I am a Bioinformaticist and Data Analysts. Working with R on projects related to transcriptomic analysis, RNA-seq analysis, NGS etc.
I am extremely passionate about solving problems in the following domains:
RNA-seq workflow, Next Generation Sequencing, Transcriptomic analysis, Meta-Analysis, Survival Analysis, Comparative Bioinformatics Analysis, DNA methylation data, Computer Vision, Data Structure and algorithms, Statistical Analysis, Data Science.
Data analysis, Data Visualization, Data mining, Data Scraping, Forecasting using R, PYTHON.
Machine learning- KNN, PCA, LDA, Random forest., Clustering Data
Research article writing for journals.
Center for Research in Basic Sciences, Jamia Milia Islamia
jul. 2018 - mar. 2020 (1 ano, 8 meses)
- Worked on several projects.
- Published two research papers
- identification of differentially expressed genes based on meta-analysis
- TCGA workflow
- Survival Analysis
- Signature Genes
Center for Research in Basic Sciences, Jamia Millia Islamia
jan. 2018 - jul. 2018 (6 meses, 1 dia)
- worked on gene expression data
- Identified Differentially Expressed Genes
- Protein-Protein Network Analysis
- Functional Enrichment Analysis
Master of Science in Bioinformatics
Jamia Millia Islamia, India 2016 - 2018
B.Sc. Honors in Botany
Delhi University, India 2011 - 2014
Post Graduate in Bioinformatics
Jamia Millia Islamia
- R programming ( between intermediate to advanced level)
- Python(intermediate level)
- Data visualization
- Data manipulation
- Data analysis
- Bioinformatics Analyses
- Transcriptomic analysis
- Gene Set Enrichment Analysis
- Neighbor-Joining algorithm
- Writing Research Articles
Transcriptome Meta-Analysis Deciphers a Dysregulation in Immune Response-Associated Gene Signatures
Genes, Multidisciplinary Digital Publishing Institute
This study aimed to identify potential genes for diagnostics and therapeutic purposes in sepsis by a comprehensive bioinformatics analysis. Our criteria are to unravel sepsis-associated signature genes from gene expression datasets. Differentially expressed genes (DEGs) were identified from samples of sepsis patients using a meta-analysis and then further subjected to functional enrichment and protein‒protein interaction (PPI) network analysis for examining their potential functions.
Identification of Differentially expressed genes in small and non-small cell lung cancer
This study deals with the analysis of gene expression of two subtypes to identify the Differentially Expressed Genes (DEGs). For this study, we selected two datasets from the Omnibus database, which included 50 non-small cell lung cancer samples, 31 small cell lung cancer samples, and 48 samples from normal lung tissue. After DEGs identification using the meta-analysis approach, they were then subjected to further analysis.