The aim of this interdisciplinary seminar is to obtain an overview and understanding of the current state of the art in microbiome research. On the one hand, we will look at some biological problems that are currently being studied. On the other hand, we will survey the bioinformatics approaches that are being used to help solve these questions.
In the first session of the seminar, we will provide an overview over the topics to be discussed in the seminar and then the topics will be assigned to the participants. Each participant will be provided with one or more papers on their topic. In addition, participants are expected to also survey the literature so as to identify additional papers that are useful for their topic.
Each participant will give a 25 minute oral presentation, followed by questions and discussion, and then feedback. Sessions will be chaired by a participant of the seminar. In addition, each participant will provide a write-up of their topic (~12 pages) in which they present the content of their presentation in their own words.
Grading will be based on the quality of the oral presentation. The quality of the writeup and the level of participation in discussions will used to push the grade up or down. Participation in all sessions is mandatory.
To ensure a high quality of presentations and write-ups, each participant must discuss their presentation with their supervisor at least one week before the scheduled presentation.
|26-April||Overview and topic selection|
Genetic determinants of the gut microbiome in UK Twins https://pubmed.ncbi.nlm.nih.gov/27173935/
Decoding the language of microbiomes using word-embedding techniques, and applications in inflammatory bowel disease https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007859
DeepMicrobes: taxonomic classification for metagenomics with deep learning https://academic.oup.com/nargab/article/2/1/lqaa009/5740226
Species abundance information improves sequence taxonomy classification accuracy https://www.nature.com/articles/s41467-019-12669-6
CoCoNet: An Efficient Deep Learning Tool for Viral Metagenome Binning https://doi.org/10.1093/bioinformatics/btab213
Strain-level metagenomic assignment and compositional estimation for long reads with MetaMaps https://www.nature.com/articles/s41467-019-10934-2
Systematic detection of horizontal gene transfer across genera among multidrug-resistant bacteria in a single hospital https://elifesciences.org/articles/53886
DeepNOG: fast and accurate protein orthologous group assignment https://academic.oup.com/bioinformatics/article/36/22-23/5304/6050698
PStrain: an iterative microbial strains profiling algorithm for shotgun metagenomic sequencing data https://academic.oup.com/bioinformatics/article/36/22-23/5499/6042705
Identity: rapid alignment-free prediction of sequence alignment identity scores using self-supervised general linear models https://academic.oup.com/nargab/article/3/1/lqab001/6125549?login=true
Gut microbiome, big data and machine learning to promote precision medicine for cancer https://www.nature.com/articles/s41575-020-0327-3
- LEMMI: a continuous benchmarking platform for metagenomics classifiers https://genome.cshlp.org/content/30/8/1208.full
- HiCBin: Binning metagenomic contigs and recovering metagenome-assembled genomes using Hi-C contact maps https://www.biorxiv.org/content/10.1101/2021.01.28.428549v3
- Viral quasispecies assembly https://genome.cshlp.org/content/27/5/835.ful, https://academic.oup.com/bioinformatics/article abstract/35/24/5086/5506652
- Balrog: A universal protein model for prokaryotic gene prediction https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008727&rev=2
- A unified catalog of 204,938 reference genomes from the human gut microbiome https://www.nature.com/articles/s41587-020-0603-3
- Associations of fecal microbial profiles with breast cancer and nonmalignant breast disease in the Ghana Breast Health Study https://onlinelibrary.wiley.com/doi/10.1002/ijc.33473
- Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9319639
- TADA: phylogenetic augmentation of microbiome samples enhances phenotype classification, https://academic.oup.com/bioinformatics/article/35/14/i31/5529256
- Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks, https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006693
- IDMIL: an alignment-free Interpretable Deep Multiple Instance Learning (MIL) for predicting disease from whole-metagenomic data https://academic.oup.com/bioinformatics/article/36/Supplement_1/i39/5870478
- Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences https://www.nature.com/articles/s41598-021-83922-6