Biology subjects

Kallenborn, F.

Publications and source records attributed to Kallenborn, F..

6 recordsLinked to original sources

bioRxiv2026

Efficient protein structure prediction fromcompact computers to datacenters withOpenFold-TRT

We introduce accelerations for deep learning inference with OpenFold and TensorRT that, combined with MMseqs2-GPU on an x86 system with one NVIDIA RTX PRO 6000 Blackwell Server Edition GPU, reach up to 131x faster inference compared to AlphaFold2. ARM-optimizations enable homology search on power efficient DGX Spark, and allow search beyond available GPU RAM on NVIDIA Grace Hopper Superchip at comparable overall folding speed. These accelerations enable high-throughput protein structure inference at no accuracy cost using familiar tools.

bioinformatics↗

bioRxiv2025

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

BackgroundAll-Food-Sequencing (AFS) is a method for untargeted metagenomic analysis that allows for the detection and quantification of food ingredients. While this approach avoids some of the shortcomings of targeted PCR-based methods, its performance depends on sequencing technologies, taxonomic classification tools, and genomic reference databases. ResultsAFS-MetaCache2 implements an improved reference database construction mechanism compared to prior approaches. To demonstrate the effectiveness to AFS, we sequenced sausages composed of mammalian and avian species using both short-read (Illumina) and long-read (Oxford Nanopore Technologies) platforms. While both approaches reliably detect the main components, our comparison shows that long-read sequencing is superior in terms of both quantification accuracy and false positive rates. The evaluation of representative metagenomic tools (Kraken2+Bracken, KrakenUniq, AFS-MetaCache1) demonstrates that AFS-MetaCache2 yields the best accuracy and fastest database build times, while reducing peak main memory consumption. It thus allows for efficient scaling to large reference genome sets. ConclusionOur study suggests that deep sequencing of total genomic DNA from samples with heterogeneous taxon composition, using 3rd generation sequencing technology followed by metagenomic analysis with AFS-MetaCache2, is a valuable approach for bio-surveillance of food ingredients. Our software is available at https://github.com/muellan/metacache.

bioinformatics↗

bioRxiv2025

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

BackgroundThe continually increasing volume of sequence data results in a growing demand for fast implementations of core algorithms. Computation of pairwise alignments based on dynamic programming is an important part in many bioinformatics pipelines and a major contributor to overall runtime due to the associated quadratic time complexity. This motivates the need for a library of efficient implementations on modern GPUs for a variety of alignment algorithms for different types of sequence data including DNA, RNA, and proteins. ResultsAccelign is a library of accelerated pairwise sequence alignment algorithms for CUDA-enabled GPUs. Its parallelization strategy is based on a common wavefront design that can be adapted to support a variety of dynamic programming algorithms: local, global, and semi-global alignment of genomic and protein sequences with a variety of commonly used scoring schemes supporting one-to-one, one-to-many or all-to-all pairwise sequence alignments. This leads to a peak performance between 16.1 TCUPS and 9.1 TCUPS for computing optimal global alignment scores with linear gaps and affine gap penalties on a single RTX PRO 6000 Blackwell GPU, respectively. In addition, our library demonstrates significant speedups in several real-world case studies over prior CPU-based (SeqAn, Parasail, BSalign) and GPU-based libraries (ADEPT, GASAL2), and can even outperform highly customized algorithms (WFA-GPU, CUDASW++4.0). Furthermore, the performance of our approach scales linearly with the number of employed GPUs, which makes it feasible to exploit multi-GPU nodes for increased processing speeds. ConclusionAccelign provides significant speedups for commonly used pairwise alignment algorithms compared to prior implementations. It is freely available at https://github.com/fkallen/Accelign.

bioinformatics↗

bioRxiv2025

QutRNA2: Robust tRNA modification discovery from Nanopore direct tRNA sequencing

Transfer RNAs (tRNAs) are essential for protein synthesis and are extensively modified to ensure their structure and function. Direct RNA sequencing with Oxford Nanopore Technologies enables positional modification analysis but is challenged by tRNAs short length, redundancy, and dense modifications. We present QutRNA2, a scalable workflow featuring GPU-accelerated local alignment, statistical filtering, pairwise error profile comparison, and customizable visualization. Achieving up to 25-fold speed gains over CPU methods, QutRNA2 identifies enzyme-dependent modifications in nuclear- and mitochondrial-encoded tRNAs, demonstrated in human and mouse samples. This open-source solution provides a comprehensive, multiplexing-compatible framework for tRNA analysis, addressing a key gap in current tools.

bioinformatics↗

bioRxiv2024

GPU-accelerated homology search with MMseqs2

Rapidly growing protein databases demand faster sensitive sequence similarity detection. We present GPU-accelerated search utilizing intra-query parallelization delivering 6x faster single-protein searches compared to state-of-the-art CPU methods on 2x64 cores--speeds previously requiring large protein batches. It is most cost effective, including in large-batches at 0.45x MMseqs2-CPU speed (8 GPUs delivering 2.4x). It accelerates ColabFold structure prediction 31.8x compared to AlphaFold2 and Foldseek search 4-27x. MMseqs2-GPU is open-source at mmseqs.com.

bioinformatics↗

bioRxiv2023

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

BackgroundThe maximal sensitivity for local pairwise alignment makes the Smith-Waterman algorithm a popular choice for protein sequence database search. However, its quadratic time complexity makes it compute-intensive. Unfortunately, current state-of-the-art software tools are not able to leverage the massively parallel processing capabilities of modern GPUs with close-to-peak performance. This motivates the need for more efficient implementations. ResultsCUDASW++4.0 is a fast software tool for scanning protein sequence databases with the Smith-Waterman algorithm on CUDA-enabled GPUs. Our approach achieves high efficiency for dynamic programming-based alignment computation by minimizing memory accesses and instructions. We provide both efficient matrix tiling, and sequence database partitioning schemes, and exploit next generation floating point arithmetic and novel DPX instructions. This leads to close-to-peak performance on modern GPU generations (Ampere, Ada, Hopper) with throughput rates of up to 1.94 TCUPS, 5.01 TCUPS, 5.71 TCUPS on an A100, L40S, and H100, respectively. Evaluation on the Swiss-Prot, UniRef50, and TrEMBL databases shows that CUDASW++4.0 gains over an order-of-magnitude performance improvements over previous GPU-based approaches (CUDASW++3.0, ADEPT). In addition, our algorithm demonstrates significant speedups over top-performing CPU-based tools (BLASTP, SWIPE, SWIMM2.0), can exploit multi-GPU nodes with linear scaling, and features an impressive energy efficiency of up to 15.7 GCUPS/Watt. ConclusionCUDASW++4.0 changes the standing of GPUs in protein sequence database search with Smith-Waterman alignment by providing close-to-peak performance on modern GPUs. It is freely available at https://github.com/asbschmidt/CUDASW4.

bioinformatics↗