Extreme Scale De Novo Metagenome Assembly

Metagenome assembly is the process of transforming a set of short, overlapping, and potentially erroneous DNA segments from environmental samples into the accurate representation of the underlying microbiomes's genomes. State-of-the-art tools require big shared memory machines and cannot handle contemporary metagenome datasets that exceed Terabytes in size. In this paper, we introduce the MetaHipMer pipeline, a high-quality and high-performance metagenome assembler that employs an iterative de Bruijn graph approach. MetaHipMer leverages a specialized scaffolding algorithm that produces long scaffolds and accommodates the idiosyncrasies of metagenomes. MetaHipMer is end-to-end parallelized using the Unified Parallel C language and therefore can run seamlessly on shared and distributed-memory systems. Experimental results show that MetaHipMer matches or outperforms the state-of-the-art tools in terms of accuracy. Moreover, MetaHipMer scales efficiently to large concurrencies and is able to assemble previously intractable grand challenge metagenomes. We demonstrate the unprecedented capability of MetaHipMer by computing the first full assembly of the Twitchell Wetlands dataset, consisting of 7.5 billion reads - size 2.6 TBytes.Comment: Accepted to SC1

Genome2D: a visualization tool for the rapid analysis of bacterial transcriptome data

Genome2D is a Windows-based software tool for visualization of bacterial transcriptome and customized datasets on linear chromosome maps constructed from annotated genome sequences. Genome2D facilitates the analysis of transcriptome data by using different color ranges to depict differences in gene-expression levels on a genome map. Such output format enables visual inspection of the transcriptome data, and will quickly reveal transcriptional units, without prior knowledge of expression level cutoff values. The compiled version of Genome2D is freely available for academic or non-profit use from

Inference, Orthology, and Inundation: Addressing Current Challenges in the Field of Metagenomics

The vast increase in the number of sequenced genomes has irreversibly changed the landscape of the biological sciences and has spawned the current post-genomic era of research. Genomic data have illuminated many adaptation and survival strategies between species and their habitats. Moreover, the analysis of prokaryotic genomic sequences is indispensible for understanding the mechanisms of bacterial pathogens and for subsequently developing effective diagnostics, drugs, and vaccines. Computational strategies for the annotation of genomic sequences are driven by the inference of function from reference genomes. However, the effectiveness of such methods is bounded by the fractional diversity of known genomes. Although metagenomes can reconcile this limitation by offering access to previously intangible organisms, harnessing metagenomic data comes with its own collection of challenges. Since the sequenced environmental fragments of metagenomes do not equate to discrete and fully intact genomes, this prevents the conventional establishment of orthologous relationships that are required for functional inference. Furthermore, the current surge in metagenomic data sets requires the development of compression strategies that can effectively accommodate large data sets that are comprised of multiple sequences and a greater proportion of auxiliary data, such as sequence headers. While modern hardware can provide vast amounts of inexpensive storage for biological databases, the compression of nucleotide sequence data is still of paramount importance in order to facilitate fast search and retrieval operations through a reduction in disk traffic. To address the issues of inference and orthology a novel protocol was developed for the prediction of functional interactions that supports data sources that lack information about orthologous relationships. To address the issue of database inundation, a compression protocol was designed that can differentiate between sequence data and auxiliary data, thereby offering reconciliation between sequence specific and general-purpose compression strategies. By resolving these and other challenges, it becomes possible to extend the potential utility of the emerging field of metagenomics