Enabling large-scale next-generation sequence assembly with Blacklight

Persistent memory as an effective alternative to random access memory in metagenome assembly

BACKGROUND

The assembly of metagenomes decomposes members of complex microbe communities and allows the characterization of these genomes without laborious cultivation or single-cell metagenomics. Metagenome assembly is a process that is memory intensive and time consuming. Multi-terabyte sequences can become too large to be assembled on a single computer node, and there is no reliable method to predict the memory requirement due to data-specific memory consumption pattern. Currently, out-of-memory (OOM) is one of the most prevalent factors that causes metagenome assembly failures.

RESULTS

In this study, we explored the possibility of using Persistent Memory (PMem) as a less expensive substitute for dynamic random access memory (DRAM) to reduce OOM and increase the scalability of metagenome assemblers. We evaluated the execution time and memory usage of three popular metagenome assemblers (MetaSPAdes, MEGAHIT, and MetaHipMer2) in datasets up to one terabase. We found that PMem can enable metagenome assemblers on terabyte-sized datasets by partially or fully substituting DRAM. Depending on the configured DRAM/PMEM ratio, running metagenome assemblies with PMem can achieve a similar speed as DRAM, while in the worst case it showed a roughly two-fold slowdown. In addition, different assemblers displayed distinct memory/speed trade-offs in the same hardware/software environment.

CONCLUSIONS

We demonstrated that PMem is capable of expanding the capacity of DRAM to allow larger metagenome assembly with a potential tradeoff in speed. Because PMem can be used directly without any application-specific code modification, these findings are likely to be generalized to other memory-intensive bioinformatics applications.

Transcriptomic signatures of cold adaptation and heat stress in the winter ant (Prenolepis imparis)

Climate change is a serious threat to biodiversity; it is therefore important to understand how animals will react to this stress. Ectotherms, such as ants, are especially sensitive to the climate as the environmental temperature influences myriad aspects of their biology, from optimal foraging time to developmental rate. In this study, we conducted an RNA-seq analysis to identify stress-induced genes in the winter ant (Prenolepis imparis). We quantified gene expression during heat and cold stress relative to a control temperature. From each of our conditions, we sequenced the transcriptome of three individuals. Our de novo assembly included 13,324 contigs that were annotated against the nr and SwissProt databases. We performed gene ontology and enrichment analyses to gain insight into the physiological processes involved in the stress response. We identified a total of 643 differentially expressed genes across both treatments. Of these, only seven genes were differentially expressed in the cold-stressed ants, which could indicate that the temperature we chose for trials did not induce a strong stress response, perhaps due to the cold adaptations of this species. Conversely, we found a strong response to heat: 426 upregulated genes and 210 downregulated genes. Of these, ten were expressed at a greater than ten-fold change relative to the control. The transcripts we could identify included those encoding for protein folding genes, heat shock proteins, histones, and Ca2+ ion transport. One of these transcripts, hsc70-4L was found to be under positive selection. We also characterized the functional categories of differentially expressed genes. These candidate genes may be functionally conserved and relevant for related species that will deal with rapid climate change.

Computational Strategies for Scalable Genomics Analysis

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.