Computational Analysis of Molecular Data

Code:

B4048

Acronym:

B4048

Keywords
Classification	Keyword
OFICIAL	Biology

Instance: 2016/2017 - 2S

Active?	Yes
Responsible unit:	Department of Biology
Course/CS Responsible:	Master in Biodiversity, Genetics and Evolution

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:BGE	9	Official Study Plan	1	-	3	21	81
M:GF	7	plano de estudos do Mestrado em Genética Forense a partir de 2013_2014.	1	-	3	21	81

Teaching language

English

Objectives

The aim of this curricular unit is to provide training in conceptual and practical aspects of data analysis of population genomics datasets, with emphasis on applications. This includes an introduction on the main concepts of coalescent, Bayesian, approximate Bayesian (ABC), and likelihood-based approaches. Emphasis will be on interpretation of output from statistical approaches and software programs. The main computational methods to analyze molecular data from high-throughput sequencing and to estimate demography and selection will also be taught.

Learning outcomes and competences

This curricular unit addresses current methodologies of computational analysis using sequence data. The advances in parallel, high-throughput sequencing technologies boosted the generation of sequence data, prompting for new ways of dealing with large volumes of information. In this curricular unit, focus will be put into current statistical methods that are best suited to particular questions but, more importantly, on the understanding of data types/formats, models and underlying algorithms. As the development of computational analysis methods for molecular data is rapidly growing, students will be able to cope with the newest progress in bioinformatics tools, as the curricular unit emphasizes fundamental concepts rather than particular software.

Working method

Presencial

Program

• Overview and introduction to statistical approaches in population genetics


• Frequentist, likelihood, and Bayesian approaches


• The theory of coalescence


• Estimation of effective populacional (Ne) and Approximate Bayesian Methods


• Sequence data analysis and quality scores


• Combining genetics and demography to assess dispersal (and detect selection)


• Detecting selection: FST-outliers and local adaptation


• Landscape genetics and spatial statistics


• Analysis using high-throughput sequencing


• RAD sequencing, Short read sequence analysis and SNP detection


• Data mining. File formats and conversion. Fetching data through APIs.


• Bootstrap, Jackknife and permutation methods


• Scripting languages (Python, Perl and R)

Mandatory literature

Gascuel O; Mathematics of Evolution and Phylogeny, Oxford University Press, 2007. ISBN: 978-0199231348
Manly BJF; Randomization, Bootstrap and Monte Carlo Methods in Biology, Chapman & Hall, 2006. ISBN: 978-1584885412

Teaching methods and learning activities

Theoretical classes, laboratory work (computational analysis).

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	50,00
Participação presencial	20,00
Trabalho escrito	30,00
Total:	100,00

Eligibility for exams

Conclusion of the laboratory work and respective report.

Attendance of a minimum of 50% of the theoretical course.

Calculation formula of final grade

Report of the laboratory practical work (30/100), written test on topics covered in the theoretical component of the discipline (50/100);

Attendance and participation in class (20/100).