Pondicherry University, where I honed my expertise in molecular biology, genetic engineering, and cutting-edge biotechnological techniques. As part of my Master’s dissertation, I explored the molecular dynamics of TRPV and opioid receptors, investigating their binding interactions with various natural and synthetic compounds through computational methods such as molecular docking and simulations. This hands-on research experience fueled my passion for computational biology and laid the groundwork for my current focus on bioinformatics.
I earned my Ph.D. at the University of Campania, Italy, where my research focused on characterizing the molecular heterogeneity of Wilms tumors, a pediatric kidney cancer, using a multi-omics approach. By integrating methylome, RNA-Seq, and whole exome sequencing (WES) data, I aimed to unravel the complex biological underpinnings of this disease. This work not only enhanced our understanding of tumor biology but also highlighted the power of computational methods in advancing precision oncology.