Georg Zeller

TL;DR: A machine learning method is developed, designated margin-based prediction of polymorphic regions (mPPR), to predict PRs from resequencing array data, which provides a fine-scale view of polymorphIC sequences in A. thaliana and can be applied to other segmentation tasks related to the analysis of genomic variation.

TL;DR: This study uses metagenomic assembly followed by a reference-based binning strategy to screen over 6500 gut metagenomes spanning geography and lifestyle and reconstruct over 1300 E. rectale high-quality genomes from metagenome, providing new insights into the population structure and ecology of E.rectale and shows that shotgun metagenomics can enable population genomics studies of microbiota members at a resolution and scale previously attainable only by extensive isolate sequencing.

TL;DR: It is felt that in a comparison of different approaches, it is important to minimize the potential confounding sources by ensuring equal treatment of all methods under study, and conceptually simpler methods, such as relative-abundance normalization, should not be dismissed on these grounds.

TL;DR: Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages.

TL;DR: The presented proGenomes2 provides threefold more genomes, enhanced habitat annotations, updated taxonomic and functional annotation and improved linkage to the NCBI BioSample database.