Christina Boucher

SEQuel: improving the accuracy of genome assemblies

Motivation: Assemblies of next-generation sequencing (NGS) data, although accurate, still contain a substantial number of errors that need to be corrected after the assembly process. We develop SEQuel, a tool that corrects errors (i.e. insertions, deletions and substitution errors) in the assembled contigs. Fundamental to the algorithm behind SEQuel is the positional de Bruijn graph, a graph structure that models k-mers within reads while incorporating the approximate positions of reads into the model. Results: SEQuel reduced the number of small insertions and deletions in the assemblies of standard multi-cell Escherichia coli data by almost half, and corrected between 30% and 94% of the substitution errors. Further, we show SEQuel is imperative to improving single-cell assembly, which is inherently more challenging due to higher error rates and non-uniform coverage; over half of the small indels, and substitution errors in the single-cell assemblies were corrected. We apply SEQuel to the recently assembled Deltaproteobacterium SAR324 genome, which is the first bacterial genome with a comprehensive single-cell genome assembly, and make over 800 changes (insertions, deletions and substitutions) to refine this assembly. Availability: SEQuel can be used as a post-processing step in combination with any NGS assembler and is freely available at http://bix.ucsd.edu/SEQuel/. Contact: ppevzner@cs.ucsd.edu

The SLOW GROWTH3 Pentatricopeptide Repeat Protein Is Required for the Splicing of Mitochondrial NADH Dehydrogenase Subunit7 Intron 2 in Arabidopsis

Incomplete splicing of a mitochondrial gene affects plant growth and development. Mitochondria play an important role in maintaining metabolic and energy homeostasis in the cell. In plants, impairment in mitochondrial functions usually has detrimental effects on growth and development. To study genes that are important for plant growth, we have isolated a collection of slow growth (slo) mutants in Arabidopsis (Arabidopsis thaliana). One of the slo mutants, slo3, has a significant reduction in mitochondrial complex I activity. The slo3 mutant has a four-nucleotide deletion in At3g61360 that encodes a pentatricopeptide repeat (PPR) protein. The SLO3 protein contains nine classic PPR domains belonging to the P subfamily. The small deletion in the slo3 mutant changes the reading frame and creates a premature stop codon in the first PPR domain. We demonstrated that the SLO3-GFP is localized to the mitochondrion. Further analysis of mitochondrial RNA metabolism revealed that the slo3 mutant was defective in splicing of NADH dehydrogenase subunit7 (nad7) intron 2. This specific splicing defect led to a dramatic reduction in complex I activity in the mutant as revealed by blue native gel analysis. Complementation of slo3 by 35S:SLO3 or 35S:SLO3-GFP restored the splicing of nad7 intron 2, the complex I activity, and the growth defects of the mutant. Together, these results indicate that the SLO3 PPR protein is a splicing factor of nad7 intron 2 in Arabidopsis mitochondria.

Exploring Non-touchscreen Gestures for Smartwatches

Although smartwatches are gaining popularity among mainstream consumers, the input space is limited due to their small form factor. The goal of this work is to explore how to design non-touchscreen gestures to extend the input space of smartwatches. We conducted an elicitation study eliciting gestures for 31 smartwatch tasks. From this study, we demonstrate that a consensus exists among the participants on the mapping of gesture to command and use this consensus to specify a user-defined gesture set. Using gestures collected during our study, we define a taxonomy describing the mapping and physical characteristics of the gestures. Lastly, we provide insights to inform the design of non-touchscreen gestures for smartwatch interaction.

MEGARes: an antimicrobial resistance database for high throughput sequencing

Antimicrobial resistance has become an imminent concern for public health. As methods for detection and characterization of antimicrobial resistance move from targeted culture and polymerase chain reaction to high throughput metagenomics, appropriate resources for the analysis of large-scale data are required. Currently, antimicrobial resistance databases are tailored to smaller-scale, functional profiling of genes using highly descriptive annotations. Such characteristics do not facilitate the analysis of large-scale, ecological sequence datasets such as those produced with the use of metagenomics for surveillance. In order to overcome these limitations, we present MEGARes (https://megares.meglab.org), a hand-curated antimicrobial resistance database and annotation structure that provides a foundation for the development of high throughput acyclical classifiers and hierarchical statistical analysis of big data. MEGARes can be browsed as a stand-alone resource through the website or can be easily integrated into sequence analysis pipelines through download. Also via the website, we provide documentation for AmrPlusPlus, a user-friendly Galaxy pipeline for the analysis of high throughput sequencing data that is pre-packaged for use with the MEGARes database.

Use of Metagenomic Shotgun Sequencing Technology To Detect Foodborne Pathogens within the Microbiome of the Beef Production Chain

ABSTRACT Foodborne illnesses associated with pathogenic bacteria are a global public health and economic challenge. The diversity of microorganisms (pathogenic and nonpathogenic) that exists within the food and meat industries complicates efforts to understand pathogen ecology. Further, little is known about the interaction of pathogens within the microbiome throughout the meat production chain. Here, a metagenomic approach and shotgun sequencing technology were used as tools to detect pathogenic bacteria in environmental samples collected from the same groups of cattle at different longitudinal processing steps of the beef production chain: cattle entry to feedlot, exit from feedlot, cattle transport trucks, abattoir holding pens, and the end of the fabrication system. The log read counts classified as pathogens per million reads for Salmonella enterica, Listeria monocytogenes, Escherichia coli, Staphylococcus aureus, Clostridium spp. (C. botulinum and C. perfringens), and Campylobacter spp. (C. jejuni, C. coli, and C. fetus) decreased over subsequential processing steps. Furthermore, the normalized read counts for S. enterica, E. coli, and C. botulinum were greater in the final product than at the feedlots, indicating that the proportion of these bacteria increased (the effect on absolute numbers was unknown) within the remaining microbiome. From an ecological perspective, data indicated that shotgun metagenomics can be used to evaluate not only the microbiome but also shifts in pathogen populations during beef production. Nonetheless, there were several challenges in this analysis approach, one of the main ones being the identification of the specific pathogen from which the sequence reads originated, which makes this approach impractical for use in pathogen identification for regulatory and confirmation purposes.

Resistome diversity in cattle and the environment decreases during beef production

Antimicrobial resistant determinants (ARDs) can be transmitted from livestock systems through meat products or environmental effluents. The public health risk posed by these two routes is not well understood, particularly in non-pathogenic bacteria. We collected pooled samples from 8 groups of 1741 commercial cattle as they moved through the process of beef production from feedlot entry through slaughter. We recorded antimicrobial drug exposures and interrogated the resistome at points in production when management procedures could potentially influence ARD abundance and/or transmission. Over 300 unique ARDs were identified. Resistome diversity decreased while cattle were in the feedlot, indicating selective pressure. ARDs were not identified in beef products, suggesting that slaughter interventions may reduce the risk of transmission of ARDs to beef consumers. This report highlights the utility and limitations of metagenomics for assessing public health risks regarding antimicrobial resistance, and demonstrates that environmental pathways may represent a greater risk than the food supply. DOI: http://dx.doi.org/10.7554/eLife.13195.001

Characterization of the resistome in manure, soil and wastewater from dairy and beef production systems.

It has been proposed that livestock production effluents such as wastewater, airborne dust and manure increase the density of antimicrobial resistant bacteria and genes in the environment. The public health risk posed by this proposed outcome has been difficult to quantify using traditional microbiological approaches. We utilized shotgun metagenomics to provide a first description of the resistome of North American dairy and beef production effluents, and identify factors that significantly impact this resistome. We identified 34 mechanisms of antimicrobial drug resistance within 34 soil, manure and wastewater samples from feedlot, ranch and dairy operations. The majority of resistance-associated sequences found in all samples belonged to tetracycline resistance mechanisms. We found that the ranch samples contained significantly fewer resistance mechanisms than dairy and feedlot samples, and that the resistome of dairy operations differed significantly from that of feedlots. The resistome in soil, manure and wastewater differed, suggesting that management of these effluents should be tailored appropriately. By providing a baseline of the cattle production waste resistome, this study represents a solid foundation for future efforts to characterize and quantify the public health risk posed by livestock effluents.

On the Structure of Small Motif Recognition Instances

Given a set of sequences, S , and degeneracy parameter, d , the Consensus Sequence problem asks whether there exists a sequence that has Hamming distance at most d from each sequence in S . A valid motif set is a set of sequences for which such a consensus sequence exists, while a decoy set is a set of sequences that does not have a consensus sequence but whose pairwise Hamming distances are all at most 2d . At present, no efficient solution is known to the Consensus Sequence problem when the number of sequences is greater than three. For instances of Consensus Sequence with binary sequences and cardinality four, we present a combinatorial characterization of decoy sets and a linear-time exact algorithm, resolving an open problem posed by Gramm et al. [7].

Genome Sequence of Xanthomonas arboricola pv. Corylina, Isolated from Turkish Filbert in Colorado

ABSTRACT Previously, we reported the isolation of a bacterium producing leaf spots in Turkish filbert. Here, we present the draft genome assembly of the bacterium identified as Xanthomonas arboricola pv. corylina. To our knowledge, this is the first published genome of this pathovar of X. arboricola.

Variable-Order de Bruijn Graphs

The de Bruijn graph GK of a set of strings Sis a key data structure in genome assembly that represents overlaps between all the K-length substrings of S. Construction and navigation of the graph is a space and time bottleneck in practice and the main hurdle for assembling large genomes. This problem is compounded because state-of-the-art assemblers do not build the de Bruijn graph for a single order (value of K) but for multiple values of K: they builddde Bruijn graphs, each with a specific order, i.e., GK1, GK2, GKd. Al-though, this paradigm increases the quality of the assembly produce but it greatly increases runtime, because of the need to construct graphs instead of one. In this paper, we show how to augment a succinct de Bruijn graph representation by Bowe et al. (Proc. WABI, 2012) to support new operations that let us change order on the fly, effectively representing all de Bruijn graphs of order up to some maximum Kin a single data structure. Our experiments show our variable-order de Bruijn graph only modestly increases space usage, construction time, and navigation time compared to a single order graph.