Genome-scala algorithms

Level of Study

Master

Teaching semester

Spring. Runs first time spring 2020.

Objectives and Content

Will be offered for the first time spring 2020

The course covers advanced algorithms used for analysis of biological sequences with a focus on algorithms specialized for analysis of data produced by high-throughput technologies. The course includes use of indexing methods and related data structures including the de-Bruijn graphs, Burrows-Wheeler transform and related algorithms. The course also goes into use of such methods for de-novo sequence assembly, analysis of DNA resequencing data, and various other applications including ChIP-seq, ATAC-seq and bisulphite-sequencing. The course also includes methods for analysis of third-generation long read sequencing and single-cell sequencing technologies.

Learning Outcomes

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge, skills and general competence

The student

• Understands and is able to implement algorithms for analysis of high-throughput sequencing data including de-novo assembly, de-Bruijn graphs, Burrows-Wheeler alignment
• Understands and is able to design and choose approaches to analyze data from various applications of sequencing including ChIP-seq, ATAC-seq
• Has a general understanding of third-generation technologies for sequencing and the data produced

Required Previous Knowledge

INF100

Recommended Previous Knowledge

Be able to implement basic algorithms in a programming language of your own choice. A basic understanding of algorithms and efficiency, as well as statistics, is required. Good background within algorithms is recommended, at least corresponding to INF102. In addition, a good background in bioinformatics is recommended, corresponding to BINF100, BINF200, and BINF201.

Access to the Course

Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences.

Teaching and learning methods

The course is given as lectures and mandatory exercises

Lectures, 4 hours per week

Exercises, 2 hours per week

Compulsory Assignments and Attendance

Compulsory assignments are valid for 1 subsequent semester

Forms of Assessment

The forms of assessment are:

• Mandatory exercises, 50 % of total grade.
• Due to coronavitus situation the exam will be digital oral examination, 50% of total grade.

All compulsory assignments must be approved before examination.

Examination Support Material

Non-programmable calculator, according to the faculty regulations

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Assessment Semester

Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.

Reading List

The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester.

Course Evaluation

The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.

Programme Committee

The Programme Committee is responsible for the content, structure and quality of the study programme and courses.

Course Coordinator

Course coordinator and administrative contact person can be found on Mitt UiB, or contact Student adviser

Course Administrator

The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.