Objectives of Programme's Learning Outcomes

• Skill 4: Master communication techniques.
• Communicate, in English both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
• Skill 5: Develop and integrate a high degree of autonomy.
• Pursue further training and develop new skills independently.

Learning Outcomes of UE

At the end of this course, students will be able to understand the mechanisms of simple resolutions of computer science problems (algorithms). They will be able to develop simple algorithms on data from molecular biology (genetic sequences). Students will be able to explain the methods of sequence alignment and the issue of sequencing of genomes. They can assess the complexity of simple algorithms and explain why some problems have an inherent complexity that makes them insoluble in practice (NP-completeness). The students will be able to write small scripting programs to automate bioinformatics tasks.

Content of UE

Algorithmic; genetic sequence databanks; pseudo-language; variable; complexity of algorithms; efficiency; dot-plot, sequence alignment, substitution matrices, NP-completeness, assembly of DNA fragments

Prior Experience

Concepts of biochemistry; handling of a personal computer and Internet

Type of Assessment for UE in Q1

• N/A

Q1 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q3

• Oral examination

Q3 UE Assessment Comments

Oral exam to assess the mastership of the learned concepts (100%)

Type of Resit Assessment for UE in Q1 (BAB1)

• N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable