Course Identification

An Introduction to deep-sequencing analysis for biologists
20203331

Lecturers and Teaching Assistants

Dr. Dena Leshkowitz, Dr. Ester Feldmesser, Dr. Gil Stelzer, Dr. Bareket Dassa, Dr. Noa Wigoda, Dr. Tsviya Olender, Dr. Ron Rotkopf
N/A

Course Schedule and Location

2020
First Semester
Tuesday, 09:15 - 11:00, Wolfson Auditorium

Tutorials
Wednesday, 11:15 - 13:00, FGS, Rm B
05/11/2019

Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; Regular; 3.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; Regular; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 3.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Elective; Regular; 3.00 points

Comments

We advise that first year MSc students will not take the course.
* No tutorial on December 11th, 2019

Prerequisites

No

Restrictions

50

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

50%
50%
Mandatory to submit 80% of exercises

Evaluation Type

Examination

Scheduled date 1

13/02/2020
Wolfson Auditorium
1000-1200
N/A

Scheduled date 2

19/03/2020
Wolfson Auditorium
1000-1200
N/A

Estimated Weekly Independent Workload (in hours)

2

Syllabus

This course is an introduction to deep sequencing analysis. The course is based on web tools. No programming skills are required.

  1. Introduction to Illumina  NGS technology
  2. NGS applications and introduction to analysis
  3. Illumina Primary Analysis Pipeline & Quality Control
  4. Illumina library preparation
  5. Sequence alignment to genome
  6. RNA-Seq gene level differential expression and Mars-seq analysis
  7. RNA-Seq transcript level analysis and de novo transcriptome assembly
  8. RNA-Seq analysis for non-model organisms
  9. Clustering analysis on gene expression data
  10. Functional analysis: Gene Ontology and pathways
  11. Single cell RNA-Seq technology and analysis
  12. Epigenomic data analysis: ChIP-Seq & ATAC-Seq
  13. Variant detection analysis
  14. Long read sequencing technologies: PacBio and Nanopore
  15. In-house developed NGS pipelines interface
  16. Metagenomics overview

Learning Outcomes

Upon successful completion of the course students will be able to:

  1. Demonstrate familiarity with the common applications of deep-sequencing.
  2. Discuss the basics steps of data analysis.
  3. Extract the biological meaning of the results.

Reading List

Website

N/A