Peter Meinicke

CoCoPyE improves the prediction of genome quality indices by using a two-stage feature extraction and machine learning approach, outperforming existing tools in simulations.

BinChecker assesses microbial draft genome quality using adaptive clustering and protein domain analysis, achieving accuracy comparable to existing methods with faster performance.

The paper introduces BiG-MEx, an ultrafast tool for mining metagenomic data to identify and analyze biosynthetic gene clusters, demonstrating its effectiveness on TARA Oceans and Human Microbiome Project datasets.

Tax4Fun2 predicts habitat-specific functional profiles and redundancy from 16S rRNA gene sequences using user-defined genomic information.

This paper introduces a normalization method for metatranscriptomic data that accounts for taxonomic composition differences to improve differential expression analysis.

The paper benchmarks computational metagenomics software using complex datasets, showing strengths and limitations in assembly, binning, and taxonomic profiling.