Back in 2016, I realized that I copy & pasted code snippets for my various microbiome analysis. Instead of coming up with a clean concept to export these snippets, I abused another repository (hence the strange name for mapping identifiers from GreenGenes) to at least re-use the same code: ggmap. It once even had some unit tests, but in order to go fast, I ignored them failing over time. I never intended someone else to use this uggly part of software, but now realize that I torture my students by forcing them to do exactly this: use this badly documented, wobbly, partially deprecated code collection.

Should you have a slightly masochistic fun in refactoring my python code, I (and probably my students) would greatly appreachiate your efforts.

Almost 25 years ago, Hidden Markov Models (HMM) were successfully used in the emerging new field of Bioinformatics to predict the topology of trans-membrane proteins (original papers "A hidden Markov model for predicting transmembrane helices in protein sequences" and "Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes")

Authors of the program TM-HMM were excited about the tight coupling of mathematical modeling via HMMs and the modeled molecular biology. They used multiple algorithms to "score" amino acid sequences e.g. for being transmembrane proteins (Viterbi) or mark sub-sequences for being the helical regions (forward-backward) transecting the membrane. Labeling the many states of the HMM into "inside", "outside", "transmembrane" or "unlabeled" turns computation of the most probable "label" into an NP-hard problem - as proven much later by Broňa Brejová et al.. The TM-HMM program is a great example for real-world sized instances of dynamic programming; although a modern version is now based on deep learning - which, in principle, also uses forward-backward ideas.

Within a thesis, you would need to re-implement the HMM in Algebraic Dynamic Programming such that we can then use novel auto-generated algorithmic ideas to potentially improve prediction accuracy for this long standing problem.

Many RNA molecules do not encode proteins (mRNA), but by themselves exert important biological functions. Most functions are realized through their three dimensional structure - which is often times more conserved than the primary nucleotide sequence. Thus, sequence homology search with e.g. BLAST often does not return good results.

Secondary structure of RNA is the set of nucleotides that form base-pairs and functions as a scaffold for the final 3D structure. Since RNA folds hierarchically, prediction of secondary structure gives valuable insights about an RNA - while true 3D predictions are mostly computational intractable.

RNAshapes / pKiss / KnotInFrame and other software packages were developed at Bielefeld University and are popular tools to predict different aspects of secondary structures. They are based on the same algorithmic ideas (algebraic dynamic programming) and share the same code base back-end. The front end is written in Perl and lacks modern software engineering must-haves, like continuous testing, modularity or documentation.

Since our group is working on several algorithmic extensions of the back-end, we are looking for an encouraged student who re-implements the front-end in Python3, adds unit tests, creates bioconda packages, ... to allow for easy maintenance and distribution.

The transition to a biobased economy involving the depolymerization and fermentation of renewable agro-industrial sources is a challenge that can only be met by achieving the efficient hydrolysis of biomass to monosaccharides. In nature, lignocellulosic biomass is mainly decomposed by fungi. Further read.

In collaboration with the Institute of Food Chemistry and Food Biotechnology, we are sequencing specific fungi genomes and aim to understand their functional capabilities through wet-lab experiments and in-silico genomic annotations. We use Funannotate for the latter, however this pipeline needs to be adapted to operate on our high-performance compute cluster and be specialized towards the fungal genomes for which we generate short- and long- genomic reads and short- transcriptional reads.

Dimensionality reduction is necessary to project data of microbial experiments into 2D or 3D to allow interactive exploration and hypothesis generation. In QIIME2, one of the leading software platforms for microbial analysis, this is currently supported via Principal Coordinate Analysis (PCoA) with subsequent visualization via the Emperor tool. As with every projection, PCoA cannot preserve all pairwise distances which can generate visual artifacts that mislead the analyst. T-distributed Stochastic Neighbor Embedding (t-SNE) is an alternative to PCoA that focuses on local structures. A similar technique is Uniform Manifold Approximation and Projection (UMAP).
Within this project, existing QIIME2 plugins shall be extended such that users can not only use PCoA but also t-SNE and UMAP to compute projections that can be explored via Emperor. Python implementations and conda installable packages for t-SNE and UMAP exist but need to be properly wrapped into the Python3 based QIIME2 platform.

Background
The microbiota is an essential part of the body for different organisms and interacts with the host in countless ways. This interaction has a strong influence of the host health status and wellbeing. Currently, deep sequencing methods such as 16S next-generation sequencing is the gold-standard to assess the bacterial microbiome. However, there are different methods available. A recently described method by Zimmermann et al. (E.J. Immunol. 2016) uses flow cytometry do discriminate bacteria based on DNA-staining and size/shape.

At the "Leibniz Research Institute for Environmental Medicine" in Düsseldorf, we used this method and developed a novel and easy approach to evaluate characteristic features and differences between given microbiota samples. In our approach we use hexagonal binning across the bivariate flow cytometry data and the resulting hexagonal gates for dissimilarity calculations.

In context of a master thesis our new developed approach should be compared to already established tools for flow cytometry data analysis (FlowEMMi, FlowDiv, ...). Furthermore, the influence of the number of hexagons for automatic and representative clustering as well as for distance matrix of the treatment groups should be analysed. Additionally, correlations analysis with sequencing data are envisaged.

Data
Flow cytometry files (*.fcs) of samples from murine microbiota. The microbiota was manipulated by different treatments (e.g. dioxin, nanoparticles, diets). Corresponding next generation sequencing data for correlation analysis.

Requirements
Knowledge of the R statistical programming language and/or Python. Creativity and persistency to solve problems with data processing. An open-mind for biological questions and communications skills for explaining the work done to researchers from different fields.

Contact
For more information or questions, please contact Stefan Janssen or Katrin Hochrath (IUF Düsseldorf)

The mammal gut is a complex ecosystem harbouring approximately 10¹⁴ microorganisms with an important role in health and disease of the host. The main factors that contribute to the inter-individual variation of the intestinal microbiota are the environment, diet, age, gender and genotype.

Whether and to which extent the host genotype shapes the gut microbiome is still subject of debate. While some allocate a decisive role to the genotype, others demonstrate that the foster-mother's gut microbiota rather than the genotype are essential in the future composition of the gut microbiota. However, these hypotheses have been drowned secondarily in settings following other objectives and by different methods.

Thus, the main goal of this project is to study the drift of gut microbiome of simultaneously, with the same microbiome, naturally colonized C57BL/6J and BALB/c inbreed mice in relation to host genotype, sex and cage. For this C57BL/6J and BALB/c embryos were implanted into B6CF1 recipient foster-mothers in order to obtain parental C57BL/6J and BALB/c mice colonised naturally with the same gut microbiota of the foster-mothers. The two different mice strains were breed completely separated from each other in the standardised environmental conditions of individually ventilated cages (IVCs) over three generations. The standardisation of the environmental conditions will strengthen the role of intrinsic factors such as genotype or sex on microbiome variation. From the parental as well as from each of the three following generations, the composition of gut microbiome was recorded at 7 and 15 weeks of age by next-generation-sequencing analysis of V3-V4 regions of the 16S rRNA genes isolated from fecal and cecal samples.

This 16S analysis project requires to apply multiple alpha- and beta-diversity metrics to quantify complexity and diversity of the different microbial communities. Furthermore, differentially abundant features must be identified via discrete false discovery rates or similar statistical tools. These analyses among the two genetic backgrounds will allow us concluding whether the microbiome of the C57BL/6J and BALB/c can be tailored by the host genotypes.

In addition, recording of the cytokines levels defining the Th1/Th2 immune answer in serum and of the calprotectin concentration in the cecal content will correlate possible phenotypic outcomes with the gut microbiome. Overall, these aspects are essential for different pathologies where microbiota seem to be involved and a contribution of host genetic is assumed.

The enormous success and ubiquitous application of next and third generation sequencing has led to a large number of available high-quality draft and complete microbial genomes in the public databases. Today, the NCBI RefSeq database contains ~16,000 complete bacterial genomes. Concurrently, the selection of appropriate reference genomes (RGs) is increasingly important as it has enormous implications for routine in-silico analyses, as for example in detection of single nucleotide polymorphisms, scaffolding of draft assemblies, comparative genomics, etc. To address this issue many databases, methods and tools have been published in recent years e.g. RefSeq, DNA-DNA hybridization, average nucleotide identity (ANI) as well as percentage of conserved DNA values and kmer hashing methods (Mash). Nevertheless, the sheer amount of currently available databases and potential RGs contained therein, together with the plethora of tools available, often requires manual selection of the most suitable RGs.

To tackle this issue the bioinformatics command line tool ReferenceSeeker (https://github.com/oschwengers/referenceseeker) was recently designed and implemented which combines a fast kmer profile-based lookup of candidate reference genomes (CRGs) from high quality databases with rapid computation of (mutual) highly specific ANI and conserved DNA values in a scalable and rapid implementation.

As the analysis of cohorts of microbial genomes becomes increasingly important (viral & bacterial outbreaks), this approach should be extended from now providing the m best RGs for a single query genome to the best m RGs for a cohort of n query genomes.

Therefore, new scoring metrics and ranking methods need to be tested and evaluated. In addition, a precise assessment of the impact of the RG selection on subsequent analyses (SNP detection & creation of phylogenetic trees) is an interesting question which deserves further attention.

Map fragments to rep set of GG and check if leading mismatches occur.

The program RNAhybrid predicts potential targets for miRNAs. It was written in the early days of ADP in Haskell (version 1) and was ported into C via ADPc (version 2) some 18 years ago. It is time to lift this program into Bellman's GAP with new energy parameters, temperature modifications, fixes to the underlying grammar, and many more algorithmic tricks.

Besides the core algorithm, we need a modern command line interface (CLI) - written in Python, acompanied with a rich test bed and high quality documentation.