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BDG LifeSciences' Research Project Training Program is a one-of-a-kind initiative designed to strengthen your profile and enhance career opportunities, whether for jobs or higher studies, by offering the chance to work on novel research projects using the latest technologies in bioinformatics. Since 2010, this program has addressed the critical need for high-quality publications by combining innovative teaching methods with practical applications. Conducted entirely online, it provides participants with the flexibility to choose session timings while saving on travel, accommodation, and food expenses. With over 88 research projects successfully completed and published at the international level, this program is ideal as a major or thesis project for final-year students or for those looking to advance their profiles.
Applications are now open for ONLY 4 SEATS in our 90th novel research project, titled "Single-cell RNA-seq Analysis of Chemotherapy-Induced Transcriptomic Changes in Pancreatic Ductal Adenocarcinoma (PDAC)" . The focus of this project is to analyze the impact of chemotherapy on the tumor microenvironment in pancreatic cancer using single-cell RNA sequencing data equipping undergraduates, graduates, and higher-degree holders with practical skills, real-world experience, and valuable publication opportunities.
Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive and treatment-resistant cancers, with limited therapeutic options. Chemotherapy is a mainstay of treatment, but its impact on the tumor microenvironment (TME) remains poorly understood. In this workshop, we will explore a research project modeled on a recent single-cell RNA sequencing (scRNA-seq) study that investigates the molecular and cellular alterations in PDAC tumors following chemotherapy.
Participants will work with real single-cell transcriptomic data derived from human PDAC tumors. The goal is to understand how treatment influences cell populations, gene expression, immune interactions, and potential therapeutic targets within the tumor ecosystem.
This project provides hands-on experience with modern computational biology techniques and enables students to carry out a full data analysis workflow, from count matrix preprocessing to biological interpretation.
If you're passionate about cancer research, transcriptomics, and real-world data analysis, this program offers a unique opportunity to learn cutting-edge single-cell techniques and gain practical experience working with actual patient-derived datasets. This program uses a hands-on, project-based approach to single-cell RNA-seq analysis, guiding participants through a complete workflow—from loading count matrices to identifying tumor-specific pathways—using advanced tools like Seurat, SoupX, InferCNV, and CellPhoneDB in R.
This program equips you with in-demand skills in single-cell transcriptomics, bioinformatics analysis, and data interpretation—key areas in academic research, biotech, and pharma—giving you a competitive edge in job applications, graduate studies, or research roles.
With over a decade of experience delivering cutting-edge training worldwide, BDG LifeSciences blends scientific expertise with interactive learning to offer industry-relevant, practical education—trusted by students, researchers, and professionals across the globe.
Literature Review:
Participants will engage in a comprehensive literature review, critically analyzing existing research relevant to bioinformatics. This stage emphasizes the importance of understanding the current state of knowledge, identifying gaps in research, and formulating questions that will guide subsequent stages of the project.
By the end of this research project, participants will be able to:
Requirements: Basic Skill Requirement to be able to work on this project
Objective:
To analyze how chemotherapy alters the tumor microenvironment (TME) in pancreatic cancer using publicly available single-cell RNA-seq data.
Tools Required:
Project Modules
1. Loading Count Matrix and Metadata
2. Quality Control and Filtering
3. Normalization, Scaling, and PCA
4. Clustering and UMAP Visualization
5. Cell Type Annotation
6. CNV Analysis to Identify Malignant Cells
7. Differential Gene Expression Analysis
8. Gene Set Enrichment Analysis (GSEA)
9. Ligand-Receptor Interaction Analysis
10. Reporting and Presentation
The Research Project Training Program by BDG LifeSciences is not just a course but an investment in your future. The fee you pay guarantees unparalleled value, providing you with cutting-edge skills, real-world experience, and the opportunity to contribute to internationally published research. This program is designed to elevate your academic and professional profile, equipping you with expertise in bioinformatics and drug discovery that is highly sought after in today’s competitive job market.
By participating, you gain the chance to learn from seasoned experts, work on innovative projects, and create a strong foundation for careers in bioinformatics, pharmaceutical research, or higher education. The program’s comprehensive structure ensures you receive everything you need to succeed—training, practical application, resources, certifications, and networking opportunities.
Whether you aim to pursue advanced studies, secure a high-impact job, or become a leader in your field, this program opens doors to new possibilities and heights of success. With BDG LifeSciences, you are not just learning—you are building a future where your contributions to science and technology can make a real difference.
To secure your spot:
- Gain insight into our past workshops:
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TEAM FOR CURRENT/ONGOING RESEARCH PROJECTS
88. Computational Insights into Selective ERβ Agonists for Benign Prostatic Hyperplasia (BPH) Treatment | A Virtual Screening, Molecular Docking & Simulations Study.
87. Decoding Pan-Cancer Pathogenesis: A Multi-Layered Analysis of Prognostic mRNAs, miRNAs, lncRNAs via Co-Expression Networks and PPINs
86. Unveiling Autoimmune Genes and Regulatory Elements in Head and Neck Squamous Cell Carcinoma through Advanced Machine Learning and Network-Based Analysis
85. Molecular Modeling study of derivatives of Leaf Extracts of medicinal plant Solanum torvum and Serine/Threonine Kinase from Mycobacterium Tuberculosis
84. Targeting Tumor Progression: Identifying Differentially Expressed Genes and Pathways in Pancreatic Ductal Adenocarcinoma using RNAseq
83. Next Generation Sequencing | Unraveling the Cancer Code: Gene Expression Profiling with RNAseq
82. NGS Data Analysis | Prediction of Multiple Myeloma Using RNASeq Data
81. NGS Data Analysis of Cancer Tissues | A Cancer Biology Study
79. Molecular Modeling study of Cyclophilin A and derivatives of Ganoderiol F (26,27-Dihydroxylanosta-7,9(11),24-trien-3-one): Design of novel inhibitors for CyclophilinA
78. Molecular Modeling study of derivates of Ethyl 2-[(4-chlorophenyl)carbamoylamino]-5-methyl-4-phenylthiophene-3-carboxylate and α-D-glucose-1-phosphate thymidylyltransferase (Mycobacterium-RmlA) | Discovery of new drugs for multidrug-resistant (MDR)Mtb
76. Molecular Modelling study of p53-MDM2 and derivatives of Ganoderiol F | Discovery of new Anti-CANCER Drugs by Molecular Docking & MD Simulations Approach
75. NGS Data Analysis on Alzheimer's
74. NGS Data Analysis on Cancer Biology | Analyzing cancer tissues
73. Inhibitory study of Focal Adhesion Kinase (FAK): A Virtual screening, Molecular Docking & ADMET study for combating cancer
72. Virtual Screening and Molecular Docking study of derivatives of chromen-2-one as selective Estrogen Receptor beta Agonists (SERBAs): Molecular Modeling study of Benign Prostatic Hyperplasia
71. Molecular Modeling Study of extracts of medicinal plants as potential anti-tubercular agents
70. Virtual screening & Molecular Docking of DOT1L & derivatives of Pinometostat | Molecular Modeling study of Therapeutic Target in Mixed-lineage Leukemia (MLL)
69. Targeting the Wnt/β-catenin signaling pathway in cancer by molecular modeling study of Ganoderiol F and Beta- Catenin
68. Biomarker discovery based on omics technology
67. Study of SARS-CoV-2 main protease (Mpro) and derivatives of Norterihanin to investigate potential inhibitors using Virtual Screening & Molecular Docking
66. Molecular Modelling study of SARS-CoV-2 spike protein of COVID-19 with derivatives of Saikosaponins | Examining the anticoronaviral activity of saikosaponins (A, B2, C and D)
65. Molecular Modeling study of Southeast Asian Medicinal Plant Aglaia erythrosperma and α-D-glucose-1-phosphate thymidylyltransferase (Mycobacterium-RmlA) | Discovery of new drugs for multidrug-resistant (MDR) Mtb
64.Molecular Modeling study of Cyclophilin A and derivatives of 1,8-Diamino-2,4,5,7-tetrachloroanthraquinone: Design of novel inhibitors for Cyclophilin A
63. Molecular Modelling study of Catalytic domain of protein kinase PknB from Mycobacterium tuberculosis | Discovery of new Anti-Tubercular Drugs
62. Molecular Modelling study of p53-MDM2 | Discovery of new Anti-CANCER Drugs by Molecular Docking & MD Simulations Approach
61. Molecular modeling of sphingosine 1-phosphate receptor 1(S1P1) as target for multiple sclerosis | A Virtual screening, Molecular docking & ADMET study
60. Inhibitory study of α-D-glucose-1-phosphate thymidylyltransferase (Mycobacterium-RmlA) | Discovery of new drugs for multidrug-resistant (MDR) Mtb
59. Molecular modeling study of derivatives of dutasteride and Human Steroid 5β-Reductase (AKR1D1) | Discovery of new drugs for prostate cancer
57. Molecular modeling study of α-glucosidase Inhibitors (AGIs) | Discovery of new anti-diabetic drugs by controlling postprandial hyperglycemia
56. Discovery of new ligands for PPAR Gamma responsible for Diabetes Type 2: A Virtual Screening, Docking & ADMET Study.
53. Molecular Modelling study of phytoconstituents from medicinal plants of India | Discovery of natural anti-tubercular agents
49. Molecular Modeling study of Zika Virus | Virtual Screening, Protein Modeling, Docking, ADMET and MD Simulations Study
39. Study of derivatives of Chalcones as new Tyrosinase inhibitors: A Molecular Docking, ADME & Tox Study
34. Study of extracts of Veratrum Dahuricum as potential Anti-tumor molecules: Molecular Docking & Modeling study with Farnesyl Pyrophosphate Synthase (FFPS)