Bertil Schmidt

QutRNA2: Robust tRNA modification discovery from Nanopore direct tRNA sequencing

QutRNA2 improves tRNA modification detection using Nanopore sequencing with GPU acceleration, achieving faster performance and identifying modifications in human and mouse tRNAs.

Accelign: a GPU-based Library for Accelerating Pairwise Sequence Alignment

Accelign accelerates pairwise sequence alignment using GPUs, achieving significant speedups over existing CPU and GPU implementations for DNA, RNA, and protein sequences.

Efficient protein structure prediction fromcompact computers to datacenters withOpenFold-TRT

The paper accelerates protein structure prediction using OpenFold with TensorRT and MMseqs2-GPU, achieving 131x faster inference than AlphaFold2 on specific hardware.

RabbitVar: ultra-fast and accurate somatic small-variant calling on multi-core architectures

RabbitVar is a somatic variant caller that achieves high accuracy and efficiency for small variants in cancer genomics, outperforming existing tools on real-world datasets.

Improved Metagenomic Analysis for All-Food-Sequencing with AFS-MetaCache2: Illumina vs. Nanopore

This paper improves metagenomic analysis for food sequencing using AFS-MetaCache2, showing long-read sequencing and the new tool outperform existing methods in accuracy and efficiency.

GPU-accelerated homology search with MMseqs2

The paper introduces GPU-accelerated homology search with MMseqs2, achieving faster sequence similarity detection than CPU methods and improving structure prediction speeds.

CUDASW++4.0: Ultra-fast GPU-based Smith-Waterman Protein Sequence Database Search

CUDASW++4.0 improves protein sequence database search speed using GPU-based Smith-Waterman alignment with performance gains over previous methods and CPU tools.

RabbitTClust: enabling fast clustering analysis of millions bacteria genomes with MinHash sketches

RabbitTClust clusters large bacterial genome datasets using MinHash sketches, achieving fast processing times for millions of genomes.

Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale

This paper demonstrates that locality-sensitive hashing can classify signals in high-throughput mass spectrometry data efficiently and scalably.

Identification and quantification of meat product ingredients by whole-genome metagenomics (All-Food-Seq)

This paper evaluates All-Food-Seq (AFS) for detecting and quantifying meat ingredients in food samples, showing it outperforms qPCR and matches ddPCR in accuracy.