Bioinformatics and Systems Biology Core
The analysis of high-throughput genomics, proteomics, metabolomics and epigenomics data is the most critical step in systematically understanding pathophysiology of a disease as well as identifying potential biomarkers. The analysis of OMICS data often presents a challenge to translational and basic science researchers. The mission of Bioinformatics and Systems Biology core is to provide expertise and infrastructure in designing, analyses and simulation of high-throughput OMICS data to answer underlying biological questions. The core support analysis of data from many next-generation sequencing assays including transcriptional quantification (RNA-Seq), protein-nucleic acid interactions (ChIP-Seq), global methylation, genotyping or variant analysis through genome sequencing. To support cutting edge research, a special emphasis was made on implementing/developing systems biology frameworks and models for integrative analysis of genomic, epigenomics proteomic, metabolomic, imaging and clinical data to identifying key molecules driving pathophysiology. In addition to analyses support, core also provides resources for data management and high performance computing.
- Experimental design assistance for OMICS studies
- Analysis of high throughput sequencing, microarray, proteomic and metabolomics data.
- RNA-Seq analysis (mRNA expression/alternate splicing/isoforms/novel transcripts or gene/ Gene fusion/ detection of RNA editing)
- Variation discovery/allele analysis (CNV/SNP) from Exome or DNA-Seq data
- Analysis of epigenomics, ChIP-Seq and DNA Methylation data
- Comprehensive workflow for analysis of Microbiome sequencing data
- Single cell mRNA-Seq data analysis from various platforms.
- Integrated analysis of transcriptome, miRNA, epigenome and proteomics data
- Functional Genomics analysis of data including pathway and functional enrichment analysis
- Systems biology analysis of data to identify potential therapeutic targets or biomarkers.
- Development of predictors using Bayesian, SVM, ANN, and KNN Algorithms for diagnosis and prognosis of disease.
- Target design for enrichment and deep sequencing
- Antigenicity and subcellular localization prediction
- Data and text mining from Public databases and literature
- Personalized consulting
- Customized data analysis and interpretation