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Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

ABSTRACT Next-generation sequencing has enabled metagenomics, the study of the genomes of microorganisms sampled directly from the environment without cultivation. We previously developed a proof-of-concept, scalable metagenome clustering algorithm based on Apache Spark to cluster sequence reads according to their species of origin. To overcome its under-clustering problem on short-read sequences, in this study we developed a new, two-step Label Propagation Algorithm (LPA) that first forms clusters of long reads and then recruits short reads to these clusters. Compared to alternative label propagation strategies, this hybrid clustering algorithm (hybrid-LPA) yields significantly larger read clusters without compromising cluster purity. We show that adding an extra clustering step before assembly leads to improved metagenome assemblies, predicting more complete genomes or gene clusters from a synthetic metagenome dataset and a real-world metagenome dataset, respectively. These results suggest that hybrid-LPA is a good alternative to current metagenome assembly practice by providing benefits in both scalability and accuracy on large metagenome datasets. Availability and implementation https://bitbucket.org/zhong_wang/hybridlpa/src/master/ . Contact zhongwang@lbl.gov

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

ABSTRACT Motivation Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Results Based on a previously developed scalable read clustering method on Apache Spark, SpaRC, that has very low false positives, here we extended its capability by adding a new method to further cluster small clusters. This method exploits statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using a synthetic dataset from mouse gut microbiomes we show that this method has the potential to cluster almost all of the reads from genomes with sufficient sequencing coverage. We also explored several clustering parameters that deferentially affect genomes with various sequencing coverage. Availability https://bitbucket.org/berkeleylab/jgi-sparc/ . Contact zhongwang@lbl.gov

The microstructure, mechanical properties, corrosion behavior, cytocompatibility, and antibacterial properties of biodegradable Zn-3Cu-xMg (x = 0, 0.5, 1 wt.%) alloys with or without pulsed magnetic field treatment during casting were systematically investigated. Mg addition induced the formation of fine Mg2Zn11 precipitated along the matrix grain boundaries. With the increase in Mg content, the precipitation of the Mg2Zn11 phase increased, and the grain size became finer. Pulsed magnetic field treatment exacerbated the occurrence of this phenomenon. Under the combined action of the Mg2Zn11 phase and refined grain size, Zn3Cu0.5Mg alloy with pulsed magnetic field treatment had the best strength–ductility match (σUTS = 181.46 ± 1.06 MPa, δ = 3.95 ± 0.07%), moderate corrosion rate (icorr = 5.69 ± 3.96 μA/cm2), positive cytocompatibility, and antibacterial properties. This study indicated that Zn3Cu0.5Mg alloy with pulsed magnetic field treatment had the greater potential to further improve its properties through subsequent conventional metal-forming processing and severe plastic deformation techniques to meet clinical requirements, compared to existing as-cast Zn alloys. [ABSTRACT FROM AUTHOR]

MOTIVATION:Whole genome shotgun based next-generation transcriptomics and metagenomics studies often generate 100-1000 GB sequence data derived from tens of thousands of different genes or microbial species. Assembly of these data sets requires tradeoffs between scalability and accuracy. Current assembly methods optimized for scalability often sacrifice accuracy and vice versa. An ideal solution would both scale and produce optimal accuracy for individual genes or genomes. RESULTS:Here we describe an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomes and metagenomes from both short and long read sequencing technologies. It achieves near-linear scalability with input data size and number of compute nodes. SpaRC can run on both cloud computing and HPC environments without modification while delivering similar performance. Our results demonstrate that SpaRC provides a scalable solution for clustering billions of reads from next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar large-scale sequence data analysis problems. AVAILABILITY AND IMPLEMENTATION:https://bitbucket.org/berkeleylab/jgi-sparc.

Introduction: This study aims to elucidate the temporal and spatial distribution patterns of sports tourism resources within the Sichuan-Chongqing Urban Agglomeration, examining how these distributions reflect the developmental pathways of urban regions. The theoretical framework posits that the availability and allocation of sports tourism resources are influenced by various geographical and socio-economic factors, with topography, economic conditions, and population density playing pivotal roles in determining their distribution and development. Methods: Utilizing GeoDetector technology alongside indices such as the Geographic Concentration Index and the Disparities Index, we conducted a comprehensive analysis of the spatiotemporal dynamics of sports tourism resources, revealing that the distribution of these resources serves as a key indicator of urban development speed. Results: Our findings indicate that topographical features significantly influence sports tourism resource development differently in Chengdu compared to Chongqing. Furthermore, we identified a strong positive correlation between population density and the distribution of sports tourism resources, highlighting the motivational role of populated areas and emphasizing potential inequalities if attention is focused solely on densely populated regions. Discussion: This research provides novel insights into the complex relationships governing sports tourism resource distribution by employing advanced geo-analytical tools. It offers a strategic roadmap for equitable and sustainable urban development by addressing disparities in resource allocation, ultimately contributing to informed policy-making and enhanced regional resilience. This research aids in the strategic planning and sustainable development of sports tourism, providing a blueprint for urban development in the region that balances the needs of diverse populations and landscapes. [ABSTRACT FROM AUTHOR]

The combination of the Hadoop MapReduce programming model and cloud computing allows biological scientists to analyze next-generation sequencing (NGS) data in a timely and cost-effective manner. Cloud computing platforms remove the burden of IT facility procurement and management from end users and provide ease of access to Hadoop clusters. However, biological scientists are still expected to choose appropriate Hadoop parameters for running their jobs. More importantly, the available Hadoop tuning guidelines are either obsolete or too general to capture the particular characteristics of bioinformatics applications. In this study, we aim to minimize the cloud computing cost spent on bioinformatics data analysis by optimizing the extracted significant Hadoop parameters. When using MapReduce-based bioinformatics tools in the cloud, the default settings often lead to resource underutilization and wasteful expenses. We choose k-mer counting, a representative application used in a large number of NGS data analysis tools, as our study case. Experimental results show that, with the fine-tuned parameters, we achieve a total of 4× speedup compared with the original performance (using the default settings). This paper presents an exemplary case for tuning MapReduce-based bioinformatics applications in the cloud, and documents the key parameters that could lead to significant performance benefits.

BackgroundObesity affects nearly half of adults in the United States and is contributing substantially to a pandemic of obesity-associated chronic conditions such as type 2 diabetes, hypertension, and arthritis. The obesity-associated chronic condition pandemic is particularly severe in low-income, medically underserved, predominantly African-American areas in the southern United States. Little is known regarding the impact of geographic, income, and racial disparities in continuity of care on major health outcomes for patients with obesity-associated chronic conditions. ObjectiveThe aim of this study is to assess, among patients with obesity-associated chronic conditions, and within this group, patients with type 2 diabetes, (1) whether continuity of care is associated with lower overall and potentially preventable emergency department and hospital utilization, (2) the effect of geographic, income, and racial disparities on continuity of care and on health care utilization, (3) whether continuity of care particularly protects individuals at risk for disparities from adverse health outcomes, and (4) whether characteristics of health systems are associated with higher continuity of care and better outcomes. MethodsUsing 2015-2018 data from 4 practice-based research networks participating in the Southern Obesity and Diabetes Coalition, we will conduct a retrospective cohort analysis and distributed meta-analysis. Patients with obesity-associated chronic conditions and with type 2 diabetes will be assessed within each health system, following a standardized study protocol. The primary study outcomes are overall and preventable emergency department visits and hospitalizations. Continuity of care will be calculated at the facility level using a modified version of the Bice-Boxerman continuity of care index. Race will be assessed using electronic medical record data. Residence in a low-income area or a health professional shortage area respectively will be assessed by linking patient residence ZIP codes to the Centers for Medicare & Medicaid Services database. ResultsIn 4 regional health systems across Tennessee, Mississippi, Louisiana, and Arkansas, a total of 53 adult hospitals were included in the study. A total of 147,889 patients with obesity-associated chronic conditions who met study criteria were identified in these health systems, of which 45,453 patients met the type 2 diabetes criteria for inclusion. Results are expected by the end of 2020. ConclusionsThis study should reveal whether health system efforts to increase continuity of care for patients with obesity and diabetes have potential to improve outcomes and reduce costs. Analyzing disparities in continuity of care and their effect on major health outcomes can help demonstrate how to improve care and use of health care resources for vulnerable patients with obesity-associated chronic conditions, and within this group, patients with type 2 diabetes. Better understanding of the association between continuity and health care utilization for these vulnerable populations will contribute to the development of higher-value health systems in the southern United States. International Registered Report Identifier (IRRID)DERR1-10.2196/20788

Genome foundation models hold transformative potential for precision medicine, drug discovery, and understanding complex biological systems. However, existing models are often inefficient, constrained by suboptimal tokenization and architectural design, and biased toward reference genomes, limiting their representation of low-abundance, uncultured microbes in the rare biosphere. To address these challenges, we developed GenomeOcean , a 4-billion-parameter generative genome foundation model trained on over 600 Gbp of high-quality contigs derived from 220 TB of metagenomic datasets collected from diverse habitats across Earth's ecosystems. A key innovation of GenomeOcean is training directly on large-scale co-assemblies of metagenomic samples, enabling enhanced representation of rare microbial species and improving generalizability beyond genome-centric approaches. We implemented a byte-pair encoding (BPE) tokenization strategy for genome sequence generation, alongside architectural optimizations, achieving up to 150× faster sequence generation while maintaining high biological fidelity. GenomeOcean excels in representing microbial species and generating protein-coding genes constrained by evolutionary principles. Additionally, its fine-tuned model demonstrates the ability to discover novel biosynthetic gene clusters (BGCs) in natural genomes and perform zero-shot synthesis of biochemically plausible, complete BGCs. GenomeOcean sets a new benchmark for metagenomic research, natural product discovery, and synthetic biology, offering a robust foundation for advancing these fields., Competing Interests: Conflict of Interests FC was an employee of Illumina Inc. All other authors declare no conflict of interest.

Ischemic stroke is an acute cerebrovascular disease that is one of the leading causes of death and neurological disorders worldwide. Exosomes are a novel class of intercellular signaling regulators containing cell-specific proteins, lipids, and nucleic acids that transmit messages between cells and tissues. MicroRNAs are regulatory non-coding ribonucleic acids that are usually present in exosomes as signaling molecules. Studies have shown that exosomes and exosomal microRNAs can improve the prognosis of ischemic stroke by inhibiting the inflammatory response, reducing apoptosis, improving the imbalance of oxidative and antioxidant systems, and regulating cellular autophagy, among other processes. Previous studies have shown that exercise training can exert neuroprotective effects on ischemic stroke by promoting the release of exosomes and regulating the expression of exosomal microRNAs, which in turn regulate multiple signaling pathways. Exosomes and exosomal microRNAs may be key targets for exercise to promote cerebrovascular health. Therefore, the study of exercise-mediated exosomes and their microRNAs may provide new perspectives for exploring the mechanism of exercise intervention in the prevention and treatment of ischemic stroke., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Thieme. All rights reserved.)

We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e., DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C 2 LR) strategy. Empirical results on 23 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training. Model, codes, and data is publicly available at https://github.com/MAGlCS-LAB/DNABERT_S.