Adrian Fritz

PGRanker: Enhancing Phenotype Knowledge with Graph Learning for Mendelian Gene Prioritization

PGRanker improves Mendelian gene prioritization by integrating phenotype data with gene networks using graph learning, achieving higher recall and better ranking of causal genes.

Critical Assessment of Metagenome Interpretation - the second round of challenges

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

Haploflow: Strain-resolved de novo assembly of viral genomes

Haploflow assembles viral strain genomes from mixed samples, outperforming existing assemblers in speed and accuracy on benchmark datasets.

Tutorial: Assessing metagenomics software with the CAMI benchmarking toolkit

This tutorial explains how to use the CAMI benchmarking toolkit to evaluate metagenomics software performance using standardized datasets and metrics.

Evaluating assembly and variant calling software for strain-resolved analysis of large DNA-viruses

This study benchmarks assemblers and variant callers for strain-resolved analysis of large DNA viruses, identifying best practices and challenges in handling low-divergent viral populations.

CAMITAX: Taxon labels for microbial genomes

CAMITAX improves taxonomic labeling of microbial genomes using an ensemble method combining multiple approaches, outperforming individual methods across taxonomic ranks.

CAMISIM: Simulating metagenomes and microbial communities

CAMISIM simulates microbial communities and metagenomes with truth standards, enabling benchmarking of metagenomic analysis tools.

AMBER: Assessment of Metagenome BinnERs

AMBER evaluates genome binning tools using benchmark datasets and provides performance metrics and visualizations.

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

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