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Last year (Spring 2022) was the first edition of the course, and it was an optional course, with a total of 7 students attending the lectures. This year (Spring 2023) the course became mandatory for MBIOT5 students. Two issues reported by the students last year were particularly critical to consider for scaling-up to a larger group. One was the use of mybinder.org, a free cloud-based solution for hosting jupyter notebooks, that was often unreliable. With the help of the IT department, a local jupyter server was deployed at NTNU and everything worked quite well. The other issue was the size of the room. Last year we used computer rooms, but the lectures were often interrupted by outside students that wanted to use the computer rooms. This year we switched to normal lecture rooms. However, we expected around 35 students this year, and reserved rooms with maximum capacity of 40 students, but more than 50 students initially signed up for the course, which resulted in a very crowded lecture room. Next year we will allocate a lecture room with larger capacity.

The teaching and learning activities, similarly to last year, consisted of a theoretical lecture, followed by an interactive learning activity, and computer exercises. The students were generally happy with the activities but had some issues with the computer exercises. The students would like to have more context regarding the exercises and how they connect to the theoretical part of the lecture. Some students had difficulties with the programming exercises, although they were expected to have acquired programming skills in the previous semester. Unfortunately, the students with difficulties did not seek my help and, although I shared some online training resources, they also did not try to improve their programming skills on their own. Some students mentioned this was because the programming exercises were not graded and not required for the exam. This is something that needs to be reconsidered for the next edition of the course. The course evaluation consisted of two group projects (mandatory, non-graded) and a final written exam. In general, all students performed well in the group projects. However, similarly to the lectures, they were stronger on the theoretical understanding than the practical implementation. Half of the students performed extraordinarily well on the exam, achieving top grade. The other half varied from fail (one case) to very good grades, which indicates that the exam was neither too easy nor too hard.

Since the course was just recently developed, the contents and syllabus continue to be quite up-to-date and relevant. The students are very happy with the topics addressed and find them relevant to their future professional life. The main adjustments to consider for next year is understanding how to restructure the computer exercises, and their evaluation, to better address the needs of the students that have difficulties with computer programming.

NTNU:TIA:REST

Institutt for bioteknologi og matvitenskap

Department of Biotechnology and Food Science

Ingen trykte eller håndskrevne hjelpemidler tillatt. Bestemt, enkel kalkulator tillatt.

Engelsk

Mer om vurdering

Evalueringen vil bestå av 2 deler:<ul><li>To gruppeprosjekt, obligatorisk, ikke gradert.</li><li>Avsluttende skriftlig eksamen.</li></ul>

Anbefalte forkunnskaper

TDT4110 Informasjonsteknologi, grunnkurs eller lignende innføringskurs til programmering (python).

Faglig innhold

Målet med dette kurset er å forberede bioteknologiske studenter til et stadig mer beregningsbasert arbeidsmarked. De siste årene har vi vært vitne til flere teknologiske fremskritt innen multi-omics-tilnærminger med høy gjennomstrømning, som genererer enorme mengder data og flytter flaskehalsen i utviklingen av nye bioteknologiske applikasjoner fra våtlaboratoriet til datamaskinen (så kalt tørr-lab). Dette målet er også i tråd med NTNUs visjon om å øke digital styrke i utdanning og forskning. Innholdet i emnet stammer fra tett sammenkoblede felt, nemlig bioinformatikk, beregningsbiologi, systembiologi og datavitenskap. Pensum er delt inn i to hoveddeler: Del I - Fra sekvenser til funksjoner:<ul><li>Historien om genomsekvensering</li><li>Metoder for genomannotering</li><li>Sekvensjusteringer og fylogenetiske trær</li><li>Proteinstrukturer og domener</li><li>Funksjonell annotering og ontologier</li></ul> Del II - Fra funksjoner til systemer:<ul><li>Multi-omics-teknologier</li><li>Dataanalyse og visualisering</li><li>Biologiske nettverk og reaksjonsveier</li><li>Reaksjonsvei-anrikelse analyse</li><li>Modellering av biologiske systemer</li></ul>

Kursmateriell

Læringsutbytte

<ul><li>Lær historien om bioinformatikk og beregningsbiologi.</li><li>Forstå genomsekvenseringsmetoder og deres begrensninger.</li><li>Bruk beregningsverktøy for genommontering og annotering.</li><li>Forklar prinsippene og anvendelsene for sekvensjustering.</li><li>Pakk ut informasjon fra bioinformatikkdatabaser ved hjelp av kommandolinjeverktøy.</li><li>Utvikle skript for å behandle og analysere forskjellige typer datasett.</li><li>Kombiner forskjellige datakilder for å analysere funksjonen til gener og proteiner.</li><li>Forstå funksjonene og begrensningene til flere omics-teknologier.</li><li>Kontekstualisere mangfoldig informasjon ved hjelp av biologiske veier.</li><li>Lag matematiske modeller av enkle biologiske systemer.</li></ul>

Læringsformer og aktiviteter

Undervisningen vil bestå av:<ul><li>Forelesninger: 3 t/uke (teori og aktiv diskusjon)</li><li>Datalab: 3 t/uke (programmeringsøvelser)</li><li>Prosjekter og selvstudier: ca 6 t/uke</li></ul>

Gruppeprosjekt 1

Gruppeprosjekt 2

Beregningsbasert bioteknologi

Bioteknologi

Bioteknologi (5-årig) - masterstudium

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Does not apply, because this was the first edition of the course.

The teaching and learning activities in the course: The lectures consisted of three parts: theoretical introduction to the topic using slides (aiming to reach Bloom’s Taxonomy levels 1-2), followed by an active learning activity, usually in the form of a game (Bloom’s level 3-4), and practical exercises to solve in the computer (Bloom’s level 5-6). Since this was the first edition of the course, I asked feedback from all the students on a regular basis. In general, the students were very engaged, especially during the active learning activities, and gave very positive feedback to the structure of the course. The computer exercises were executed in a cloud environment (using Jupyter notebooks) so that students didn’t have to install all the software dependencies in their own computers. In this first edition we used a free cloud hosting service, which did not always work optimally. Next year we will transition to a different solution, possibly a private server inside NTNU.   The assessment form(s) in the course: The course assessment consisted of two group projects with oral presentation during the course, and one final digital exam. The group projects allowed the students to apply what they had learned in some real-case scenarios. They were asked to work with real data and interpret their results in the context of a given biological application. The students were satisfied with the structure of the assessment but asked for the project descriptions to be more detailed. This feedback as already put into practice during the second project. Assessment of the learning environment in the course: The learning environment was not optimal. Since this first edition of the course was not a mandatory course (as it will be in the future for MBIOT5 students), it was hard to predict the number of students that would sign up. A very large computer room was allocated to the course. This room was frequently used by other students as a study room, and they often interrupted the lectures, many of them even refusing to leave the room when asked to do so. This created an uncomfortable situation for the students attending the lectures. Furthermore, all students opted to bring their own laptops, so the computers in the room where never used. Next time we will opt to use a regular lecture room instead, which is less likely to be disturbed by other students. Assessment of the course's academic content and learning outcomes: The aim of this course is to improve the computational skills of biotechnology students. It is divided into two main parts. The first part, Bioinformatics, covers genome sequencing, assembly, sequence alignment, protein structures, and functional gene annotation. The second part, Systems Biology, covers biological networks, omics technologies, data analysis, and mathematical modeling. These are recent topics that, unlike more classical topics, are not covered by a single textbook. The syllabus was compiled taking into consideration multiple textbooks and scientific publications. These will be continuously updated with every edition of the course. The feedback from the students regarding the relevance of the topics was very positive. All students mentioned that what they have learned in this course complements very well with other courses in their study program and that they have developed useful skills that they will be able to apply in the future.

Evalueringen vil bestå av 2 deler:<ul><li>To gruppeprosjekt (2-3 personer): Del I (25 %) + Del II (25 %)</li><li>Avsluttende skriftlig eksamen: 50 % av karakteren</li></ul>

Emnets målsetning er å introdusere forskningsfeltene bioinformatikk og beregningsbasert biologi, og forberede studentene på å gjennomføre beregningsbaserte oppgaver i et bredt utvalg av bioteknologiske områder. Studentene vil forbedre de generelle programmeringsferdighetene i python fra forrige semester gjennom biologi-orienterte øvelser, som å benytte databaser i bioinformatikk, prosessere eksperimentelle datasett, og bygge enkle modeller av biologiske systemer. Gitt at dette forskningsfeltet er i rask endring, vil vi fokusere på å bruke veletablerte verktøy og ressurser som trolig vil ha langsiktig relevans, for eksempel de som er tilgjengelige fra det Europeiske Instituttet for Bioinformatikk og rammeverket ELIXIR.Del 1 - Fra gener til funksjon<ul><li>Historien for gensekvensering</li><li>Metoder for sammensetting av genom</li><li>Standard filformater og -bibliotek</li><li>Sekvenssammenstilling</li><li>Bygge fylogenetiske trær</li><li>Funksjonell annotasjon og ontologier</li><li>Programmeringsadgang til bioinformatikk ressurser</li></ul>Del II - Fra funksjon til system<ul><li>Eksperimentelle metoder for cellulær fenotyping</li><li>Omikk data (transkriptomikk, proteomikk, metabolomikk)</li><li>Integrasjon av data og metadata</li><li>Metoder for normalisering og visualisering</li><li>Modellering av biologiske system</li></ul>

<ul><li>Studenten har generell kunnskao om bioinformatikkens historie.</li><li>Studenten forstår prosessen med sammensetning av genom og dens begrensninger.</li><li>Studenten kan anvende tilgjengelige verktøy for sammensetning av genom.</li><li>Studenten forstår prosessen for funksjonell annotasjon og dens begrensninger.</li><li>Studenten kan anvende tilgjengelige verktøy for funksjonell annotasjon.</li><li>Studenten er kjent med de viktigste databasene for bioinformatikk.</li><li>Studenten kan oppdage gen og protein funksjoner ved sekvensering.</li><li>Studenten kan forklare forskjellene mellom forskjellige omikk.</li><li>Studenten forstår konseptene data og metadata.</li><li>Studenten kan lese og transformere tabulære datasett programmatisk.</li><li>Studenten kan anvende tilgjengelige verktøy for data visiualisering.</li><li>Studenten kan forklare hva en model er, og dens begrensninger.</li><li>Studenten kan laste opp en model og kjøre simuleringer med passende programvare.</li><li>Studenten kan tolke resultater fra simulering og deres biologiske betydning.</li></ul>

Undervisningen vil bestå av:<ul><li>Forelesninger: 3 t/uke (teori og aktiv diskusjon)</li><li>Datalab: 3 t/uke (programmeringsøvelser)</li><li>Prosjekter og selvstudier: ca 6 t/uke</li></ul>

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Beregningsbasert bioteknologi - BT2100