Southwest-University: Difference between revisions
imported>Weigang m (→Papers & Data) |
imported>Weigang m (→Papers & Data) |
||
Line 59: | Line 59: | ||
| Transcriptome & Regulome || [https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-019-0477-8 Nava_etal_2019_BMCGenomics] || Tables S2 & S3 || RNA-Seq & CHIP-Seq | | Transcriptome & Regulome || [https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-019-0477-8 Nava_etal_2019_BMCGenomics] || Tables S2 & S3 || RNA-Seq & CHIP-Seq | ||
|- | |- | ||
| | | Proteomics || [[Qiu_etal_2017|https://www.ncbi.nlm.nih.gov/pubmed/28232952]] || (to be posted) || SILAC | ||
|- | |- | ||
| Example || Example || Example || Example | | Example || Example || Example || Example |
Revision as of 23:04, 21 June 2019
Professor, Department of Biological Sciences, City University of New York, Hunter College & Graduate Center
Adjunct Faculty, Department of Physiology and Biophysics Institute for Computational Biomedicine, Weil Cornell Medical College
Course Overview
Welcome to BioMedical Genomics, a computer workshop for advanced undergraduates and graduate students. A genome is the total genetic content of an organism. Driven by breakthroughs such as the decoding of the first human genome and next-generation DNA -sequencing technologies, biomedical sciences are undergoing a rapid and profound transformation into a highly data-intensive field.
Genome information is revolutionizing virtually all aspects of life sciences including basic basic, medicine, and agriculture. Meanwhile, use of genomic data requires life scientists to be familiar with concepts and skills in biology, computer science, as well as data analysis.
This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises.
The pre-requisites of the course includes college-level courses in molecular biology, cell biology, and genetics. Introductory courses in computer programming and statistics are preferred but not strictly required.
Learning goals
By the end of this course successful students will be able to:
- Describe next-generation sequencing (NGS) technologies & contrast it with traditional Sanger sequencing
- Explain applications of NGS technology including pathogen genomics, cancer genomics, human genomic variation, transcriptomics, meta-genomics, epi-genomics, and microbiome, and single-cell genomics
- Visualize and explore genomics data using RStudio
- Replicate key results using a data set associated with a primary research paper
Useful links
- Install R and R Studio
- Unix Tutorial
- Textbook
Quizzes and Exams
Student performance will be evaluated by attendance, three (4) quizzes and a final report:
- Attendance: 50 pts
- Quizzes: 4 x 25 pts = 100 pts
- Final report: 50 pts
Total: 200 pts
Course Schedule
- July 8 (Mon), 8:40-12:10
- July 9 (Tu), 8:40-12:10
- July 10 (Wed), 8:40-12:10
- July 11 (Thur), 8:40-12:10
- July 12 (Fri), 8:40-12:10
- July 15 (Mon), 8:00-12:10
- July 16 (Tu), 8:00-12:10
- July 17 (Wed), 8:00-12:10
- July 18 (Thur), 8:00-12:10
- July 19 (Fri), 8:00-12:10
Papers & Data
Omics Application | Paper link | Data set | NGS Technology |
---|---|---|---|
Microbiome | Rimoldi_etal_2018_PlosOne | S1 Dataset | 16S rDNA amplicon sequencing |
Transcriptome | Wang_etal_2015_Science | Tables S2 & S4 | RNA-Seq |
Transcriptome & Regulome | Nava_etal_2019_BMCGenomics | Tables S2 & S3 | RNA-Seq & CHIP-Seq |
Proteomics | https://www.ncbi.nlm.nih.gov/pubmed/28232952 | (to be posted) | SILAC |
Example | Example | Example | Example |
Example | Example | Example | Example |
Example | Example | Example | Example |
Example | Example | Example | Example |
Example | Example | Example | Example |
Example | Example | Example | Example |