Next Generation Sequencing

Code:

BIOL4029

Acronym:

BIOL4029

Keywords
Classification	Keyword
OFICIAL	Biology

Instance: 2021/2022 - 1S

Active?	Yes
Responsible unit:	Department of Biology
Course/CS Responsible:	Master in Bioinformatics and Computational Biology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
E:BBC	1	PE_Bioinformatics and Computational Biology	1	-	6	42	162
M:BBC	13	The study plan since 2018	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

The aim of this course is to equip graduate students with advanced knowledge on NGS data analysis through bioinformatics and computational biology, both from a theoretical and practical point of view. The course is specially designed to help students interested in developing their careers in new sequencing technologies to solve biological problems related to proteins, genes, genomes and their interactions.

Learning outcomes and competences

Acquisition of knowledge about
Working with Unix command line and creation of small scripts for modifying files without needing to be opened.
Knowledge of techniques for cleaning third generation sequencing readings.
Mastery of assembling techniques of readings by mapping and de novo.
Analysis of RNA-Seq and transcriptomic data
Prediction and localization of genes within contigs and software used.
Functional annotation of proteins.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

none

Program

I. Introduction to NGS. Library preparation and sequencing technologies.
      Technology and methods behind each NGS platform

II. Introduction to command line tricks. Learning main commands to move across folders, create, delete, sort, move files in Unix system.



      Introduction to file editor AWK. Examples, integrated functions and conditions to apply to text files. Exercises

III. What is read data, visualising it and analysing quality scores. General overviews on how sequencing error effects:

1. read mapping, 


2. variant detection, 


3. haplotype reconstruction, 


4. phylogenies 


5. de novo assembly 


6. Differential expression.

Case study: mapping algorithms in detail.
Practical: variant detection and the evolution of drug resistance in HIV-1 using mapped reads.            
      Variant detection following removal of sequencing error using a Bayesian framework.

      Novel ways at looking at genetic variation that don’t require mapping of reads e.g. Markov Models.
      Transcriptome assembly vs genome assembly.

V. Three methods of transcriptome assembly in detail (and how error effect each of them):

1. Reference based to genome. 


2. De novo assembly of mRNA. 


3. A hybrid approach. 

Practical: preforming an assembly using the hybrid approach.

VI. The problem of Chimeras following assembly and how they arise (Network analysis).
        Practical - removing chimeras from the data.
        Differential expression
        Experiments based on quality filtered read counts.



        Post-sequencing pre-processing.
        Quality filters applied to raw data and adaptor removal
         Exercises

IX. Gene prediction in prokaryotes and eukaryotes. Basis of the prediction.
        Prediction based in homology and ab-initio methods (based in signals).
        Available software and exercises

X. Gene functional annotation.
       Terms databases, methodology and strategies.
         Available software and exercises

Mandatory literature

Teresa K. Attwood; Introduction to bioinformatics. ISBN: 0-582-32788-1

Complementary Bibliography

Tore Samuelsson; Genomics and Bioinformatics, Cambridge University Press, 2012. ISBN: 9781139022095

Teaching methods and learning activities

Classroom lessons
Activities directed and supervised by tutors.
Tasks/Exercises for applying acquiring knowledge

keywords

Natural sciences > Biological sciences > Biology > Genetics
Natural sciences > Biological sciences > Biology > Computational biology
Natural sciences > Biological sciences > Biology > Molecular biology

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Trabalho prático ou de projeto	90,00
Participação presencial	10,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Frequência das aulas	22,00
Trabalho de investigação	20,00
Total:	42,00

Eligibility for exams

Hours of student presence divided by the total number of hours of the course (≥ 80% assistance = frequencia)

Calculation formula of final grade

Sum of the grades obtained in the individual assignments submit by the student, divided by the total number of assignments distributed.

Examinations or Special Assignments

A assigment for each chaper of course program

Internship work/project

no

Special assessment (TE, DA, ...)

A assigment for each chaper of course program

Classification improvement

Modulation by class attendance

Observations

N.a