Learning outcomes

At the end of the course, a successful student will be able to choose and apply different regression methods or non-parametric methods according to their data and will be able to predict the function and taxonomic source of a given protein from sequence and structure analyses.

Goals

Acquire a tool box for advanced data analyses in statistics and introduce them to the basics bioinformatics analyses (sequence alignment, protein structure prediction, data clustering).

Content

The course comprises two main modules: statistics and bioinformatics.
Statistics:
1. ANOVA 1 and 2
2. Non-parametric tests
3. Multiple linear regression
4. ANCOVA
5. Linear mixed models
Bioinformatics
6. Sequence alignment
7. Protein structure prediction
8. Data clustering.
 

Table of contents

1. ANOVA 1 and 2

2. Non-parametric tests

3. Multiple linear regression

4. ANCOVA

5. Linear mixed models

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6. Sequence alignments

7. Protein structure prediction

8. Data clustering.

Exercices

Computer lab sessions using R

Teaching methods

Read the course reference material; question-and-answer sessions.

 

Assessment method

Mini-tests durimng quadrimester, examen in the session and presentation of the results of bioinformatics analyses

Sources, references and any support material

Material on webcampus.

Language of instruction

French