Currently, my research focuses on understanding the role of Epithelial-to-Mesenchymal Transition (EMT) in
cancer progression. EMT induces cellular plasticity in epithelial-origin cells, enabling them to change
their phenotype. During my PhD, we identified two distinct cancer cell evolutionary trajectories during
EMT-dependent cancer progression: one activating an inflammatory, anti-tumoral response that drives
inflammation, and the other promoting an invasive, pro-tumoral program that facilitates metastatic
dissemination (Youssef et al., Nature Cancer, 2024). Building on this framework, this project aims to
investigate how different stromal cells interact with each type of tumor cell in an EMT-dependent manner and
how these interactions influence cancer progression. To achieve this, we will systematically analyze
single-cell and spatial transcriptomics data using state-of-the-art computational methods.
Additionally, I am interested in understanding the role of alternative splicing and isoform diversity in
EMT-dependent cancer progression using long-read sequencing data analysis. This line of research will help
determine whether isoform diversity is functionally relevant, linked to specific disease states, or plays
distinct roles in EMT-driven cancer progression.
I am a highly motivated and independent researcher with a strong background in bioinformatics. As a collaborative team player, I excel at troubleshooting and problem-solving. With extensive experience in data analysis, I am passionate about leveraging bioinformatics to address complex biological questions, particularly those with direct implications for human health.
Exp 9 years
Exp 6-8 years
Exp 6-8 years
I have strong programming skills in R, Python, PERL, Shell Scripting, Java, JavaScript, HTML, CSS, and MySQL with extensive experience in developing automated data analysis pipelines. Additionally, I am proficient in web application development, creating interactive platforms for bioinformatics analysis and data visualization. I am proficient in working within the Conda framework and version control using Git, allowing me to develop efficient and reproducible computational workflows. I am comfortable working with Linux and Windows operating system.
PROJECT: Throughout my Ph.D. tenure, I focused on scRNA-Seq and scATAC-Seq data analysis to understand the complex biological process called Epithelial to Mesenchymal Transition (EMT) in the context of neural crest development, kidney fibrosis, and breast cancer progression. This involved analysing publicly available single cell and bulk RNA-Seq data as well as data generated in the lab, implementing complex data analysis workflows and ML algorithms. In particular in this project, we have implemented deep learning to trace the origin of cells during disease progression. Our work has been successfully published in Nature Cancer Journal (Youssef et. al., Nat Cancer. 2024). The outcome of this project has provided a solid foundation for extending my PhD work in a new framework where I am leading the project as a Postdoc in the same lab to understand the interaction between tumour and immune cells within the microenvironment, and its impact on EMT dependent cancer progression using image-based spatial transcriptomics approach. Simultaneously, I am developing another projects where I am analysing long-read sequencing data analysis to understand the isoform diversity and it's functional relevance linking specific disease states in EMT-driven cancer progression.
PROJECT: During my master's dissertation, I worked on genome-wide identification of Scaffold/Matrix Attachment Regions (S/MARs)-DNA elements that play a crucial role in chromatin organization, gene regulation, and disease biology. Despite their significance, a comprehensive map of human S/MARs was previously unavailable. To address this, I analyzed ChIP-Seq data of 14 S/MAR-binding proteins, identifying a non-redundant set of human S/MARs and characterizing their genomic features, including chromosomal distribution, motif abundance, and their role in retroviral integration. This study provided novel insights into genome architecture and its implications for gene regulation and antiviral therapeutics. To make this data accessible, I developed MARome-a web-based resource for browsing and retrieving human S/MAR information. This work has been successfully published in a peer-reviewed journal (Narwade, et al., Nucleic Acids Res. 2019).
MARome is a web-based resource for browsing human Scaffold/Matrix Attachment Regions (S/MARs) and associated information. This database provide not only simple keyword or gene symbol based search option but also cordinate search and sequence search. The retrieved data is visualized in a dnymic tabular format and cross linked with the external databases.
LinkMicFunPred is a tool designed based on conserved/core gene approach to predict functional profiles from
16S rRNA gene sequence data. MicFunPred identifies genus of unknown 16S rRNA gene sequence based on
nearest neighbor search using a custom database and predicts a set of core genes using ~32,000 reference
genomes.
ProBioPred is a server designed using advanced machine learning models for the prediction of a
potential probiotic candidate. The Support Vector Machine (SVM) models was built based on the presence
of genes imparting Probiotic Properties, Virulence Factors, and Antibiotic Resistance Genes from the
curated genome sequence.
