Biology Education

Traditionally, the choice of codons to encode amino acid sequences of proteins has been assumed to be neutral terms of fitness. We now know that synonymous substitutions that do not change amino acid identities can strongly influence the function of proteins. This is because codons affect mRNA stability, expression levels, protein folding rates, and how the protein folds as it emerges from the ribosome. We study how the choice of codons is correlated with the three-dimensional structure of proteins and the evolution of codon usage. Our work has resulted in a database where codon usage is correlated with 3D protein structure and other features. This database can be mined to identify correlations between structural features in protein, such as secondary structure, and codon usage. We also built large language models of codon sequences to understand what features control codon usage in coding sequences.

Project: We would like to develop better ways to visualize our data by creating a webpage that displays the data in our database. This includes mapping codon sequence biases onto 3D structures, visualizing codon conservation in protein families, and mapping coding usage on species phylogenetic trees.

Desired background: The candidate should have a strong interest in visualization and a solid background in Python programming.

Environment: In addition to research on codon usage, the research group (andrelab.lu.se) also does research in protein structure prediction, computational protein design, and protein evolution. The group uses a range of computational approaches (from deep learning to molecular simulation) and experiments to complement computational projects, providing a multidisciplinary environment for a thesis student to learn.

Contact: If this sounds interesting to you, contact Ingemar André, ingemar.andre@biochemistry.lu.se, for further information.

Our lab works on identifying the genes that regulate immune cell formation and function in humans. For this, we have produced large datasets of paired immunophenotypic data (1500 immune cell features measured by flow cytometry) and genome-wide genotype data in thousands of individuals. The flow cytometry data is analysed by a method called “gating”, where each cell is classified into different categories according to parameters like size, complexity and surface protein expression. To automate the gating process, we have developed pattern recognition tool (AliGater). We are now looking for a student who will use AliGater and extend the tool as needed to gate a new set of samples.

This project is suitable for a for ambitious students who are comfortable with Python, ideally as a 30 credit project. You will get support from the developers of AliGater and work together with a diverse team of researchers with clinical, wet lab and bioinformatics expertise.

For more information, contact: Aitzkoa Lopez de Lapuente Portilla aitzkoa.lopez_de_lapuente_portilla@med.lu.se

Our Clinical neurogenetics research group is focused on the investigation of the genetic background of a number of neurological disorders. Patients with disorders like Parkinson disease, dystonia, ataxia, hereditary causes of dementia or stroke, are examined at the Department for Neurology, Skåne University Hospital, within research studies and their potential contributing or causative genetic factors are elucidated. The aim of our research is to identify new gene mutations that cause these diseases and to understand how gene mutations affect cellular mechanisms.

At present, we can offer the following project for a bioinformatics masters student:

We performed genetic testing on 12 samples from patients with frontotemporal dementia and Whole Genome Sequencing (WGS) data from these patients are available for analysis. The overall aim of this project is to investigate the data for potential genetic variations that can cause this disorder, as for example Single Nucleotide Variants (SNVs) and Short Tandem Repeats (STRs).

Applicants:

MSc bioinformatics students with an interest in neurogenetics are very welcome to apply. Familiarity with Bash, Python and Linux is a requirement. Recommended project length is 15cr (10 weeks).

Supervisors:

Efthymia Kafantari (MSc in bioinformatics and in medical genetics), Department for Neurology, Skåne University Hospital and Clinical Neurogenetics, Lund University (primary supervisor)

Joel Wallenius (MSc in bioinformatics), Department for Neurology, Skåne University Hospital and Clinical Neurogenetics, Lund University

Andreas Puschmann (neurologist, professor), Department for Neurology, Skåne University Hospital and Clinical Neurogenetics, Lund University

Contact:

Efthymia Kafantari      efthymia.kafantari@med.lu.se

Joel Wallenius              joel.wallenius@skane.se

Andreas Puschmann   andreas.puschmann@med.lu.se

Remarkably few vertebrates display tumor resistance properties. This select group includes salamanders, renowned for their tumor resistance, yet the molecular mechanisms underlying this ability remain unknown. Inspecting the attributes of salamanders reveals their massive genomes, ranging from 4.5 to 43 times larger than the human genome, as a unique biological feature. The sheer volume of genetic material in salamander cells makes the task of cell division complex, in terms of replication and also energetic demands. What are the advantages associated with possessing a giant genome? Our preliminary results suggest that giant genome size has imparted pressures that shaped novel innovations in cell cycle control. These innovations may be linked to the noted tumor resistance observed in newts.

The project: Others and we have created various scRNAseq datasets from animals with various genome sizes, different tissues, and across contexts (e.g., development versus adult homeostasis). The susceptibility to cancer and requirements for cell cycle regulation vary across these contexts. This project entails generating convolutional neural networks for coexpression to define the cell cycle regulome and its plasticity across cell types, lifestage, and phylogeny. The goal is to define a core, evolutionary conserved cell cycle regulome and subnodes of this regulome that are deployed across various species, genome sizes, and cell types. Moving forward, we will embed the neural-network-derived gene regulatory network into an agent-based model (ABM) aimed at quantitating the network resilience to specific gene perturbations.

The student: Master student (preferably 60cr thesis) with experience in bash, R or python. Experience in Java and/or neural networks and deep learning is advantageous.

What to expect: You will become well versed in quality control of publicly available data, working with various non-human species datasets, data integration, and workflow management. You will learn about cell cycle biology and explore its divergences across species. Depending on progress and interest you may also delve into generating neural networks and their integration in ABMs.

If this sounds of interest, please email nicholas.leigh@med.lu.se and virginia.turati@med.lu.se with a short motivation for what interests you about this project and your CV.

We use whole-genome and whole-transcriptome sequencing data from primary cancer samples to elucidate genetic subgroups of potential clinical relevance. Our next step will be to identify mutational signatures on the single-nucleotide, copy number and structural variant levels. To do so, multiple variant callers will be used, their output consolidated into a consensus list of variants per case, and then signature profilers will extract and annotate relevant mutational signatures. The project would entail setting up a pipeline that can generate variant call format (vcf) files from ready-mapped whole-genome sequencing data, match the output of multiple variant callers within each category of mutations and then apply signature extraction tools.

Contacts:

Karolin Hansén Nord PhD,  karolin.hansen_nord@med.lu.se

Associate Professor, Senior Lecturer

Karim H. Saba PhD

Associate Researcher

Division of Clinical Genetics

Department of Laboratory Medicine

Faculty of Medicine

Lund University

Web page Complex Genomes in Cancer

Description: Endogenous retroviruses (ERVs) are genomic remnants of retroviral infections and subsequent expansion that are present in most organisms. In the human genome ERVs occupy approximately 8% of our DNA, some of which retain enhancer and promoter sequences to drive the expression of their viral-genes. However, many of these sequences also affect the expression of other nearby genes in the host genome. Recent research suggests that ERVs have been evolutionarily selected in genomic locations where they can drive or boost the expression of immune-related genes. They can also trigger an immune response by the transcription of their viral-genes.

Aberrant ERV transcription has been observed in several neurological diseases and upon neuroinflammation. Interestingly, an ERV-derived envelope protein has been found in cerebral spinal fluid in about a third of schizophrenia patients of a large cohort, and researchers at our lab have shown that the presence of this protein in the brain impairs synaptic maturation in mice models (DOI: 10.1126/sciadv.abc0708).

To study if this is the case in human neurons and provide a potential therapeutic model, the lab has been growing neuronal cultures transcriptionally activating ERVs using a CRISPR activation system. We have, so far, validated 3 guides using induced pluripotent stem cells and are waiting for the sequencing data of the neuronal cultures experiments. 

This project represents a bioinformatic challenge given that the envelope protein identified at psychiatric patients has not been pinpointed to a specific genomic locus. We have or are in the process of creating several datasets related to this project including bulk RNAseq, single nuclei RNAseq, CUT&RUN for histone modifications and long read RNAseq. 

We are looking for: Master student (preferably but not exclusively of 60cr thesis) who feels comfortable working on the terminal (bash), scripting with R, and using git. Knowledge on workflow management (Snakemake or Nextflow) is a plus. 

You will gain experience with: workflow management, making your own pipelines and using others, working on a computing cluster, using git, using statistical models to identify differentially expressed genes between experimental conditions accounting for batch effects and other covariates, transposon biology and how to adequate your bioinformatical approaches for repetitive elements, scientific thinking, visualization, and writing. 

If you find this project interesting, send me a message to raquel.garza@med.lu.se, tell me what you find interesting about it, and a little bit about your experience and ambitions!