This paper proposes FastAlign, a faster, cheaper, and practical end-to-end solution for sequence alignment using commercial CPUs. It introduces two key innovations: a multi-stage seeding algorithm that improves search performance while maintaining low memory consumption, and an intra-query parallel seed-extension algorithm that eliminates redundancy and increases SIMD utilization. Evaluation results show that FastAlign achieves 2.27× ∼ 3.28× throughput speedup and 2.54× ∼ 5.65× cost reduction compared to state-of-the-art CPU and GPU baselines while guaranteeing 100% identical output to the de facto software BWA-MEM. FastAlign is open-sourced at https://github.com/zzhofict/BWA-FastAlign.git.

Sequence alignment is a fundamental and often time-consuming step in genomic data analysis. Typically, it adheres to the seed-and-extension paradigm and numerous accelerator-based approaches have been proposed to optimize either of the kernels. However, these approaches often increase costs and contribute minimally to the overall alignment process. To address this, we have designed an optimized full pipeline, FastBWA, which seeks to enhance performance while keeping costs low and explores the potential of CPU computing resources. Our implementation demonstrates that FastBWA achieves up to 2.5× and 1.8× in end-to-end alignment throughput compared to BWA-MEM and its newer version, BWA-MEM2.

SUMMARY: Duplicate marking is a critical preprocessing step in gene sequence analysis to flag redundant reads arising from polymerase chain reaction amplification and sequencing artifacts. Although Picard MarkDuplicates is widely recognized as the gold-standard tool, its single-threaded implementation and reliance on global sorting result in significant computational and resource overhead, limiting its efficiency on large-scale datasets. Here, we introduce FastDup: a high-performance, scalable solution that follows the speculation-and-test mechanism. FastDup achieves up to 20× throughput speedup with 32 threads and guarantees 100% identical output compared to Picard MarkDuplicates. AVAILABILITY AND IMPLEMENTATION: FastDup is a C++ program available from Zenodo https://zenodo.org/records/15727829, Bioconda https://anaconda.org/bioconda/fastdup and GitHub https://github.com/zzhofict/FastDup.git under the MIT license.

This paper presents a drug development platform based on virtual screening technology. The platform integrates key components such as pocket prediction, molecular docking, molecular dynamics simulation, and ADMET evaluation to achieve an efficient and automated drug virtual screening process. The platform utilizes Docker for modular encapsulation, ensuring environment isolation and convenient deployment. It also provides standardized input-output formats and a task allocation system, enabling users to quickly deploy and customize the workflow. Experimental results demonstrate the effectiveness of the platform in identifying real drugs and evaluating virtual screening results, providing an efficient and reliable solution for drug development. The platform features easy deployment and migration, independent module execution, automated workflow implementation, personalized customization and replacement, task allocation for computationally intensive steps, and complex operations in molecular dynamics simulation.

High-throughput next-generation sequencing technologies are producing a flood of cheap genomic information, providing precision medicine with the opportunity to better understand the primary cause of complicated diseases like cancer. However, even current state-of-the-art approaches still have large gaps with data generation due to limited scalability, accuracy and computational efficiency. To explore how to efficiently and effectively synthesize genomic data into knowledge, we propose GATK-Spark, a balanced parallelization approach that implements an in-memory version of GATK using Apache Spark. First, we performed a rigorous analysis of current GATK optimization strategies. We identify that compute resource utilization, text-based data format and long time single-thread file cutting and mergence operations are three major scalable bottlenecks. Second, we share our experiences designing a new approach optimized for GATK with big-data computing frameworks Apache Spark - GATK-Spark, which reduces the original execution of 20 hours to 30 minutes with a speedup in excess of 37 at 256 CPU cores. This work will facilitate the understanding of genomics analytics pipeline and design of strategies for accelerating large scale genomic analysis applications.