Hardware Acceleration of Genomics Data Analysis: Challenges and Opportunities

Isolation and identification of core-genes in barley roots under low phosphorus stress

BACKGROUND

Low phosphorus stress significantly limits plant growth and agricultural production. Identifying core genes responsive to low-phosphorus stress and breeding new high-phosphorus-efficient crop varieties are crucial for solving practical production problems. Barley is an important crop with genetic diversity and stress tolerance. In this study, we aimed to explore the core genes in barley roots under low-phosphorus stress.

METHODS AND RESULTS

Based on the transcriptome sequencing data of barley root under low phosphorus stress, we used the Weighted Gene Co-expression Network Analysis (WGCNA) method on the high phosphorus-efficient genotype GN121. All expressed genes related to phosphorus content were grouped into 16 co-expression modules. Six highly correlated modules were selected for GO enrichment analysis. Genes in the green module were significantly enriched in "response to stress" and "response to oxidative stress" signaling pathways, while genes in the turquoise module were significantly enriched in "cell response to stimulation" and "cell response to stress" pathways. Through further analysis of these two modules, we identified three core genes: endoglucan-1,3-β-glucosidase 3 (HORVU2Hr1G044440) and peroxidase 5 (HORVU1Hr1G023750 and HORVU1Hr1G016820).

CONCLUSIONS

The identified three core genes above mentioned are involved in the regulation of abiotic stress. These results offer clues for further research on the molecular mechanism of barley's response to low phosphorus stress and genetic resources for breeding high-phosphorus-efficient crop varieties. The findings contribute to understanding how barley adapts to low-phosphorus environments and provide a basis for improving crop phosphorus-use efficiency.

Bioinformatics in Russia: history and present-day landscape

Bioinformatics has become an interdisciplinary subject due to its universal role in molecular biology research. The current status of Russia's bioinformatics research in Russia is not known. Here, we review the history of bioinformatics in Russia, present the current landscape, and highlight future directions and challenges. Bioinformatics research in Russia is driven by four major industries: information technology, pharmaceuticals, biotechnology, and agriculture. Over the past three decades, despite a delayed start, the field has gained momentum, especially in protein and nucleic acid research. Dedicated and shared centers for genomics, proteomics, and bioinformatics are active in different regions of Russia. Present-day bioinformatics in Russia is characterized by research issues related to genetics, metagenomics, OMICs, medical informatics, computational biology, environmental informatics, and structural bioinformatics. Notable developments are in the fields of software (tools, algorithms, and pipelines), use of high computation power (e.g. by the Siberian Supercomputer Center), and large-scale sequencing projects (the sequencing of 100 000 human genomes). Government funding is increasing, policies are being changed, and a National Genomic Information Database is being established. An increased focus on eukaryotic genome sequencing, the development of a common place for developers and researchers to share tools and data, and the use of biological modeling, machine learning, and biostatistics are key areas for future focus. Universities and research institutes have started to implement bioinformatics modules. A critical mass of bioinformaticians is essential to catch up with the global pace in the discipline.

Porting and Optimizing BWA-MEM2 Using the Fujitsu A64FX Processor

Sequence alignment pipelines for human genomes are an emerging workload that will dominate in the precision medicine field. BWA-MEM2 is a tool widely used in the scientific community to perform read mapping studies. In this paper, we port BWA-MEM2 to the AArch64 architecture using the ARMv8-A specification, and we compare the resulting version against an Intel Skylake system both in performance and in energy-to-solution. The porting effort entails numerous code modifications, since BWA-MEM2 implements certain kernels using x86_64 specific intrinsics, e.g., AVX-512. To adapt this code we use the recently introduced Arm's Scalable Vector Extensions (SVE). More specifically, we use Fujitsu's A64FX processor, the first to implement SVE. The A64FX powers the Fugaku Supercomputer that led the Top500 ranking from June 2020 to November 2021. After porting BWA-MEM2 we define and implement a number of optimizations to improve performance in the A64FX target architecture. We show that while the A64FX performance is lower than that of the Skylake system, A64FX delivers 11.6% better energy-to-solution on average. All the code used for this article is available at https://gitlab.bsc.es/rlangari/bwa-a64fx.

Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs

Precision medicine programs to identify clinically relevant genetic variation have been revolutionized by access to increasingly affordable high-throughput sequencing technologies. A decade of continual drops in per-base sequencing costs means it is now feasible to sequence an individual patient genome and interrogate all classes of genetic variation for < $1,000 USD. However, while advances in these technologies have greatly simplified the ability to obtain patient sequence information, the timely analysis and interpretation of variant information remains a challenge for the rollout of large-scale precision medicine programs. This review will examine the challenges and potential solutions that exist in identifying predictive genetic biomarkers and pharmacogenetic variants in a patient and discuss the larger bioinformatic challenges likely to emerge in the future. It will examine how both software and hardware development are aiming to overcome issues in short read mapping, variant detection and variant interpretation. It will discuss the current state of the art for genetic disease and the remaining challenges to overcome for complex disease. Success across all types of disease will require novel statistical models and software in order to ensure precision medicine programs realize their full potential now and into the future.