Our Objectives
- We aim to provide one-stop information repository related to NER specific skin cancers, namely: XP, CS, TTD.
- To develop one of its kind NER Specific Skin Cancer DB covering not only the Literature, Gene Expression, Molecular and Interaction data but also the Clinical Information like - Manifestations and Epidemiology, Drugs and Therapeutics, Clinical Trials etc, along with the in-depth analysis of the collected information.
- A database that will continue to grow, add the advancements and up-gradation in biological, medical and technological terms in the above mentioned skin cancers.
Our Methodology and Resources
For the extensive understanding of the mechanism of NER and its relation with carcinogenesis, we applied a wide variety of in-silico approaches via most cited computational tools, software and databases on different forms of raw data. A myriad of sequential steps were employed for characterization and analysis on the collected data for respective skin cancers. To understand the methodology of characterization, first let’s discuss the diverse categories of data collected by us.
| Data Type | Resources Referred |
|---|---|
| Literature | NCBI-PubMed, MedLine, GeneCards, MedScape, ResearchGate GmbH, ScienceDirect, Elsevier |
| Gene Data | NCBI, HGNC, GeneCards, EMBL European Bioinformatics Institute, UniProtKB |
| Genetic Annotation | Gene Ontology DB, Reactome, AmiGO2 |
| Gene Expression Data | COSMIC, NCBI-SRA, Human Protein Atlas, Array Express |
| NER Pathway Information | KEGG, Reactome |
| NER Pathway Reconstruction | Tool - CellDesigner |
| Interactions | STRING database, Cytoscape tool |
| Protein Family Classification | InterPro, UniProtKB, EMBL European Bioinformatics Institute |
| Clinical Information | Drugs - FDA, DrugBank, MedScape; Clinical Trials - clinicaltrials.gov.in |
Future Scope
- Our further focus is on the study and inclusion of more skin cancers.
- Meaningful biological and molecular analysis of the collected data along with data visualization techniques.
- Large-scale genome characterization efforts involve the generation and interpretation of data at an unprecedented scale, which has brought into sharp focus the need for improved information technology infrastructure for data storage and new computational tools to render the data suitable for meaningful analyses.
- All the general considerations that will be kept in mind while storing cancer genomic data include-quality control (QC) of data, the accurate estimation of signal and noise in the collected data sets, reproducible approaches to complex genomic analyses, large scale genome characterization etc.
