P000058, Analysis of High Throughput Sequencing RNA-Seq Data II - apply to your own data, 1.5 Hp
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Syllabus

Finalized by: Research school Organism Biology, Mattias Thelander chair, 2023-10-13
Valid from : Second half-year 2023 (2023-07-01)

Level

Third cycle

Grading Scale

The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.

Course language

English

Entry Requirements

Objectives

The aims of this course are to:

- apply the knowledge of course "Analysis of High Throughput Sequencing RNA-Seq Data I" to student’s own data (in lieu of the exemplary dataset otherwise available)

- deepen the understanding of concepts taught in course "Analysis of High Throughput Sequencing RNA-Seq Data I"

- give the students the possibility to do RNA-seq analyses on their own data in a supervised setting

- expose the participants to modern computing technologies, as the hands-on analyses will be performed using cloud computing resources

- develop the participants’ awareness to critically reflect onto RNA-Seq analysis strengths and weaknesses

The corresponding Learning Outcomes are:

- strengthen the skills to navigate and interact with the Unix Command Line Interface (CLI) and RStudio Integrated Development Environment (IDE)

- perform data analyses (quality control, pre-processing, pseudo-alignment, differential expression etc) independently on a real world data set

- interpret and reflect on the results

- present the results to a scientific audience

Content

High Throughput Sequencing (HTS) and, in particular, Next Generation Sequencing (NGS) have revolutionized the way we conduct gene expression analysis. In comparison to micro-array-based methods, NGS has unleashed an almost unlimited power to perform gene expression analysis, for a similar price and with faster, more comprehensive, more efficient, and more reproducible characteristics. Rapidly, the data generation rate has exceeded the analytical capabilities and data analysis has become the major bottleneck in gene expression studies. This course, aimed at advanced students, has the objective to help addressing this problem by (i) helping participants critically assess the challenges faced in the HTS field, (ii) developing efficient communication skill with bioinformatician, and (iii) training students in analyzing HTS data.

The course is a follow up to course "Analysis of High Throughput Sequencing RNA-Seq Data I", where students are taught the principles and basics of RNAseq analyses. Here, the students will apply the knowledge gained from that course to their own data. The course will provide one ECT and will run over one week.

The students will analyze their own data by applying the methods learned in "Analysis of High Throughput Sequencing RNA-Seq Data I"with some guidance from the teachers (synchronous and asynchronous). Students will need to organize their access to the high-performance computing facility (probably their PI / supervisor will need to do so), with the help of SLUBI if needed. First, with asynchronous support from SLUBI, the students will perform the pre-processing and initial analysis of their data. Then they will meet with the trainers to address issues, discuss the data interpretation, and prepare a presentation about their project and results. The last day of the course will be in the form of an online mini-symposium, where the students will present their work to the other participants and trainers.

The course will mix independent and supervised hands-on analyses, literature review, data interpretation, visualization and presentation. The course will offer the participants the possibility to review and deepen their understanding of the course material at their own pace and to discuss it among peers.

Examination Formats and Requirements for Passing the Course

Pass / Failed. The requirements for passing are described in the course assessment criteria which are contained in a supplement to the course syllabus.

Responsible Department/Equivalent

Department of Plant Biology

Supplementary information

Other Information

Teaching on this course will be provided by the SLU Bioinformatics Infrastructure (SLUBI), namely by Nicolas Delhomme, Iryna Shutava, Adnan Niazi, Abu Bakar Siddique, Lizel Potgieter and Amrei Binzer-Panchal. The course is organized on behalf of the SLU research school Organism Biology. There is no tuition fee. Participants are expected to bring their own laptop for the practical computer exercises.

The course will be conducted online only. However, if students wish so, they can organise themselves in groups and join using one of the teaching facilities available at SLU.

The maximum number of participants is 20. SLU-registered PhD students are prioritized over other researchers but otherwise admission will be on a first come, first serve basis, provided that the minimal requirements are met.

If you have any question, please contact the course leader or the slack channel #organism-biology-research-school-rna-seq-course of the slubi-workspace.slack.com workspace.