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  5. Applied Computational Multi-Omics

Applied Computational Multi-Omics

Objectives

1. Understand state-of-the-art approaches applied to the study of the genome, transcriptome and transcriptional
regulatory factors using high-throughput sequencing data.
2. Acquire hands on in depth knowledge concerning the application of computational approaches to analyse and
integrate Multi-Omics data.
3. Understand the importance of the integration of Multi-Omics data in solving relevant questions in the Life and
Health sciences.



General characterization

Code

12496

Credits

6.0

Responsible teacher

Ana Rita Fialho Grosso, Paula Maria Theriaga Mendes Bernardo Gonçalves

Hours

Weekly - 4

Total - 91

Teaching language

Português

Prerequisites

Available soon

Bibliography

Rodriguez-Ezpeleta et al (2012) Bioinformatics for High Throughput Sequencing, Springer Cham



Teaching method

The lectures will consist in the presentation of the essential concepts for each topic. Hands-on exercises will be carried out in practical sessions using computers.



Evaluation method

Evaluation will be accomplished in one individual test and two projects to be conducted in small groups, contributing respectively 50% (test) and 50% (group project) of the final grade. The completion of each evaluation element is mandatory.. The test will be individual, without the possibility to consult bibliography. The group project consists in the data analysis, interpretation and integration of results in order to answer a biological question, with further preparation of a scientific manuscript.

Subject matter

1. Determination of Single Nucleotide Variants and Functional Impact

 

2. Determination of Structural Variants.

 

3. Biomedical Applications: study of diseases with a genetic component, Population Structure, Genome-wide Association Studies (GWAS), Survival Analysis.

 

4. Chromatin Immunoprecipation Sequencing (ChIP-seq) data analysis.

 

5. Motif discovery from ChIP-seq data.

 

6. Transcriptome sequencing (RNA-seq) data analysis.

 

7. Characterization of the Transcriptome profiles.

 

8. Identification of significant transcriptome alterations.

 

9. Integration of Multi-Omics data.

 

10. Functional Annotation and Analysis.