Terabase-Scale Coassembly of a Tropical Soil Microbiome.

Petabases of environmental metagenomic data are publicly available, presenting an opportunity to characterize complex environments and discover novel lineages of life. Metagenome coassembly, in which many metagenomic samples from an environment are simultaneously analyzed to infer the underlying genomes' sequences, is an essential tool for achieving this goal. We applied MetaHipMer2, a distributed metagenome assembler that runs on supercomputing clusters, to coassemble 3.4 terabases (Tbp) of metagenome data from a tropical soil in the Luquillo Experimental Forest (LEF), Puerto Rico. The resulting coassembly yielded 39 high-quality (>90% complete, <5% contaminated, with predicted 23S, 16S, and 5S rRNA genes and ≥18 tRNAs) metagenome-assembled genomes (MAGs), including two from the candidate phylum Eremiobacterota. Another 268 medium-quality (≥50% complete, <10% contaminated) MAGs were extracted, including the candidate phyla Dependentiae, Dormibacterota, and Methylomirabilota. In total, 307 medium- or higher-quality MAGs were assigned to 23 phyla, compared to 294 MAGs assigned to nine phyla in the same samples individually assembled. The low-quality (<50% complete, <10% contaminated) MAGs from the coassembly revealed a 49% complete rare biosphere microbe from the candidate phylum FCPU426 among other low-abundance microbes, an 81% complete fungal genome from the phylum Ascomycota, and 30 partial eukaryotic MAGs with ≥10% completeness, possibly representing protist lineages. A total of 22,254 viruses, many of them low abundance, were identified. Estimation of metagenome coverage and diversity indicates that we may have characterized ≥87.5% of the sequence diversity in this humid tropical soil and indicates the value of future terabase-scale sequencing and coassembly of complex environments. IMPORTANCE Petabases of reads are being produced by environmental metagenome sequencing. An essential step in analyzing these data is metagenome assembly, the computational reconstruction of genome sequences from microbial communities. "Coassembly" of metagenomic sequence data, in which multiple samples are assembled together, enables more complete detection of microbial genomes in an environment than "multiassembly," in which samples are assembled individually. To demonstrate the potential for coassembling terabases of metagenome data to drive biological discovery, we applied MetaHipMer2, a distributed metagenome assembler that runs on supercomputing clusters, to coassemble 3.4 Tbp of reads from a humid tropical soil environment. The resulting coassembly, its functional annotation, and analysis are presented here. The coassembly yielded more, and phylogenetically more diverse, microbial, eukaryotic, and viral genomes than the multiassembly of the same data. Our resource may facilitate the discovery of novel microbial biology in tropical soils and demonstrates the value of terabase-scale metagenome sequencing.

Critical Assessment of Metagenome Interpretation: the second round of challenges.

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.

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection.

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

ADEPT: a domain independent sequence alignment strategy for gpu architectures.

BACKGROUND: Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. RESULTS: In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods for each domain. ADEPT's driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. CONCLUSIONS: ADEPT demonstrates a performance that is either comparable or better than existing GPU strategies. We demonstrated the efficacy of ADEPT in supporting existing bionformatics software pipelines by integrating ADEPT in MetaHipMer a high-performance denovo metagenome assembler and PASTIS a high-performance protein similarity graph construction pipeline. Our results show 10% and 30% boost of performance in MetaHipMer and PASTIS respectively.

Terabase-scale metagenome coassembly with MetaHipMer.

Metagenome sequence datasets can contain terabytes of reads, too many to be coassembled together on a single shared-memory computer; consequently, they have only been assembled sample by sample (multiassembly) and combining the results is challenging. We can now perform coassembly of the largest datasets using MetaHipMer, a metagenome assembler designed to run on supercomputers and large clusters of compute nodes. We have reported on the implementation of MetaHipMer previously; in this paper we focus on analyzing the impact of very large coassembly. In particular, we show that coassembly recovers a larger genome fraction than multiassembly and enables the discovery of more complete genomes, with lower error rates, whereas multiassembly recovers more dominant strain variation. Being able to coassemble a large dataset does not preclude one from multiassembly; rather, having a fast, scalable metagenome assembler enables a user to more easily perform coassembly and multiassembly, and assemble both abundant, high strain variation genomes, and low-abundance, rare genomes. We present several assemblies of terabyte datasets that could never be coassembled before, demonstrating MetaHipMer's scaling power. MetaHipMer is available for public use under an open source license and all datasets used in the paper are available for public download.

The parallelism motifs of genomic data analysis.

Genomic datasets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share these data with the research community, but some of these genomic data analysis problems require large-scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high-end parallel systems today and place different requirements on programming support, software libraries and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high-performance genomics analysis, including alignment, profiling, clustering and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or 'motifs' that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.