Coriolis: enabling metagenomic classification on lightweight mobile devices.

MOTIVATION: The introduction of portable DNA sequencers such as the Oxford Nanopore Technologies MinION has enabled real-time and in the field DNA sequencing. However, in the field sequencing is actionable only when coupled with in the field DNA classification. This poses new challenges for metagenomic software since mobile deployments are typically in remote locations with limited network connectivity and without access to capable computing devices. RESULTS: We propose new strategies to enable in the field metagenomic classification on mobile devices. We first introduce a programming model for expressing metagenomic classifiers that decomposes the classification process into well-defined and manageable abstractions. The model simplifies resource management in mobile setups and enables rapid prototyping of classification algorithms. Next, we introduce the compact string B-tree, a practical data structure for indexing text in external storage, and we demonstrate its viability as a strategy to deploy massive DNA databases on memory-constrained devices. Finally, we combine both solutions into Coriolis, a metagenomic classifier designed specifically to operate on lightweight mobile devices. Through experiments with actual MinION metagenomic reads and a portable supercomputer-on-a-chip, we show that compared with the state-of-the-art solutions Coriolis offers higher throughput and lower resource consumption without sacrificing quality of classification. AVAILABILITY AND IMPLEMENTATION: Source code and test data are available from http://score-group.org/?id=smarten.

Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes.

Identification of patient subtypes from retrospective Electronic Health Record (EHR) data is fraught with inherent modeling issues, such as missing data and variable length time intervals, and the results obtained are highly dependent on data pre-processing strategies. As we move towards personalized medicine, assessing accurate patient subtypes will be a key factor in creating patient specific treatment plans. Partitioning longitudinal trajectories from irregularly spaced and variable length time intervals is a well-established, but open problem. In this work, we present and compare k-means approaches for subtyping opioid use trajectories from EHR data. We then interpret the resulting subtypes using decision trees, examining how each subtype is influenced by opioid medication features and patient diagnoses, procedures, and demographics. Finally, we discuss how the subtypes can be incorporated in static machine learning models as features in predicting opioid overdose and adverse events. The proposed methods are general, and can be extended to other EHR prescription dosage trajectories.

Reverse engineering and analysis of large genome-scale gene networks.

Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web.

Identifying Taxonomic Units in Metagenomic DNA Streams on Mobile Devices.

With the emergence of portable DNA sequencers, such as Oxford Nanopore Technology MinION, metagenomic DNA sequencing can be performed in real-time and directly in the field. However, because metagenomic DNA analysis tasks, e.g., classification, taxonomic units assignment, etc., are compute and memory intensive, and the available methods are designed for batch processing, the current metagenomic tools are not well suited for mobile devices. In this work, we propose a new memory-efficient approach to identify Operational Taxonomic Units (OTUs) in metagenomic DNA streams on mobile devices. Our method is based on finding connected components in overlap graphs constructed over a real-time stream of long DNA reads as produced by the MinION platform. We propose an efficient algorithm to maintain connected components when an overlap graph is streamed and show how redundant information can be removed from the stream by transitive closures. We also propose how our algorithms can be integrated into a larger DNA analysis pipeline tailored for mobile computing. Through experiments on simulated and real-world metagenomic data, executed on the actual mobile device, we demonstrate that our resulting solution is able to recover OTUs with high precision. Our experiments also demonstrate the compounding benefits of introducing feedback loops in the DNA analysis pipeline.

A brassinosteroid transcriptional network revealed by genome-wide identification of BESI target genes in Arabidopsis thaliana.

Brassinosteroids (BRs) are important regulators for plant growth and development. BRs signal to control the activities of the BES1 and BZR1 family transcription factors. The transcriptional network through which BES1 and BZR regulate large number of target genes is mostly unknown. By combining chromatin immunoprecipitation coupled with Arabidopsis tiling arrays (ChIP-chip) and gene expression studies, we have identified 1609 putative BES1 target genes, 404 of which are regulated by BRs and/or in gain-of-function bes1-D mutant. BES1 targets contribute to BR responses and interactions with other hormonal or light signaling pathways. Computational modeling of gene expression data using Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) reveals that BES1-targeted transcriptional factors form a gene regulatory network (GRN). Mutants of many genes in the network displayed defects in BR responses. Moreover, we found that BES1 functions to inhibit chloroplast development by repressing the expression of GLK1 and GLK2 transcription factors, confirming a hypothesis generated from the GRN. Our results thus provide a global view of BR regulated gene expression and a GRN that guides future studies in understanding BR-regulated plant growth.

Large-scale metagenomic sequence clustering on map-reduce clusters.

Taxonomic clustering of species from millions of DNA fragments sequenced from their genomes is an important and frequently arising problem in metagenomics. In this paper, we present a parallel algorithm for taxonomic clustering of large metagenomic samples with support for overlapping clusters. We develop sketching techniques, akin to those created for web document clustering, to deduce significant similarities between pairs of sequences without resorting to expensive all vs. all comparison. We formulate the metagenomic classification problem as that of maximal quasi-clique enumeration in the resulting similarity graph, at multiple levels of the hierarchy as prescribed by different similarity thresholds. We cast execution of the underlying algorithmic steps as applications of the map-reduce framework to achieve a cloud ready implementation. We show that the resulting framework can produce high quality clustering of metagenomic samples consisting of millions of reads, in reasonable time limits, when executed on a modest size cluster.

Chloroplast photooxidation-induced transcriptome reprogramming in Arabidopsis immutans white leaf sectors.

Arabidopsis (Arabidopsis thaliana) immutans (im) has green and white sectoring due to the action of a nuclear recessive gene, IMMUTANS. The green sectors contain normal-appearing chloroplasts, whereas the white sectors contain abnormal chloroplasts that lack colored carotenoids due to a defect in phytoene desaturase activity. Previous biochemical and molecular characterizations of the green leaf sectors revealed alterations suggestive of a source-sink relationship between the green and white sectors of im. In this study, we use an Affymetrix ATH1 oligoarray to further explore the nature of sink metabolism in im white tissues. We show that lack of colored carotenoids in the im white tissues elicits a differential response from a large number of genes involved in various cellular processes and stress responses. Gene expression patterns correlate with the repression of photosynthesis and photosynthesis-related processes in im white tissues, with an induction of Suc catabolism and transport, and with mitochondrial electron transport and fermentation. These results suggest that energy is derived via aerobic and anaerobic metabolism of imported sugar in im white tissues for growth and development. We also show that oxidative stress responses are largely induced in im white tissues; however, im green sectors develop additional energy-dissipating mechanisms that perhaps allow for the formation of green sectors. Furthermore, a comparison of the transcriptomes of im white and norflurazon-treated white leaf tissues reveals global as well as tissue-specific responses to photooxidation. We conclude that the differences in the mechanism of phytoene desaturase inhibition play an important role in differentiating these two white tissues.