Cancer research has been significantly aided by advancements in proteogenomics technologies, where proteomics information derived from mass spectrometry is used to complement genomics using next generation sequencing. With the recent advent of Cancer Moonshot Project, the critical role that proteogenomics can play in improving cancer patient treatment is increasingly being recognized. This course will utilize advanced genomic and proteomic technologies and their data from high-quality human biospecimens to identify potentially actionable therapeutic molecular targets. This course is a part of a workshop by experts in the fields of proteomics and proteogenomics in cancer research from the Broad Institute of MIT and Harvard and Indian Institute of Technology Bombay. The course will comprise interactive lectures with case studies, hands-on sessions and demonstrations on proteogenomics aimed at accelerated understanding of cancer and will cover the principles of proteogenomics followed by experimental sessions, where proteomics data using LC-MS/MS will be processed and analyzed. The next step will be to integrate the proteomics data with genomics data, from The Cancer Genome Atlas for the proteogenomics analysis. Lectures and demonstrations on different computational methods will be performed for statistical data analysis of proteogenomics data.
INTENDED AUDIENCE : Biotechnology or Engineering background students having interest in latest technologies,(BE/B.Tech)
Biotechnology. Students with science or engineering background but course is open to allPREREQUISITES : Nill
INDUSTRY SUPPORT : Thermofisher Scientific, Illumina
COURSE LAYOUT Week 1: Proteogenomics overview- Part I, Proteogenomics overview- Part II, Introduction to Genomics- Part I : Gene sequencing and mutations Introduction to Genomics-Part II : Sequence alignment, Introduction to Genomics- Part III :Transcriptome, SL1: Advancement in Cancer Genomics, SL2: Advancement in Cancer GenomicsWeek 2: Introduction to Genomics IV : Epigenome, Introduction to Genomics : cBioPortal, Genotype, Gene expression & Phenotype - Part I, Genotype, Gene expression & Phenotype- Part II, An overview of NGS technology, SH1: NGS-Sequencing by synthesis, SH2: NGS- Sequencing by synthesisWeek 3: Introduction to Proteomics, Proteomics: Sample Prep & Protein Quantification, Proteomics: Sample Prep & Protein Quantification (Hands-on), Introduction to MS-based Proteomics- Part I, Introduction to MS-based Proteomics- Part II, SL 3: Applications of NGS – Ion Torrent, SL4: Applications of NGS – Ion TorrentWeek 4: Introduction to MS-based Proteomics- Part I (Hands-on), Introduction to MS-based Proteomics- Part II (Hands-on), Data analysis: Normalization, Data analysis: Batch Correction and Missing values, Data analysis: Statistical Tests, SH3: NGS- Ion Torrent, SH4: NGS- Ion TorrentWeek 5: Machine learning and Clustering, Hypothesis testing, ProTIGY- Part I,ProTIGY- Part II, Proteogenomics approach to unravel proteoforms, SL5: Genomic Analysis using Droplet PCR, SL6: Genomic Analysis using Droplet PCRWeek 6: Workflow to Automated Data Processing, Introduction to Fire Cloud, Fire Cloud and Data Model, Bioinformatics solutions for ‘Big Data’ Analysis- Part I, Bioinformatics solutions for ‘Big Data’ Analysis-Part II, SH5: Genomic Analysis using Droplet PCR, SH6: Genomic Analysis using Droplet PCRWeek 7: Data Science infrastructure management- Part I, Data Science infrastructure management- Part II, Data Science infrastructure management- Part III, DIA-SWATH Atlas-Part I, DIA-SWATH Atlas-Part II, SL7: Introduction to Targeted Proteomics, SH7: Data Analysis using SkylineWeek 8: Human Protein Atlas-Part I Clinical, Human Protein Atlas-Part II, Affinity based proteomics & HPA, Clinical Considerations for OMICS-Part I, Considerations for OMICS- Part II, SL8: Proteomics: PTMs, SL9: Clinical ProteomicsWeek 9: ntroduction to Proteogenomics-Part I, Introduction to Proteogenomics-Part II, Sequence centric proteogenomics, Gene Variant Analysis, Proteomics in Clinical studies,SH8: ProTIGYWeek 10:Supervised Machine learning- Predictive Analysis Part I, Supervised Machine learning- Predictive Analysis Part II, Supervised Machine learning- Marker Selection, Gene Set Analysis using WebGestalt- Part I, Gene Set Analysis using WebGestalt- Part II, SH9: Supervised Machine LearningWeek 11:Biological Network Analysis- Part I, Biological Network Analysis- Part II, Mutation and Signaling - Part I, Mutation and Signaling- Part II, Pathway Enrichment,SH10: Pathway Enrichment and Network AnalysisWeek 12:Gene Set Enrichment Analysis (GSEA), Pathway enrichment: GSEA, Linked Omics, Linked Omics
(Hands-on), Proteogenomics Conclusions, SL10: Topics in Proteogenomics- Malaria and Cancer case study