GPU accelerated sequence alignment with traceback for GATK HaplotypeCaller

Background

Pairwise sequence alignment is widely used in many biological tools and applications. Existing GPU accelerated implementations mainly focus on calculating optimal alignment score and omit identifying the optimal alignment itself. In GATK HaplotypeCaller (HC), the semi-global pairwise sequence alignment with traceback has so far been difficult to accelerate effectively on GPUs.

Results

We first analyze the characteristics of the semi-global alignment with traceback in GATK HC and then propose a new algorithm that allows for retrieving the optimal alignment efficiently on GPUs. For the first stage, we choose intra-task parallelization model to calculate the position of the optimal alignment score and the backtracking matrix. Moreover, in the first stage, our GPU implementation also records the length of consecutive matches/mismatches in addition to lengths of consecutive insertions and deletions as in the CPU-based implementation. This helps efficiently retrieve the backtracking matrix to obtain the optimal alignment in the second stage.

Conclusions

Experimental results show that our alignment kernel with traceback is up to 80x and 14.14x faster than its CPU counterpart with synthetic datasets and real datasets, respectively. When integrated into GATK HC (alongside a GPU accelerated pair-HMMs forward kernel), the overall acceleration is 2.3x faster than the baseline GATK HC implementation, and 1.34x faster than the GATK HC implementation with the integrated GPU-based pair-HMMs forward algorithm. Although the methods proposed in this paper is to improve the performance of GATK HC, they can also be used in other pairwise alignments and applications.

     

Metabolic flux analysis of<Emphasis Type="Italic"> pykF</Emphasis> gene knockout<Emphasis Type="Italic"> Escherichia coli</Emphasis> based on <Superscript>13</Superscript>C-labeling experiments together with measurements of enzyme activities and intracellular metabolite concentrations

Metabolic flux analysis based on ¹³C-labeling experiments followed by the measurement of intracellular isotope distribution using both 2D NMR and GC-MS was carried out to investigate the effect of pyruvate kinase (pyk) gene knockout on the metabolism of Escherichia coli in continuous culture. In addition, the activities of 16 enzymes, and the concentrations of 5 intracellular metabolites, were measured as a function of time in batch culture as well as continuous culture. It was found that flux through phosphoenol pyruvate carboxylase and malic enzyme were up-regulated in the pykF^– mutant as compared with the wild type, and acetate formation was significantly reduced in the mutant. In addition, flux through the phosphofructose kinase pathway was reduced and that through the oxidative pentose phosphate (PP) pathway increased in the mutant. This was evidenced by the corresponding enzyme activities, and the increase in the concentrations of phosphoenol pyruvate, glucose-6-phosphate and 6-phosphogluconate, etc. It was also found for continuous cultivation that the enzyme activities of the oxidative PP and Entner-Doudoroff pathways increased as the dilution rate increased for the pykF^– mutant. To clarify the metabolism quantitatively, it was found to be quite important to integrate the information on intracellular metabolic flux distribution, enzyme activities and intracellular metabolite concentrations.     

Evaluating the performance of likelihood methods for detecting population structure and migration

A plethora of statistical models have recently been developed to estimate components of population genetic history. Very few of these methods, however, have been adequately evaluated for their performance in accurately estimating population genetic parameters of interest. In this paper, we continue a research program of evaluation of population genetic methods through computer simulation. Specifically, we examine the software MIGRATEE-N 1.6.8 and test the accuracy of this software to estimate genetic diversity (Theta), migration rates, and confidence intervals. We simulated nucleotide sequence data under a neutral coalescent model with lengths of 500 bp and 1000 bp, and with three different per site Theta values of (0.00025, 0.0025, 0.025) crossed with four different migration rates (0.0000025, 0.025, 0.25, 2.5) to construct 1000 evolutionary trees per-combination per-sequence-length. We found that while MIGRATEE-N 1.6.8 performs reasonably well in estimating genetic diversity (Theta), it does poorly at estimating migration rates and the confidence intervals associated with them. We recommend researchers use this software with caution under conditions similar to those used in this evaluation.     

Does Ethylene Mediate Cluster Root Formation under Iron Deficiency?

Casuarina glauca develops proteoid (cluster) roots in response to Fe deficiency. This study set out to investigate the possible involvement of ethylene in the initiation and/or the morphogenesis of cluster roots (CR). For this purpose, the effect of Ag+ added as silver thiosulfate, an inhibitor of ethylene action has been studied in plants growing hydroponically. No CR formation was observed in these growth conditions. Inhibition of ethylene biosynthesis by aminoethoxyvinylglycine, 1- aminoisobutyric acid, aminoxyacetic acid or cobalt chloride also eliminated the positive effect of Fe deficiency on CR formation in C. glauca. CR were not formed in Fe- deficient roots in the presence of ethylene inhibitors, suggesting a role for ethylene in the morphological responses to Fe deficiency. Interestingly, treatment of Casuarina plants with the ethylene precursor 1-aminocyclopropane-1-carboxylic acid stimulated significantly the formation of CR, even if plants are supplied with Fe. However, this stimulation did not reach the level of CR obtained in Fe-deficient plants. These results suggest that an ethylene-mediated signalling pathway is involved in CR formation process in C. glauca.     

Growth of Escherichia coli in iron-enriched medium increases HPI catalase activity

Escherichia coli has two catalases, HPI and HPII. HPI is induced during logarithmic growth in response to low concentrations of hydrogen peroxide. This induction is OxyR-dependent. On the other hand, HPII is not peroxide-inducible but is induced in entry to the stationary phase. We demonstrate here that E. coli displayed higher HPI catalase activity when compared to the cultures that were grown in a normal medium, if grown in a medium supplemented with iron-citrate. Iron supplementation had no effect on HPII catalase. This increase of HPI activity was OxyR-independent and not observed in a Deltafur mutant. The physiological significance of the increase of HPI activity is unclear, but it appears that the katG gene that codes for HPI catalase is among the genes that are regulated by Fur.     

Performance-based selection of likelihood models for phylogeny estimation

Phylogenetic estimation has largely come to rely on explicitly model-based methods. This approach requires that a model be chosen and that that choice be justified. To date, justification has largely been accomplished through use of likelihood-ratio tests (LRTs) to assess the relative fit of a nested series of reversible models. While this approach certainly represents an important advance over arbitrary model selection, the best fit of a series of models may not always provide the most reliable phylogenetic estimates for finite real data sets, where all available models are surely incorrect. Here, we develop a novel approach to model selection, which is based on the Bayesian information criterion, but incorporates relative branch-length error as a performance measure in a decision theory (DT) framework. This DT method includes a penalty for overfitting, is applicable prior to running extensive analyses, and simultaneously compares all models being considered and thus does not rely on a series of pairwise comparisons of models to traverse model space. We evaluate this method by examining four real data sets and by using those data sets to define simulation conditions. In the real data sets, the DT method selects the same or simpler models than conventional LRTs. In order to lend generality to the simulations, codon-based models (with parameters estimated from the real data sets) were used to generate simulated data sets, which are therefore more complex than any of the models we evaluate. On average, the DT method selects models that are simpler than those chosen by conventional LRTs. Nevertheless, these simpler models provide estimates of branch lengths that are more accurate both in terms of relative error and absolute error than those derived using the more complex (yet still wrong) models chosen by conventional LRTs. This method is available in a program called DT-ModSel.     

Salicylic Acid Spraying-Induced Resilience Strategies Against the Damaging Impacts of Drought and/or Salinity Stress in Two Varieties of Vicia faba L. Seedlings

Journal of Plant Growth Regulation - Under the present era of changing climate, plants face simultaneous abiotic pressures rather than single stress. Under these unprecedented and joint...     

Modeling the impact of periodic bottlenecks, unidirectional mutation, and observational error in experimental evolution

Antibiotic resistant bacteria are a constant threat in the battle against infectious diseases. One strategy for reducing their effect is to temporarily discontinue the use of certain antibiotics in the hope that in the absence of the antibiotic the resistant strains will be replaced by the sensitive strains. An experiment where this strategy is employed in vitro [5] produces data which showed a slow accumulation of sensitive mutants. Here we propose a mathematical model and statistical analysis to explain this data.The stochastic model elucidates the trend and error structure of the data. It provides a guide for developing future sampling strategies, and provides a framework for long term predictions of the effects of discontinuing specific antibiotics on the dynamics of resistant bacterial populations.This Research is part of the Initiative in Bioinformatics and Evolutionary Studies (IBEST) at the University of Idaho. Funding was provided by NSF EPSCoR EPS-0080935, NSF EPSCoR, EPS-0132626, and NIH NCRR grant NIH NCRR- 20RR016448. Paul Joyce is also funded by NSF DEB-0089756, and NSF DMS-0072198.