Section for Food Microbiology, Gut Health, and Fermentation

Course material for handling 16s data using R with phyloseq and other packages

https://mortenarendt.github.io/MicrobiomeDataAnalysis/index.html

Sparse partial least squares regression and classification using a phylogenetic similarity penalty

https://github.com/mortenarendt/StructuralKnowledgeModl

ASCA and permutation testing for non-orthogonal designs

https://github.com/mortenarendt/ASCA

Ref: 
Rasmussen, M.A., Khakimov, B., Engel, J. and Jansen, J., 2024. Permutation Strategies for Inference in ANOVA‐Based Models for Nonorthogonal Designs Including Continuous Covariates. Journal of Chemometrics, p.e3580. (See publication)

Vertical transfer of microbes using individual ASV analysis using a combined meta analysis statistics.

https://github.com/mortenarendt/VagTransfer

https://github.com/mortenarendt/MBtransfeR

Refs: 
Rasmussen, M.A., Thorsen, J., Dominguez-Bello, M.G., Blaser, M.J., Mortensen, M.S., Brejnrod, A.D., Shah, S.A., Hjelmsø, M.H., Lehtimäki, J., Trivedi, U. and Bisgaard, H., 2020. Ecological succession in the vaginal microbiota during pregnancy and birth. The ISME journal, 14(9), pp.2325-2335. (See publication)

Mortensen, M.S., Rasmussen, M.A., Stokholm, J., Brejnrod, A.D., Balle, C., Thorsen, J., Krogfelt, K.A., Bisgaard, H. and Sørensen, S.J., 2021. Modeling transfer of vaginal microbiota from mother to infant in early life. Elife, 10, p.e57051. (See publication)

Data fusion of vastly different data-sources obtained on the same set of samples done by using kernel transformation coupled with graphical modelling using graphical LASSO. 

https://github.com/mortenarendt/KerGLASSO

Refs: 
Nørgaard, S.K., Linder‐Steinlein, K., Eliasen, A.U., Stokholm, J., Chawes, B.L., Bønnelykke, K., Bisgaard, H., Smilde, A.K. and Rasmussen, M.A., 2021. On using kernel integration by graphical LASSO to study partial correlations between heterogeneous data sets. Journal of Chemometrics, 35(10), p.e3324. (See publication)

Nørgaard, S.K., Følsgaard, N., Vissing, N.H., Kyvsgaard, J.N., Chawes, B., Stokholm, J., Smilde, A.K., Bønnelykke, K., Bisgaard, H. and Rasmussen, M.A., 2023. Novel Connections of Common Childhood Illnesses Based on More Than 5 Million Diary Registrations From Birth Until Age 3 Years. The Journal of Allergy and Clinical Immunology: In Practice, 11(7), pp.2162-2171. (See publication)

Bi-linear factorization of matrices with a generalized linear mapping (|Rb|1 < L) penality on the parameters.

https://github.com/mortenarendt/genL1

Ref: 
Arendt Rasmussen, M., 2017. Generalized L1 penalized matrix factorization. Journal of Chemometrics, 31(4), p.e2855. See publication

BactFlow is a pipeline for bacterial genome assembly of single isolate and metagenomics sequencing reads extracted from Oxford Nanopore Technology (ONT) and Illumina platforms. It is designed using Nextflow DSL 2 technology and reads the generic outputs of Guppy and Dorado basecallers.

https://github.com/farhadm1990/bactflow.

This workflow includes the necessary steps involved in the analysis of 16S rRNA microbiota amplicons data from raw sequences to publication-quality visualizations and statistical analysis. Non-cultured 16S rRNA metagenomics is a promising method for understanding the ecology of an environment in regards with the number and the structure of the microbiome in association with the environmental factors, e.g. host-microbiome interactions. In prokaryotes there is a ubiquitous gene compartment integrated in the ribosome, so-called 16S rRNA genes, which are highly conserved among prokaryotes and at the same time having hypervariable regions (HVRs) V1 to V9, which are good targets for evolutionary and ecological studies on prokaryotes Jünemann et. al (2017). This module is mainly focused on 16S rRNA gene data, but I can carefully say that you can apply most of the techniques explained here to genome data and count multivariate datasets. Note: all this workflow has been done on Jupyter notebook on a cluster node with 120 GB processer from Aarhus University, Denmark. In order to multitask in different nodes, tasks on Qiime2 have been submitted to the cluster by separate bash scripts.

https://github.com/farhadm1990/16S-rRNA-amplicon

A shiny-app for interactive bioinformatics steps and statistical analysis on all count data especially 16S rRNA genes and Whole Genome (meta) genomics sequence analysis. The app includes all adjusting screws and buttons to help you translate sequence data into high resolution tables and plots. In other word, MicroLoop can do a task in less than a day which otherwise weeks might be required to accomplish.-

https://github.com/farhadm1990/microloop

A python package to create html-based reports with possibilities of adding text, table, header, code chunks and responsive tables as well as plots. 

https://github.com/farhadm1990/pytmler

An interactive R package to convert relative abundance of 16S rRNA data into their respective copy-numbers via an internal Lamba Phage standard. 

https://github.com/farhadm1990/lambda_rector

A function to create association network for microbiome data: bacteria-bacteria and feature-metabolite association. This function is dedicated to make graph/network based on the Spearman (also Pearson) correlation and the significant level of this correlation corrected for false dicorevy rate (FDR) by Benjamini-Hochberg (by default, other methods are also accepted. See the help sheet for p.ajust() function). This is a costume function and as it does not count for partial effects of taxa, you must only use it for visualization and not for validation of associations. The function is also able to perform these analysis with and without Centered-Log ratio (CLR) transformation to account for difference in read depth. For the input matrix, you can simply use the phyloseq object and the function will do the rest. By default, the graph will be made from a dataframe, based on the most significantly correlated ASVs.

https://github.com/farhadm1990/Shinny-network-R

This package is compatible with biolecter XT model output which is an excel file with different sheets inside. The function takes directory to the excel file, a working directory for the output files, the number of sheets in the excel file (very important), and a binary (TRUE/FALSE) for the presence of biological (or technical) replicates. The function, then, generates timeseries plots of different filterset values over a range of specific time

NOTE: this package is only tuned for four filtersets, Biomass, pH, Riboflavine, and DO

https://github.com/farhadm1990/biolecter.xt

Unlike 16S rRNA amplicons, shotgun metagenomics targets all DNA present in the sample, e.g. colon. This means your samples will contain DNA from bacteria, host, archeae, and DNA-virum. Therefore, in the first step the host DNA must be removed if it is not of your interest. After decontamination, short reads will be assembled to form Metagnomics Assembled Genomes (MAGs) or contigs. For taxonomic annotations MAGs were binned based on nucleotide identity (NI) threshold and will be blasted against the database. All these steps were done using ATLAS Snakmake workflow and the resultant was analysed as demonstrated in this R markdown.

https://github.com/farhadm1990/Shotgun-metagenomics

We have established an advanced platform to comprehensively study microbial interactions in their spatial and temporal contexts using a confocal ZEISS LSM 900 microscope. This platform allows for the visual exploration of microbial activities and interactions, offering fundamental insights into how microbes influence and adapt to their surroundings on solid matrices. We have also developed various techniques to examine biofilms. Additionally, the microscope is located in a GMO Class 2 facility, enabling work with genetically modified Class 2 pathogens (e.g., tagged with reporter genes) and most Class 3 pathogens.  

Further description and contact person can be found here.

The platform includes an Agilent TapeStation 4200 for DNA/RNA quality assessment,  GridION and Promethion third-generation, real-time sequencing platform, and a Bio-Rad CFX96 Real-Time PCR System. Together, these cutting-edge tools enable precise molecular analysis and high-throughput sequencing for a wide range of applications.

Further description and contact person can be found here.

The anaerobic facility provides a controlled oxygen-free environment for culturing anaerobic microorganisms. It includes specialized chambers, gas mixing system, and monitoring equipment to maintain strict anoxic conditions. This infrastructure supports research on gut microbiota, fermentation, and anaerobic metabolic processes.

Contact: Torben Sølbeck Rasmussen and Dennis Sandris Nielsen

Gut models simulating the human gastrointestinal environment for microbiome and digestion studies. It replicates key physiological conditions, including pH gradients and microbial interactions. They are used to investigate gut health and offer insights into how microorganisms influence digestion and overall health.

Contact: Dennis Sandris Nielsen

We have several biofilm models available in the section to examine microbial communities in various environments. These models allow us to study biofilm formation, structure, and behavior under different conditions, providing insights into microbial interactions, persistence, and resistance. Our advanced setups support research in food safety, health, and industrial applications.

Contact: Henriette Lyng Røder

The fermentation platform includes two solid-state and four submerged fermentation bioreactors, enabling the simulation of a wide range of solid-state and submerged fermentation processes. This versatile setup allows for comprehensive studies and optimization of fermentation conditions across different environments.

Further description and contact person can be found here.

The brewery can facilitate all parts of the brewing process of beer, as well as additional analyses of wort and beer.

Further description can be found here.

The Biolector 2 is a high-throughput, automated microbioreactor for real-time monitoring of microbial cultures. It measures parameters like biomass and pH in parallel microtiter plates. This system enhances bioprocess optimization, metabolic engineering, and fermentation studies.

Contact: Nils Arneborg

The Calscreener is a high-throughput system designed to measure microbial heat production in real-time. It enables precise thermal analysis of microbial metabolism, offering insights into growth dynamics. This tool is valuable for studying fermentation processes, microbial activity, and biotechnological applications in diverse matrices.

Contact: Henriette Lyng Røder and Dennis Sandris Nielsen