Projects

An important part of the workshop will be based on projects developed by students. Students will form project groups from the first day, and we will provide space and time each afternoon for the students to work on their short projects. Each group will be supervised by 1-2 young scientists, and senior scientists provide feedback. At the last day of the workshop, the students will present their project progress to other groups and submit a short report.

Below, you can see possible project titles already proposed by the young scientists. The students are also free, even encouraged, to bring their own proposals.

  1. option proposed by Tugce Bilgin and Murat Tuğrul:

    Evolution of gene regulation: Understanding the effect of tandem repeats for transcription factor binding sites

    Tandem repeat mutations in regulatory DNA regions can change gene expression and confer phenotypic divergence. Yet, we do not have a clear understanding how it happens. One plausible hypothesis is that they alter transcription factor binding profile on regulatory regions. In this project, we would like to elaborate on the interaction between tandem repeats and TF binding. We will start by discussing a relevant article by Contente et al. (2002) and what kind of models and genome analyses would be appropriate for such understanding. As hands-on part, we will work on the polymorphic population data set of PIG3 promoters where the “famous” tumor-suppressor p53 protein binds and induce cell death.

    Principal Reading

    – Contente et al. (2002), “A polymorphic microsatellite that mediates induction of PIG3by p53“, Nature Genetics

    Suggested reviews:

    – Gemayel et al., “Variable tandem repeats accelerate evolution of coding and regulatory sequences

    – Stormo (2000), “DNA binding sites: representation and discovery”, Bioinformatics ,16 (1): pp. 16-23

  2. option proposed by Emily Jane McTavish and Hannes Svardal :

    Using SNP data for population genetic and phylogenetic inference

    Many classical techniques for understanding evolutionary processes were developed for single gene, or few locus data sets. Over the past several years, genome wide datasets have become far more common, and in order to be tractable are often collapsed into sets of single nucleotide polymorphisms (SNPs). Analyzing SNP based datasets requires novel techniques and these data can be subject to ascertainment biases. Understanding neutral evolutionary processes using these data requires addressing these potential biases and using methods which are appropriate for SNP data. We will work with students to analyze either their own or publicly available SNP data to answer landscape genetic, population genetic or phylogenetic questions. We can apply methods such as Principal Component Analysis (PCA), coalescent phylogenetics for SNP data (i.e. SNAPP) and other techniques.

    Suggested reading:

    –  Bryant et al. (2012). Inferring Species Trees Directly from Biallelic Genetic Markers: Bypassing Gene Trees in a Full Coalescent Analysis. Mol Biol Evol 29, 1917–1932.

    – McVean, G. (2009). A Genealogical Interpretation of Principal Components Analysis. PLoS Genetics 5, e1000686.

    – Nielsen, R. (2004). Population genetic analysis of ascertained SNP data. Human Genomics 1, 218–224.

    – Novembre et al. (2008). Genes mirror geography within Europe. Nature 456, 98–101.

  3. option proposed by Melis Akman  and Mehmet Somel

    “Landscape transcriptomics”:

    Apart from gene sequence variation, gene expression variation can also have a large effect on the onset of important fitness traits in various environments. In this project, we are going to develop ideas to analyze how the environment pressures are reflected on traits and how these relate to gene expression variation. We will be analyzing a real data set composed of a common garden trait data of South African Protea repens, associated environmental variables from 19 source populations and RNA-seq sequence data. Main goal of the project is to gain insights on RNA-seq experimental design, data collection and processing. Some familiarity with R and command line applications might be useful in data analysis.

    Suggested reading:

    – Whitehead A. (2011), “Comparative genomics in ecological physiology: toward a more nuanced understanding of acclimation and adaptation,” Journal of Experimental Biology

    – Yeaman et al. (2014), “Conservation and divergence of gene expression plasticity following c. 140 million years of evolution in lodgepole pine (Pinus contorta) and interior spruce (Picea glauca×Picea engelmannii),” New Phytologist

    – Pespeni et al. (2013), “DIFFERENCES IN THE REGULATION OF GROWTH AND BIOMINERALIZATION GENES REVEALED THROUGH LONG-TERM COMMON-GARDEN ACCLIMATION AND EXPERIMENTAL GENOMICS IN THE PURPLE SEA URCHIN,” Evolution

    – Barshis et al. (2012), “Genomic basis for coral resilience to climate change,” PNAS

    – a useful website for basic concepts: http://sfg.stanford.edu/index.html

  4. option proposed by Luca Ferretti and Lilia Perfeito:
    • Impact of chromosome rearrangements on evolvability“: Chromosome rearangements are a type of genetic alteration that affects the order in which genes are coded in genomes. Changes in gene order affect gene expression and we recently showed they also affect fitness in fission yeast (Schizosaccharomyces pombe). We are now using experimental evolution to address how they may affect the rate of accumulation of both beneficial and deleterious mutations. In this context, chromosome rearrangements can be seen as large effect mutations that allow us to explore phenotype space and hence map the fitness landscape. In this project, we propose to use experimental data on chromosomal rearrangements to find the fitness landscape that best describes the evolution of yeast in a novel environment.
    • Genetic fitness landscapes: from experiments to evolution“: Experimentally resolved landscapes are typically small and in a fixed environment. What can we learn from them about real, large landscapes in variable environment, and evolution therein? How can they be combined with data from experimental evolution?
    • Suggested reading:

 

_DSC8525 _DSC8532

_DSC8524 _DSC8530
The photos taken during the student project time in the Workshop on Quantitative Evolutionary Biology 2013 in the Mathematics Village. See the link for the other photos from this event.

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s