Study programme 2021-2022Français
Algorithms and Bioinformatics
Programme component of Master's in Computer Science à la Faculty of Science

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M1-SCINFO-003-MOptional UEDELGRANGE OlivierS829 - Informatique théorique
  • DELGRANGE Olivier

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français150300055.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-INFO-022Algorithms and Bioinformatics1503000Q1100.00%
Programme component
Corequis

Objectives of Programme's Learning Outcomes

  • Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
  • Manage large-scale software development projects.
    • Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
    • Lead a project by mastering its complexity and taking into account the objectives, allocated resources and constraints that characterise it.
    • Demonstrate independence and their ability to work alone or in teams.
  • Manage research, development and innovation.
    • Understand unprecedented problems in computer science and its applications.
    • Organise and lead a research, development or innovation project to completion.
    • Methodically research valid scientific information, lead a critical analysis, propose and argue potentially innovative solutions to targeted problems.
  • Master communication techniques.
    • Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
  • Develop and integrate a high degree of autonomy.
    • Develop and integrate a high degree of autonomy to evolve in new contexts.

Learning Outcomes of UE

At the end of the instruction, the students will be able to explain the development needs of algorithms and data structures in the context of voluminous data such as genetic sequences. Students will be able to explain the classical algorithms for bioinformatics and string-matching. They will be able to implement algorithms for efficient manipulation of DNA sequences.

Content of UE

Algorithms, complexity of algorithms, strings, string-matching, sequence alignment; suffix tree; DNA sequencing and fragment assembly

Prior Experience

algorithmic, complexity of algorithms, programming in an imperative or object language

Type of Assessment for UE in Q1

  • Oral examination
  • Practical test

Q1 UE Assessment Comments

- Oral examination on the theoretical part, the score is NT (/20.)
- Presentation and written report on the (enhanced) project developed by group for the practical part, the score is NP (/20)

If NT> = 8/20 and NP> = 8/20, then SCORE = (NP + NT) / 2
Otherwise SCORE = min (NP, NT)
 

Type of Assessment for UE in Q3

  • Oral examination
  • Practical Test

Q3 UE Assessment Comments

- Oral examination on the theoretical part, the score is NT (/20.)
- Presentation and written report on the (enhanced) project developed by group for the practical part, the score is NP (/20)

If NT> = 8/20 and NP> = 8/20, then SCORE = (NP + NT) / 2
Otherwise SCORE = min (NP, NT)

If one of the two parts was successful (score> = 10) in Q1, it no longer needs to be presented in Q3, the score is retained.

Type of Resit Assessment for UE in Q1 (BAB1)

  • N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
S-INFO-022
  • Cours magistraux
  • Conférences
  • Préparations, travaux, recherches d'information

Mode of delivery

AAMode of delivery
S-INFO-022
  • Face to face

Required Reading

AA
S-INFO-022

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-INFO-022Not applicable

Recommended Reading

AA
S-INFO-022

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-INFO-022Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-INFO-022- Introduction to Computational Molecular Biology
  J. Setubal, J. Meidanis
  PWS Publishing Company, 1997
- Algorithmic Aspects of Bioinformatics
  HJ Böckenhauer - D. Bongartz
  Springer, 2007

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
S-INFO-022Authorized
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de génération : 17/09/2021
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