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Computational Approaches in Transcriptome Analysis (CATA)


Course name: Computational Approaches in Transcriptome Analysis (CATA)

Course dates: 6 May – 10 May 2019

Location: Department of Informatics, University of Bergen

Main organizer: Dr. Anagha Joshi, Computational Biology Unit, Department of Clinical Science, University of Bergen (Anagha.Joshi@uib.no)

Registration form: HERE

Study credits: 5 ECTS

Maximal number of Participants: 15

Registration deadline: April 4th

 

Lecturers:

  • Dr. Eric Bonnet, computational biologist and team leader, Centre National de Recherche en Génomique Humaine (CNRGH), France
  • Dr. Ana Cjevic, RCUK fellow, University of Cambridge, UK
  • Jayaraman Siddharth, Research fellow, University of Edinburgh, UK
  • Dr. David Dolan, Computational Biology Unit, Department of Informatics, University of Bergen
  • Professor Tom Michoel, Computational Biology Unit, Department of Informatics, University of Bergen
  • Dr. Sushma Nagaraja-Grellscheid, Computational Biology Unit, Department of Informatics, University of Bergen

 

Course description:

Microarray technologies revolutionised molecular biology by providing genome-wide snapshots of the transcriptional status of cell or tissue samples. This genome-wide exploration was facilitated by the evolution of standardized computational approaches for data processing and downstream analysis. Many of these tools were further extended or modified after the advent of RNA-sequencing technology. Now, after nearly a decade in existence, short-read bulk RNA-sequencing has decidedly gone mainstream, and further new technologies have evolved to reveal ever more intricate aspects of the transcriptional landscape of a cell. For example, single cell sequencing allows to trace cellular differentiation in minute detail, to study cell-to-cell heterogeneity, or to identify rare cell types, whereas long read single molecule RNA sequencing (Pacbio, Nanopore) allows the analysis of full-length transcripts to elucidate structural variations, expression variations within complex gene families, or the rapid identification of pathogens. Development of new technologies brings novel biological insights previously unaccessible due to technical limitations, but also means that new computational methodologies are required for data processing and analysis.

 

This five day course will familiarize students with state-of-the-art computational tools and methods for analyzing transcriptomes. This will include data generated using well established technologies such as microarrays and short-read bulk-RNA sequencing, as well as data generated using new emerging technologies, such as single-cell and long-read RNA-sequencing. The programme will be split each day into two half-day sessions, where the first half will take a lecture format, and the second half will be a hands-on practical session. The lectures will provide the introduction and computational analysis protocols for the technology presented that day. This will also include computational challenges arising from these data, both in terms of raw data processing and downstream analyses, as well as examples of novel biological insights that have already been gained. During the hands-on sessions, students will analyze example datasets and learn to interpret the results.

 

Course program:

An intensive one-week long course with lectures (including discussions in the mornings) and practical hands-on sessions (in the afternoons).

 

Date: 6th May 2019

1.30pm-2.00pm – Welcome ( +  practicalities) 

2.00pm-4.00pm – International invited lecture (Dr. Eric Bonnet)

4.00pm-4.30pm – Introduction to course material 

 

Date: 7th May 2019

Theme: Bulk RNA seq analysis

9.00am-10.00am –Invited Lecture 

10.00am-10.30am – Tea/Coffee

10.30am-12.30pm – Classroom teaching 

12.30pm-1.30pm – Lunch

1.30pm-4.30pm – Hands on/Practical session 

 

Date: 8th May 2019

Theme: RNA-seq Alternate Splicing

9.00am-10.00am – Invited Lecture 

10.00am-10.30am – Tea/Coffee

10.30am-12.30pm – Classroom teaching 

12.30pm-1.30pm – Lunch

1.30pm-4.30pm – Hands on/Practical session

 

Date: 9th May 2019

Theme: Single molecule, real time sequencing (PacBio)

9.00am-10.00am – Invited Lecture 

10.00am-10.30am – Tea/Coffee

10.30am-12.30pm – Classroom teaching 

12.30pm-1.30pm – Lunch

1.30pm-4.30pm – Hands on/Practical session 

 

Date: 10th May 2019

Theme: Single cell RNA-seq analysis

9.00am-10.00am – Invited Lecture 

10.00am-10.30am – Tea/Coffee

10.30am-12.30pm – Classroom teaching

12.30pm-1.30pm – Lunch

1.30pm-4.30pm – Hands on/Practical session 

 

The students will bring their own laptop with R installed.

 

Learning outcomes and competence

  • To obtain an understanding of established and emerging technologies for transcriptome analysis.
  • To familiarise with the research frontiers in the field of transcriptomics.
  • To acquire skills to perform transcriptome analysis, including data quality control, downstream analyses (differential expression, clustering, etc.), interpretation of results, and post-processing/presentation of results.
  • To be able to identify appropriate methods for a given problem, and to perform transcriptome analysis using R and Bioconductor packages

 

Prerequisites

 The course expects the participants to have:

1.       Knowledge of fundamentals of molecular biology: genes, expression of genetic information, eukaryotic cell structure, transcription and post-transcriptional control etc.

2.       Understanding of the linux operating system and basic skills in (any) programming language (knowledge of R would be an advantage).

 

Evaluation

Please provide a plan for evaluation (report based, presentation, written or oral or exam)

The evaluation will be report-based. The students will analyse a transcriptomic dataset (preferably their own data or data related to their own research topic), interpret and write a report.