Learning outcomes

This course consists of a Statistics module and a Bioinformatics module.

Statistics: At the end of the course, a successful student will be able to select and apply appropriate regression and non-parametric methods according to their data. Principal component analyses (PCA) may also be covered.

Bioinformatics: Students will become familiar with biological databases and data formats, and will be able to search databases, predict the function of a given protein based on sequence and structure alignments, determine the taxonomy of its source organism, and assess conservation and phylogeny. Students will also be introduced to biological network analyses and gene enrichment.

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. Data formats and databases

7. Sequence alignment and phylogeny

8. Protein structure prediction and structural alignment. Identification of functionally important residues

9. Data clustering.

10. Biological networks

 

Table of contents

1. ANOVA 1 and 2

2. Non-parametric tests

3. Multiple linear regression

4. ANCOVA

5. Linear mixed models

  •  

6. Sequence alignments

7. Protein structure prediction

8. Data clustering.

Exercices

Computer lab sessions using R and software for sequence alignments, clustering, and web-based tools. Attendance at lab/exercises sessions is mandatory.

Teaching methods

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

 

Assessment method

Stats: Statistical data analysis on a computer, open book, using R.

Bioinformatics: Sequence analyses and data clustering, open book.

Statistics:

Examination session:



  • Theory assessment: Written theoretical questions and/or multiple-choice questions, which may be integrated within the practical (TP) exam. The theory counts for 40% of the Stats module
  • Practical assessment: Exercises similar to those completed during lab sessions, using R. The practical exam counts for 60%.

Bioinformatics:

The theory of the bioinformatics module will be assessed alongside the practical part after the lab sessions. Students are expected to discuss and interpret the results of practical exercises and answer questions on the underlying theory.

Final grade:

The overall grade is at the discretion of the teaching team, based on:



  • the final exam for the Statistics module (80%);
  • the evaluation of the Bioinformatics module (20%);
  • the student’s overall participation (bonus up to 0.5 points).

A minimum overall grade of ≥10 is required to pass the course. In addition, students must obtain a minimum grade of 10 in each module, Statistics and Bioinformatics, in order to pass the teaching unit (UE).

Student participation during the course may provide a bonus of up to 0.5 points toward the first session grade.

Sources, references and any support material

Material on webcampus.

Language of instruction

French