DDBJ Read Annotation Pipeline: A Cloud Computing-Based Pipeline for High-Throughput Analysis of Next-Generation Sequencing Data

High-performance next-generation sequencing (NGS) technologies are advancing genomics and molecular biological research. However, the immense amount of sequence data requires computational skills and suitable hardware resources that are a challenge to molecular biologists. The DNA Data Bank of Japan (DDBJ) of the National Institute of Genetics (NIG) has initiated a cloud computing-based analytical pipeline, the DDBJ Read Annotation Pipeline (DDBJ Pipeline), for a high-throughput annotation of NGS reads. The DDBJ Pipeline offers a user-friendly graphical web interface and processes massive NGS datasets using decentralized processing by NIG supercomputers currently free of charge. The proposed pipeline consists of two analysis components: basic analysis for reference genome mapping and de novo assembly and subsequent high-level analysis of structural and functional annotations. Users may smoothly switch between the two components in the pipeline, facilitating web-based operations on a supercomputer for high-throughput data analysis. Moreover, public NGS reads of the DDBJ Sequence Read Archive located on the same supercomputer can be imported into the pipeline through the input of only an accession number. This proposed pipeline will facilitate research by utilizing unified analytical workflows applied to the NGS data. The DDBJ Pipeline is accessible at http://p.ddbj.nig.ac.jp/.     

Patient-specific modelling of pulmonary airflow using GPU cluster for the application in medical practice

In this paper, we propose a novel patient-specific method of modelling pulmonary airflow using graphics processing unit (GPU) computation that can be applied in medical practice. To overcome the barriers imposed by computation speed, installation price and footprint to the application of computational fluid dynamics, we focused on GPU computation and the lattice Boltzmann method (LBM). The GPU computation and LBM are compatible due to the characteristics of the GPU. As the optimisation of data access is essential for the performance of the GPU computation, we developed an adaptive meshing method, in which an airway model is covered by isotropic subdomains consisting of a uniform Cartesian mesh. We found that 4³ size subdomains gave the best performance. The code was also tested on a small GPU cluster to confirm its performance and applicability, as the price and footprint are reasonable for medical applications.     

Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model

Several opensource or commercially available software platforms are widely used to develop dynamic simulations of movement. While computational approaches are conceptually similar across platforms, technical differences in implementation may influence output. We present a new upper limb dynamic model as a tool to evaluate potential differences in predictive behavior between platforms. We evaluated to what extent differences in technical implementations in popular simulation software environments result in differences in kinematic predictions for single and multijoint movements using EMG- and optimization-based approaches for deriving control signals. We illustrate the benchmarking comparison using SIMM–Dynamics Pipeline–SD/Fast and OpenSim platforms. The most substantial divergence results from differences in muscle model and actuator paths. This model is a valuable resource and is available for download by other researchers. The model, data, and simulation results presented here can be used by future researchers to benchmark other software platforms and software upgrades for these two platforms.     

Collaborative grid infrastructure for molecular simulations: The eMinerals minigrid as a prototype integrated compute and data grid

This paper describes a prototype grid infrastructure, called the “eMinerals minigrid”, for molecular simulation scientists. which is based on an integration of shared compute and data resources. We describe the key components, namely the use of Condor pools, Linux/Unix clusters with PBS and IBM's LoadLeveller job handling tools, the use of Globus for security handling, the use of Condor-G tools for wrapping globus job submit commands, Condor's DAGman tool for handling workflow, the Storage Resource Broker for handling data, and the CCLRC dataportal and associated tools for both archiving data with metadata and making data available to other workers.     

Validation of Monte carlo Geant4 multithreading code for a 6 MV photon beam of varian linac on the grid computing

AimTo evaluate the computation time efficiency of the multithreaded code (G4Linac-MT) in the dosimetry application, using the high performance of the HPC-Marwan grid to determine with high accuracy the initial parameters of the 6 MV photon beam of Varian CLINAC 2100C.BackgroundThe difficulty of Monte Carlo methods is the long computation time, this is one of the disadvantages of the Monte Carlo methods.Materials and methodsCalculations are performed by the multithreaded code G4Linac-MT and Geant4.10.04.p02 using the HPC-Marwan computing grid to evaluate the computing speed for each code. The multithreaded version is tested in several CPUs to evaluate the computing speed according to the number of CPUs used. The results were compared to the measurements using different types of comparisons, TPR_20.10, penumbra, mean dose error and gamma index.ResultsThe results obtained for this work indicate a much higher computing time saving for the G4Linac-MT version compared to the Geant4.10.04 version, the computing time decreases with the number of CPUs used, can reach about 12 times if 64CPUs are used. After optimization of the initial electron beam parameters, the results of the dose simulations obtained for this work are in very good agreement with the experimental measurements with a mean dose error of up to 0.41% on the PDDs and 1.79% on the lateral dose.ConclusionsThe gain in computation time leads us to perform Monte Carlo simulations with a large number of events which gives a high accuracy of the dosimetry results obtained in this work.     

A software tool for modeling and simulation of numerical P systems

A P system represents a distributed and parallel bio-inspired computing model in which basic data structures are multi-sets or strings. Numerical P systems have been recently introduced and they use numerical variables and local programs (or evolution rules), usually in a deterministic way. They may find interesting applications in areas such as computational biology, process control or robotics. The first simulator of numerical P systems (SNUPS) has been designed, implemented and made available to the scientific community by the authors of this paper. SNUPS allows a wide range of applications, from modeling and simulation of ordinary differential equations, to the use of membrane systems as computational blocks of cognitive architectures, and as controllers for autonomous mobile robots. This paper describes the functioning of a numerical P system and presents an overview of SNUPS capabilities together with an illustrative example. Availability: SNUPS is freely available to researchers as a standalone application and may be downloaded from a dedicated website, http://snups.ics.pub.ro/, which includes an user manual and sample membrane structures.     

High performance computing in biology: multimillion atom simulations of nanoscale systems

Computational methods have been used in biology for sequence analysis (bioinformatics), all-atom simulation (molecular dynamics and quantum calculations), and more recently for modeling biological networks (systems biology). Of these three techniques, all-atom simulation is currently the most computationally demanding, in terms of compute load, communication speed, and memory load. Breakthroughs in electrostatic force calculation and dynamic load balancing have enabled molecular dynamics simulations of large biomolecular complexes. Here, we report simulation results for the ribosome, using approximately 2.64 million atoms, the largest all-atom biomolecular simulation published to date. Several other nano-scale systems with different numbers of atoms were studied to measure the performance of the NAMD molecular dynamics simulation program on the Los Alamos National Laboratory Q Machine. We demonstrate that multimillion atom systems represent a 'sweet spot' for the NAMD code on large supercomputers. NAMD displays an unprecedented 85% parallel scaling efficiency for the ribosome system on 1024 CPUs. We also review recent targeted molecular dynamics simulations of the ribosome that prove useful for studying conformational changes of this large biomolecular complex in atomic detail.     

Thermal‐ and urea‐induced unfolding processes of glutathione S‐transferase by molecular dynamics simulation

The Schistosoma juponicum 26 kDa glutathione S‐transferase (sj26GST) consists of the N‐terminal domain (N‐domain), containing three alpha‐helices (named H1‐H3) and four anti‐parallel beta‐strands (S1‐S4), and the C‐terminal domain (C‐domain), comprising five alpha‐helices (named H4‐H8). In present work, molecular dynamics simulations and fluorescence spectroscopic were used to gain insights into the unfolding process of sj26GST. The molecular dynamics simulations on sj26GST subunit both in water and in 8 M urea were carried out at 300 K, 400 K and 500 K, respectively. Spectroscopic measurements were employed to monitor structural changes. Molecular dynamics simulations of sj26GST subunit induced by urea and temperature showed that the initial unfolding step of sj26GST both in water and urea occurred on N‐domain, involving the disruption of helices H2, H3 and strands S3 and S4, whereas H6 was the last region exposed to solution and was the last helix to unfold. Moreover, simulations analyses combining with fluorescence and circular dichroism spectra indicated that N‐domain could not fold independent, suggesting that correct folding of N‐domain depended on its interactions with C‐domain. We further proposed that the folding of GSTs could begin with the hydrophobic collapse of C‐domain whose H4, H5, H6 and H7 could move close to each other and form a hydrophobic core, especially H6 wrapped in the hydrophobic center and beginning spontaneous formation of the helix. S3, S4, H3, and H2 could form in the wake of the interaction between C‐domain and N‐domain. The paper can offer insights into the molecular mechanism of GSTs unfolding. © 2014 Wiley Periodicals, Inc. Biopolymers 103: 247–259, 2015.     

Computational design of allosteric ribozymes as molecular biosensors

Nucleic acids have proven to be a very suitable medium for engineering various nanostructures and devices. While synthetic DNAs are commonly used for self-assembly of nanostructures and devices in vitro, functional RNAs, such as ribozymes, are employed both in vitro and in vivo. Allosteric ribozymes have applications in molecular computing, biosensoring, high-throughput screening arrays, exogenous control of gene expression, and others. They switch on and off their catalytic function as a result of a conformational change induced by ligand binding. Designer ribozymes are engineered to respond to different effectors by in vitro selection, rational and computational design methods. Here, I present diverse computational methods for designing allosteric ribozymes with various logic functions that sense oligonucleotides or small molecules. These methods yield the desired ribozyme sequences within minutes in contrast to the in vitro selection methods, which require weeks. Methods for synthesis and biochemical testing of ribozymes are also discussed.     

Single particle 3D reconstruction for 2D crystal images of membrane proteins

In cases where ultra-flat cryo-preparations of well-ordered two-dimensional (2D) crystals are available, electron crystallography is a powerful method for the determination of the high-resolution structures of membrane and soluble proteins. However, crystal unbending and Fourier-filtering methods in electron crystallography three-dimensional (3D) image processing are generally limited in their performance for 2D crystals that are badly ordered or non-flat. Here we present a single particle image processing approach, which is implemented as an extension of the 2D crystallographic pipeline realized in the 2dx software package, for the determination of high-resolution 3D structures of membrane proteins. The algorithm presented, addresses the low single-to-noise ratio (SNR) of 2D crystal images by exploiting neighborhood correlation between adjacent proteins in the 2D crystal. Compared with conventional single particle processing for randomly oriented particles, the computational costs are greatly reduced due to the crystal-induced limited search space, which allows a much finer search space compared to classical single particle processing. To reduce the considerable computational costs, our software features a hybrid parallelization scheme for multi-CPU clusters and computer with high-end graphic processing units (GPUs). We successfully apply the new refinement method to the structure of the potassium channel MloK1. The calculated 3D reconstruction shows more structural details and contains less noise than the map obtained by conventional Fourier-filtering based processing of the same 2D crystal images.