Course10210107680 lectures
Introduction to Proteogenomics
Prof. Sanjeeva Srivastava · IIT Bombay
About
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 all.
INDUSTRY SUPPORT : Thermofisher Scientific, Illumina.
Lectures
Week 1
- Lecture 1 : Proteogenomics overview-I
- Lecture 2 : Proteogenomics overview-II
- Lecture 3 : Introduction to Genomics - Part I: Gene sequencing and mutations
- Lecture 4 : Introduction to Genomics - Part II: Sequence Alignment
- Lecture 5 : Introduction to Genomics - Part III: Transcriptome
- Supplementary Lecture 1 : Perspectives in Proteogenomics - I
- Supplementary Lecture 2 : Advancement in Cancer Genomics
Week 2
- Lecture 6 : Introduction to Genomics - Part IV: Epigenome
- Lecture 7 : Introduction to Genomics: cBioPortal
- Lecture 8 : Genotype, Gene expression & Phenotype - I
- Lecture 9 : Genotype, Gene expression & Phenotype - II
- Lecture 10 : An overview of NGS technology
- Supplementary Lecture 3 : NGS-Sequencing by synthesis - I
- Supplementary Lecture 4 : NGS- Sequencing by synthesis - II
Week 3
- Lecture 11 : Introduction to Proteomics
- Lecture 12 : Proteomics: Sample Prep & Protein Quantification
- Lecture 13 : Applications of Proteomics
- Lecture 14 : Introduction to MS-based Proteomics - I
- Lecture 15 : Introduction to MS-based Proteomics - II
- Supplementary Lecture 5 : Applications of NGS – IonTorrent
- Supplementary Lecture 6 : Genomic Analysis using Droplet PCR - I
Week 4
- Lecture 16 : Introduction to MS-based Proteomics - I (Hands-on session)
- Lecture 17 : Introduction to MS-based Proteomics- II (Hands-on session)
- Lecture 18 : Data analysis: Normalization
- Lecture 19 : Data analysis: Batch Correction and Missing values
- Lecture 20 : Data analysis: Statistical Test
- Supplementary Lecture 7 : Genomic Analysis using Droplet PCR - II
- Supplementary Lecture 8 : Topics in Proteogenomics: Malaria case study
Week 5
- Lecture 21 : Machine learning and Clustering
- Lecture 22 : Hypothesis testing
- Lecture 23 : ProTIGY - I
- Lecture 24 : ProTIGY - II
- Lecture 25 : Proteomics Data Analysis
- Supplementary Lecture 9 : Proteomics Lab Demonstration I
- Supplementary Lecture 10 : Proteomics Lab Demonstration II
Week 6
- Lecture 26 : Workflow to Automated Data Processing
- Lecture 27 : Introduction to Fire Cloud
- Lecture 28 : FireCloud and Data Model
- Lecture 29 : Bioinformatics solutions for ‘Big Data’ Analysis - I
- Lecture 30 : Bioinformatics solutions for ‘Big Data’ Analysis - II
- Supplementary Lecture 11 : Introduction to Targeted Proteomics
- Supplementary Lecture 12 : Data analysis using Skyline
Week 7
- Lecture 31 : Large-scale data Science - I
- Lecture 32 : Large-scale data Science - II
- Lecture 33 : Large-scale data Science - III
- Lecture 34 : DIA-SWATH Atlas - I
- Lecture 35 : DIA-SWATH Atlas - II
- Supplementary Lecture 13 : Prediction Analysis
- Supplementary Lecture 14 : Pathway Enrichment and Network Analysis
Week 8
- Lecture 36 : Human Protein Atlas - I
- Lecture 37 : Human Protein Atlas - II
- Lecture 38 : Affinity based proteomics & HPA
- Lecture 39 : Clinical Considerations for OMICS - I
- Lecture 40 : Clinical Considerations for OMICS - II
- Supplementary Lecture 15 : Topics in Proteogenomics: Cancer case study
- Supplementary Lecture 16 : Integrative Genomics Viewer (IGV)
Week 9
- Lecture 41 : Introduction to Proteogenomics - I
- Lecture 42 : Introduction to Proteogenomics - II
- Lecture 43 : Sequence centric proteogenomics
- Lecture 44 : Variant Analysis
- Lecture 45 : Proteomics: Clinical Applications
- Supplementary Lecture 17 : Perspectives in Proteogenomics II
Week 10
- Lecture 46 : Predictive Analysis - I
- Lecture 47 : Predictive Analysis - II
- Lecture 48 : Association/ Marker Selection
- Lecture 49 : WebGestalt - I
- Lecture 50 : WebGestalt - II
- Supplementary Lecture 18: Perspectives in Proteogenomics III
Week 11
- Lecture 51 : Network Analysis - I
- Lecture 52 : Network Analysis - II
- Lecture 53 : Mutation and Signaling - I
- Lecture 54 : Mutations and Signaling - II
- Lecture 55 : Pathway Enrichment - I
- Supplementary Lecture 19 : Perspectives in Proteogenomics IV
Week 12
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Bombay
- Department
- Biotechnology
- Teacher
- Prof. Sanjeeva Srivastava
- Level
- Undergraduate
- Language
- English
- Subjects
- Biology
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed