Course Identification

Gene expression profiling: a practical course for RNA library preparation ready for sequencing

Lecturers and Teaching Assistants

Dr. Diego Jaitin, Dr. Hadas Keren-Shaul, Dr. Dena Leshkowitz, Dr. David Pilzer, Dr. Bareket Dassa
Dr. Adam Yalin

Course Schedule and Location

First Semester
21-25.11.21 Every day between 0900-1700. on the 14/12/2021 9:00-12:00 extra lecture., FGS, Lab

Field of Study, Course Type and Credit Points

Life Sciences: Laboratory; Elective; Regular; 0.50 points
Life Sciences (Molecular and Cellular Neuroscience Track): Laboratory; 0.50 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Laboratory; 0.50 points
Life Sciences (Computational and Systems Biology Track): Laboratory; 0.50 points


Course dates: 21-25/11/2021. 9:00-17:00 every day, for the laboratory part. An additional 3 hr session (lecture) will be given two weeks after the laboratory part for data analysis on 14/12/2021 9:00-12:00.
? Please note, in case the course will be required to be held in capsules/hybrid mode due to covid-19, we will require two extra days the laboratory practice, i.e. adding 28-29/11/2021.





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Registration by


Attendance and participation


Grade Type

Pass / Fail

Grade Breakdown (in %)


Evaluation Type


Scheduled date 1


Estimated Weekly Independent Workload (in hours)



Understanding biological systems using state-of-the-art genomics methods require, among other approaches, the gene expression profiling of different tissues, states, or conditions. RNA-seq is the method of choice to study gene expression with little background noise and a large dynamic range for detection of differentially expressed genes. This laboratory course will provide hands-on training on bulk MARS-seq, a method for digital gene expression analysis developed in Ido Amit lab at the Weizmann Institute. This protocol is designed for preparation of 3’ RNA sequencing libraries from low input RNA material in a simple, cost-effective fashion, due to early multiplexing of the samples. Libraries prepared in the course will be sequenced and analyzed. In order to enable fast and user-friendly data analysis, the LSCF Bioinformatics unit developed an intuitive and scalable transcriptome pipeline (UTAP) that executes the full process on Weizmann computer cluster (WEXAC), starting from sequences and ending with a comprehensive report. The sequenced data analysis part will be included in a session (3 hours) that will cover the theoretical basis for the analysis steps UTAP performs and a hands-on session to run the pipeline and understand the outputs it produces.

During the course, we will also present and discuss key aspects of genomics research, the power of unbiased gene expression profiling and Next Generation Sequencing (NGS), as well as quality control evaluation of input material, libraries and sequenced samples.

Learning Outcomes

Upon successful completion of this course students should be able to:

  1. Demonstrate an understanding of the notion of NGS technologies
  2. Demonstrate knowledge of RNA-seq technologies in general, including input requirements 
  3. Demonstrate an understanding of molecular biology processes in the process of RNA-seq library preparation
  4. Generate RNA-seq libraries from limiting amounts of RNA, ready to be sequenced in any available Illumina sequencer.
  5. Demonstrate familiarity with data resulting from Illumina platforms


Reading List

Recommended reading list:

1.  Jaitin DA, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014.PMID: 24531970 

2. Keren-Shaul H,et al. MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. PMID: 31101904

3. Wang Z. et al. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009. PMID: 19015660

4. Geraci F. Editorial: RNA-Seq Analysis: Methods, Applications and Challenges. Front Genet. 2020 PMID: 32256522 

5. Stark R. RNA sequencing: the teenage years. Nat Rev Genet. 2019. PMID: 31341269