Course Identification

An Introduction to deep-sequencing analysis for biologists
20223141

Lecturers and Teaching Assistants

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

Course Schedule and Location

2022
First Semester
Monday, 10:15 - 12:00, WSoS, Rm C
Thursday, 09:15 - 11:00, WSoS, Rm B
25/10/2021
18/03/2022

Field of Study, Course Type and Credit Points

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

Comments

We advise that first year MSc students will not take the course.

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 6 exercises

Evaluation Type

Examination

Scheduled date 1

27/02/2022
WSoS, Rm B
1000-1300
N/A

Scheduled date 2

16/03/2021
WSoS, Rm B
1000-1300
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. Experimental design for NGS (Next-Generation Sequencing)
  2. Introduction to Illumina NGS technology
  3. Illumina Primary Analysis Pipeline & Quality Control
  4. Sequence alignment to genome
  5. RNA-Seq gene level differential expression and Mars-seq analysis (UTAP pipeline)
  6. RNA-Seq transcript level analysis and de novo transcriptome assembly
  7. Clustering analysis on gene expression data
  8. Functional analysis: Gene Ontology and pathways
  9. Single cell RNA-Seq technology and analysis
  10. Epigenomic data analysis: ChIP-Seq & ATAC-Seq
  11. Variant detection analysis
  12. Long read sequencing technologies: PacBio and Nanopore
  13. 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