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NGS summer school Aarhus University
No full textDatasets for the NGS summer school at Aarhus University. The course material can be found on Github at this repository
Data for the course "Population Genomics" at Aarhus University
No full textDatasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch.
Data.tar.gz Contains the datasets and executable files for some of the softwares
Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes. You do this in the command line by
creating the folder Course_Env: mkdir Course_Env
untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
Activate the environment: conda activate ./Course_Env
Run the unpacking script (it can take quite some time to get it done): conda-unpack
Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the versione above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
conda env create -f environment_with_args.yml -p ./Course_Env
conda activate ./Course_Env
The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.
Description
The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.
The participants must at the end of the course be able to:
Identify an experimental platform relevant to a population genomic analysis.
Apply commonly used population genomic methods.
Explain the theory behind common population genomic methods.
Reflect on strengths and limitations of population genomic methods.
Interpret and analyze results of population genomic inference.
Formulate population genetics hypotheses based on data
The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.
Curriculum
The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.
Course plan
Course intro and overview:
Coop chapters 1, 2, 3, Paper: Genome Diversity Project
Drift and the coalescent:
Coop chapter 4; Paper: Platypus
Exercise: Read mapping and base calling
Recombination:
Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
Exercise: Phasing and recombination rate
Population strucure and incomplete lineage sorting:
Lecture: Coop chapter 6, Review: Incomplete lineage sorting
Exercise: Working with VCF files
Hidden Markov models:
Lecture: Durbin chapter 3, Paper: population structure
Exercise: Inference of population structure and admixture
Ancestral recombination graphs:
Lecture: Paper: Approximating the ARG, Paper: Tree inference
Exercise: ARG dashboard exercises + Inference of trees along sequence
Past population demography:
Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
Exercise: Inferring historical populations
Direct and linked selection:
Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
Admixture:
Lecture: Review: Admixture, Paper: Admixture inference
Exercise: Detecting archaic ancestry in modern humans
Genome-wide association study (GWAS):
Lecture: Coop lecture notes 99-120
Exercise: GWAS quality control
Heritability:
Lecture: Missing heritability and mixed models review ; Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
Exercise: Association testing
Evolution and disease:
Lecture: Genetic architecture review ; Article about "omnigenic" model ; Coop Lecture notes Sec. 11.0.1 (p217-221)
Exercise: Estimating heritabilit
hds-sandbox/NGS_summer_course_Aarhus: v2022.08.01
No full text<p>Final release for the 2022 version of the course material.</p>
<p><strong>Full Changelog</strong>: <a href="https://github.com/hds-sandbox/NGS_summer_course_Aarhus/commits/genomics">https://github.com/hds-sandbox/NGS_summer_course_Aarhus/commits/genomics</a></p>
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
hds-sandbox/NGS_summer_course_Aarhus: Summer Course 2023
No full text<p>Update for 2023 summer school</p>
<p><strong>Full Changelog</strong>: <a href="https://github.com/hds-sandbox/NGS_summer_course_Aarhus/compare/v2023.03.01...2023.03.01">https://github.com/hds-sandbox/NGS_summer_course_Aarhus/compare/v2023.03.01...2023.03.01</a></p>
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
hds-sandbox/NGS_summer_course_Aarhus: Latest version with updated material
No full textRepository for the NGS summer course at Aarhus universit
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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