The Taverna Interaction Service: enabling manual interaction in workflows

Summary: Taverna is an application that eases the integrationof tools and databases for life science research by the constructionof workflows. The Taverna Interaction Service extends the functionalityof Taverna by defining human interaction within a workflow andacting as a mediation layer between the automated workflow engineand one or more users. Availability: Taverna, the Interaction Service plug-in and webapplication are available as open source and can be downloadedfrom http://taverna.sourceforge.net/ Contact: taverna-users{at}lists.sourceforge.net Associate Editor: John Quackenbush     

Fractal MapReduce decomposition of sequence alignment

Background

The dramatic fall in the cost of genomic sequencing, and the increasing convenience of distributed cloud computing resources, positions the MapReduce coding pattern as a cornerstone of scalable bioinformatics algorithm development. In some cases an algorithm will find a natural distribution via use of map functions to process vectorized components, followed by a reduce of aggregate intermediate results. However, for some data analysis procedures such as sequence analysis, a more fundamental reformulation may be required.

Results

In this report we describe a solution to sequence comparison that can be thoroughly decomposed into multiple rounds of map and reduce operations. The route taken makes use of iterated maps, a fractal analysis technique, that has been found to provide a "alignment-free" solution to sequence analysis and comparison. That is, a solution that does not require dynamic programming, relying on a numeric Chaos Game Representation (CGR) data structure. This claim is demonstrated in this report by calculating the length of the longest similar segment by inspecting only the USM coordinates of two analogous units: with no resort to dynamic programming.

Conclusions

The procedure described is an attempt at extreme decomposition and parallelization of sequence alignment in anticipation of a volume of genomic sequence data that cannot be met by current algorithmic frameworks. The solution found is delivered with a browser-based application (webApp), highlighting the browser's emergence as an environment for high performance distributed computing.

Availability

Public distribution of accompanying software library with open source and version control at http://usm.github.com. Also available as a webApp through Google Chrome's WebStore http://chrome.google.com/webstore: search with "usm".     

Genome skimming identifies polymorphism in tern populations and species

Background

Large-scale protein structure alignment, an indispensable tool to structural bioinformatics, poses a tremendous challenge on computational resources. To ensure structure alignment accuracy and efficiency, efforts have been made to parallelize traditional alignment algorithms in grid environments. However, these solutions are costly and of limited accessibility. Others trade alignment quality for speedup by using high-level characteristics of structure fragments for structure comparisons.

Findings

We present ppsAlign, a parallel protein structure Alignment framework designed and optimized to exploit the parallelism of Graphics Processing Units (GPUs). As a general-purpose GPU platform, ppsAlign could take many concurrent methods, such as TM-align and Fr-TM-align, into the parallelized algorithm design. We evaluated ppsAlign on an NVIDIA Tesla C2050 GPU card, and compared it with existing software solutions running on an AMD dual-core CPU. We observed a 36-fold speedup over TM-align, a 65-fold speedup over Fr-TM-align, and a 40-fold speedup over MAMMOTH.

Conclusions

ppsAlign is a high-performance protein structure alignment tool designed to tackle the computational complexity issues from protein structural data. The solution presented in this paper allows large-scale structure comparisons to be performed using massive parallel computing power of GPU.     

Scientific workflow optimization for improved peptide and protein identification

Background

Peptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal.

Results

We have applied a new optimization framework for the Taverna scientific workflow management system (http://ms-utils.org/Taverna_Optimization.pdf) to find the best combination of parameters for a given scientific workflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated by several phenomena that can be observed when allowing for larger mass measurement errors in sequence database searches. On-the-fly parameter optimization embedded in scientific workflow management systems enables experts and non-experts alike to extract the maximum amount of information from the data. The same workflows could be used for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but also for other tasks, such as retention time prediction.

Conclusion

Using the optimization framework, we were able to learn about how the data was acquired as well as the explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptides with N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gained with the extension of the common range for the mass measurement error tolerance parameters explored by the optimization framework.     

Experimental cloud immersion and foliar water uptake in saplings of Abies fraseri and Picea rubens

Key message

Frequent cloud immersion events result in direct uptake of cloud water and improve plant water potentials during daylight hours in saplings of two dominant cloud forest species.

Abstract

In ecosystems with frequent cloud immersion, the influence on plant water balance can be important. While cloud immersion can reduce plant water loss via transpiration, recent advances in methodology have suggested that many species also absorb water directly into leaves (foliar water uptake). The current study examines foliar water uptake and its influence on daily plant water balance in tree species of the endangered spruce–fir forest of the southern Appalachian Mountains, USA. These mountain-top communities are considered relic, boreal forests that may have persisted because of the benefits of frequent cloud immersion. We examined changes in needle water content, xylem water potentials, and stable isotope values in saplings of the two dominant tree species, Abies fraseri and Picea rubens before and after a 24 h period of experimental cloud immersion. Both species exhibited foliar water uptake following immersion, evidenced by substantial changes in stable isotope values of extracted needle water that reflected the composition of the fog water. In addition, total needle water content improved 3.7–6.4 % following experimental submersion and xylem water potentials were significantly greater (up to 0.33 MPa) in cloud-immersed plants over control plants. These results indicate that foliar water uptake may be an adaptive strategy for utilizing cloud water and improving overall tree vigor in these most southerly distributed boreal species.     

BioMoby extensions to the Taverna workflow management and enactment software

Background  

As biology becomes an increasingly computational science, it is critical that we develop software tools that support not only bioinformaticians, but also bench biologists in their exploration of the vast and complex data-sets that continue to build from international genomic, proteomic, and systems-biology projects. The BioMoby interoperability system was created with the goal of facilitating the movement of data from one Web-based resource to another to fulfill the requirements of non-expert bioinformaticians. In parallel with the development of BioMoby, the European myGrid project was designing Taverna, a bioinformatics workflow design and enactment tool. Here we describe the marriage of these two projects in the form of a Taverna plug-in that provides access to many of BioMoby's features through the Taverna interface.     

FPGA accelerator for protein secondary structure prediction based on the GOR algorithm

Background

Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases.

Results

The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes.

Conclusions

Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware.     

Improved experimental data processing for UHPLC–HRMS/MS lipidomics applied to nonalcoholic fatty liver disease

Introduction

Untargeted metabolomics workflows include numerous points where variance and systematic errors can be introduced. Due to the diversity of the lipidome, manual peak picking and quantitation using molecule specific internal standards is unrealistic, and therefore quality peak picking algorithms and further feature processing and normalization algorithms are important. Subsequent normalization, data filtering, statistical analysis, and biological interpretation are simplified when quality data acquisition and feature processing are employed.

Objectives

Metrics for QC are important throughout the workflow. The robust workflow presented here provides techniques to ensure that QC checks are implemented throughout sample preparation, data acquisition, pre-processing, and analysis.

Methods

The untargeted lipidomics workflow includes sample standardization prior to acquisition, blocks of QC standards and blanks run at systematic intervals between randomized blocks of experimental data, blank feature filtering (BFF) to remove features not originating from the sample, and QC analysis of data acquisition and processing.

Results

The workflow was successfully applied to mouse liver samples, which were investigated to discern lipidomic changes throughout the development of nonalcoholic fatty liver disease (NAFLD). The workflow, including a novel filtering method, BFF, allows improved confidence in results and conclusions for lipidomic applications.

Conclusion

Using a mouse model developed for the study of the transition of NAFLD from an early stage known as simple steatosis, to the later stage, nonalcoholic steatohepatitis, in combination with our novel workflow, we have identified phosphatidylcholines, phosphatidylethanolamines, and triacylglycerols that may contribute to disease onset and/or progression.

     

Efficient genetic transformation of okra (Abelmoschus esculentus (L.) Moench) and generation of insect-resistant transgenic plants expressing the cry1Ac gene

Key message

Agrobacterium -mediated transformation system for okra using embryos was devised and the transgenic Bt plants showed resistance to the target pest, okra shoot, and fruit borer ( Earias vittella ).

Abstract

Okra is an important vegetable crop and progress in genetic improvement via genetic transformation has been impeded by its recalcitrant nature. In this paper, we describe a procedure using embryo explants for Agrobacterium-mediated transformation and tissue culture-based plant regeneration for efficient genetic transformation of okra. Twenty-one transgenic okra lines expressing the Bacillus thuringiensis gene cry1Ac were generated from five transformation experiments. Molecular analysis (PCR and Southern) confirmed the presence of the transgene and double-antibody sandwich ELISA analysis revealed Cry1Ac protein expression in the transgenic plants. All 21 transgenic plants were phenotypically normal and fertile. T₁ generation plants from these lines were used in segregation analysis of the transgene. Ten transgenic lines were selected randomly for Southern hybridization and the results confirmed the presence of transgene integration into the genome. Normal Mendelian inheritance (3:1) of cry1Ac gene was observed in 12 lines out of the 21 T₀ lines. We selected 11 transgenic lines segregating in a 3:1 ratio for the presence of one transgene for insect bioassays using larvae of fruit and shoot borer (Earias vittella). Fruit from seven transgenic lines caused 100 % larval mortality. We demonstrate an efficient transformation system for okra which will accelerate the development of transgenic okra with novel agronomically useful traits.     

vcluster: a framework for auto scalable virtual cluster system in heterogeneous clouds

Cloud computing is an emerging technology and is being widely considered for resource utilization in various research areas. One of the main advantages of cloud computing is its flexibility in computing resource allocations. Many computing cycles can be ready in very short time and can be smoothly reallocated between tasks. Because of this, there are many private companies entering the new business of reselling their idle computing cycles. Research institutes have also started building their own cloud systems for their various research purposes. In this paper, we introduce a framework for virtual cluster system called vcluster which is capable of utilizing computing resources from heterogeneous clouds and provides a uniform view in computing resource management. vcluster is an IaaS (Infrastructure as a Service) based cloud resource management system. It distributes batch jobs to multiple clouds depending on the status of queue and system pool. The main design philosophy behind vcluster is cloud and batch system agnostic and it is achieved through plugins. This feature mitigates the complexity of integrating heterogeneous clouds. In the pilot system development, we use FermiCloud and Amazon EC2, which are a private and a public cloud system, respectively. In this paper, we also discuss the features and functionalities that must be considered in virtual cluster systems.     

A novel T-DNA integration in rice involving two interchromosomal translocations

Key message

A male sterile transgenic rice plant TC-19 harboured a novel T-DNA integration in chromosome 8 with two interchromosomal translocations of 6.55 kb chromosome 3 and 29.8 kb chromosome 9 segments.

Abstract

We report a complex Agrobacterium T-DNA integration in rice (Oryza sativa) associated with two interchromosomal translocations. The T-DNA-tagged rice mutant TC-19, which harboured a single copy of the T-DNA, displayed male sterile phenotype in the homozygous condition. Analysis of the junctions between the T-DNA ends and the rice genome by genome walking showed that the right border is flanked by a chromosome 3 sequence and the left border is flanked by a chromosome 9 sequence. Upon further walking on chromosome 3, a chromosome 3/chromosome 8 fusion was detected. Genome walking from the opposite end of the chromosome 8 break point revealed a chromosome 8/chromosome 9 fusion. Our findings revealed that the T-DNA, together with a 6.55-kb region of chromosome 3 and a 29.8-kb region of chromosome 9, was translocated to chromosome 8. Southern blot analysis of the homozygous TC-19 mutant revealed that the native sequences of chromosome 3 and 9 were restored but the disruption of chromosome 8 in the first intron of the gene Os08g0152500 was not restored. The integration of the complex T-DNA in chromosome 8 caused male sterility.     

A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data

Background

Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.

Results

We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.

Conclusion

The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.     

Difference in capacity of physiological integration between two ecotypes of Leymus chinensis underlies their different performance

Background and aims

Physiological integration can enhance the performance of clonal plants, but whether this differs between ecotypes and whether such difference is underlying their distribution have scarcely been addressed. We aimed to determine whether physiological integration differs between ecotypes and whether an ecotype with a wider distribution shows a greater capacity of physiological integration.

Methods

A garden experiment was conducted with ramet pairs of both ecotypes (grey-green and yellow-green ecotype) of a typical rhizomatous clonal plant, Leymus chinensis, using rhizome connection (connected vs. disconnected) and ecotype as factors. Physiological and biomass features were measured and compared to assess the effects of physiological integration for both ecotypes.

Results

Physiological integration enhanced the maximum net photosynthetic rate, apparent quantum efficiency, respiration rate, water use efficiency, and chlorophyll content of ramets no matter whether they were subject to nutrient-poor or -rich soil, as long as they were connected to other ramets. Moreover, such an effect on photosynthetic capacity and water use efficiency was larger for the grey-green ecotype than for the yellow-green ecotype.

Conclusions

The results suggested that grey-green ecotype has significantly greater capacity of physiological integration than yellow-green ecotype, which was assumed to be one of the underlying mechanisms of the wider distribution of the former in nature.