GNU General Public License V3 Research Articles

Automatic Memory Control of Multiple Virtual Machines on a Consolidated Server

Through virtualization, multiple virtual machines (VMs) can coexist and operate on one physical machine. When virtual machines compete for memory, the performances of applications deteriorate, especially those of memory-intensive applications. In this study, we aim to optimize memory control techniques using a balloon driver for server consolidation. Our contribution is three-fold: (1) We design and implement an automatic control system for memory based on a Xen balloon driver. To avoid interference with VM monitor operation, our system works in user mode; therefore, the system is easily applied in practice. (2) We design an adaptive global-scheduling algorithm to regulate memory. This algorithm is based on a dynamic baseline, which can adjust memory allocation according to the memory used by the VMs. (3) We evaluate our optimized solution in a real environment with 10 VMs and well-known benchmarks ( DaCapo and Phoronix Test Suites). Experiments confirm that our system can improve the performance of memory-intensive and disk-intensive applications by up to 500 and 300 percent, respectively. This toolkit has been released for free download as a GNU General Public License v3 software.

X!TandemPipeline: A Tool to Manage Sequence Redundancy for Protein Inference and Phosphosite Identification.

X!TandemPipeline is a software designed to perform protein inference and to manage redundancy in the results of phosphosite identification by database search. It provides the minimal list of proteins or phosphosites that are present in a set of samples using grouping algorithms based on the principle of parsimony. Regarding proteins, a two-level classification is performed, where groups gather proteins sharing at least one peptide and subgroups gather proteins that are not distinguishable according to the identified peptides. Regarding phosphosites, an innovative approach based on the concept of phosphoisland is used to gather overlapping phosphopeptides. The graphical interface of X!TandemPipeline allows the users to launch X!tandem identification, to inspect spectra and to manually validate their assignment to peptides, to launch the grouping program, and to visualize elementary data as well as grouping and redundancy information. Identification results obtained from other search engines can also be processed. X!TandemPipeline results can be exported as ready-to-use tabulated files or as XML files that can be directly used by the PROTICdb database or by the MassChroQ quantification software. X!TandemPipeline runs fast, is easy to use, and can process hundreds of samples simultaneously. It is freely available under the GNU General Public License v3.0 at http://pappso.inra.fr/bioinfo/xtandempipeline/ .

Fast motif matching revisited: high-order PWMs, SNPs and indels

While the position weight matrix (PWM) is the most popular model for sequence motifs, there is growing evidence of the usefulness of more advanced models such as first-order Markov representations, and such models are also becoming available in well-known motif databases. There has been lots of research of how to learn these models from training data but the problem of predicting putative sites of the learned motifs by matching the model against new sequences has been given less attention. Moreover, motif site analysis is often concerned about how different variants in the sequence affect the sites. So far, though, the corresponding efficient software tools for motif matching have been lacking. We develop fast motif matching algorithms for the aforementioned tasks. First, we formalize a framework based on high-order position weight matrices for generic representation of motif models with dinucleotide or general q -mer dependencies, and adapt fast PWM matching algorithms to the high-order PWM framework. Second, we show how to incorporate different types of sequence variants , such as SNPs and indels, and their combined effects into efficient PWM matching workflows. Benchmark results show that our algorithms perform well in practice on genome-sized sequence sets and are for multiple motif search much faster than the basic sliding window algorithm. Implementations are available as a part of the MOODS software package under the GNU General Public License v3.0 and the Biopython license ( http://www.cs.helsinki.fi/group/pssmfind ). janne.h.korhonen@gmail.com.

D2o: a distributed data object for parallel high-performance computing in Python

We introduce D2O, a Python module for cluster-distributed multi-dimensional numerical arrays. It acts as a layer of abstraction between the algorithm code and the data-distribution logic. The main goal is to achieve usability without losing numerical performance and scalability. D2O's global interface is similar to the one of a numpy.ndarray, whereas the cluster node's local data is directly accessible for use in customized high-performance modules. D2O is written in pure Python which makes it portable and easy to use and modify. Expensive operations are carried out by dedicated external libraries like numpy and mpi4py. The performance of D2O is on a par with numpy for serial applications and scales well when moving to an MPI cluster. D2O is open-source software available under the GNU General Public License v3 (GPL-3) at https://gitlab.mpcdf.mpg.de/ift/D2O

MCPB.py: A Python Based Metal Center Parameter Builder.

MCPB.py, a python based metal center parameter builder, has been developed to build force fields for the simulation of metal complexes employing the bonded model approach. It has an optimized code structure, with far fewer required steps than the previous developed MCPB program. It supports various AMBER force fields and more than 80 metal ions. A series of parametrization schemes to derive force constants and charge parameters are available within the program. We give two examples (one metalloprotein example and one organometallic compound example), indicating the program's ability to build reliable force fields for different metal ion containing complexes. The original version was released with AmberTools15. It is provided via the GNU General Public License v3.0 (GNU_GPL_v3) agreement and is free to download and distribute. MCPB.py provides a bridge between quantum mechanical calculations and molecular dynamics simulation software packages thereby enabling the modeling of metal ion centers. It offers an entry into simulating metal ions in a number of situations by providing an efficient way for researchers to handle the vagaries and difficulties associated with metal ion modeling.

DwarfCode: A Performance Prediction Tool for Parallel Applications

We present DwarfCode, a performance prediction tool for MPI applications on diverse computing platforms. The goal is to accurately predict the running time of applications for task scheduling and job migration. First, DwarfCode collects the execution traces to record the computing and communication events. Then, it merges the traces from different processes into a single trace. After that, DwarfCode identifies and compresses the repeating patterns in the final trace to shrink the size of the events. Finally, a dwarf code is generated to mimic the original program behavior. This smaller running benchmark is replayed in the target platform to predict the performance of the original application. In order to generate such a benchmark, two major challenges are to reduce the time complexity of trace merging and repeat compression algorithms. We propose an O ( mpn ) trace merging algorithm to combine the traces generated by separate MPI processes, where m denotes the upper bound of tracing distance , p denotes the number of processes, and n denotes the maximum of event numbers of all the traces. More importantly, we put forward a novel repeat compression algorithm, whose time complexity is O ( nlogn ). Experimental results show that DwarfCode can accurately predict the running time of MPI applications. The error rate is below 10 percent for compute and communication intensive applications. This toolkit has been released for free download as a GNU General Public License v3 software.

FluxModeCalculator: an efficient tool for large-scale flux mode computation.

Elementary flux mode (EFM) analysis is a powerful technique for determining the metabolic capacities and robustness of stoichiometric networks. Recently, several improvements have been made to the algorithm for enumerating the EFMs, making the study of large models possible. However, currently these tools require high performance workstations to perform large-scale EFM computations, thus limiting their applicability. We developed a more time and memory efficient implementation of the algorithm for EFM enumeration in MATLAB, called FluxModeCalculator, which enables large-scale EFM computation on ordinary desktop computers. FluxModeCalculator is open source and freely available under the terms of the GNU General Public License v3.0 at http://www.lumc.nl/jan-bert-van-klinken j.b.van_klinken@lumc.nl Supplementary data are available at Bioinformatics online.

KNIME an Open Source Solution for Predictive Analytics in the Geosciences [Software and Data Sets

KNIME (Konstanz Information Miner) is a modular computational environment, which allowseasy visual assembly, interactive data analysis, and data processing. It is an open source predictive analytics platform (released under the GNU General Public License v3) suited to process a variety of data formats, from basic csv or xlsx files, to more complex data structures such as xml, url and relational databases (e.g., db2, Oracle, MySQL). Surprisingly, it has not seen wide application in the earth sciences. A number of case studies providing examples of geoscience data processing will benefit both the academia and industry, very few geoscience applications are currently reported and these are dominantly in geoinformatics. In particular, the Energy and Mineral Exploration sectors, which make extensive use of Exploratory Data Analysis, Machine Learning (ML) and Data Mining (DM) software for data classification, pattern recognition and predictive modelling, will benefit significantly from KNIME. In contrast to other predictive analytics platforms (e.g., Orange, R, Rapid- Miner, Scikit-learn), what makes KNIME particularly appealing to geoscience applications is its ability to integrate different programming languages in the same workflow environment, some of them like the statistical software R or Matlab are well known in the geoscience community. KNIME is supported by an extensive community of users and developers. Since KNIME is built on top of Eclipse it shares the benefit of a plugin architecture that makes it easily extensible, many custom-built nodes are available and easily accessible through the Community Contributions area.

SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis.

A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html.

ParTIES: a toolbox for Paramecium interspersed DNA elimination studies.

Developmental DNA elimination occurs in a wide variety of multicellular organisms, but ciliates are the only single-celled eukaryotes in which this phenomenon has been reported. Despite considerable interest in ciliates as models for DNA elimination, no standard methods for identification and characterization of the eliminated sequences are currently available. We present the Paramecium Toolbox for Interspersed DNA Elimination Studies (ParTIES), designed for Paramecium species, that (i) identifies eliminated sequences, (ii) measures their presence in a sequencing sample and (iii) detects rare elimination polymorphisms. ParTIES is multi-threaded Perl software available at https://github.com/oarnaiz/ParTIES. ParTIES is distributed under the GNU General Public Licence v3.

Parallel implementation of the time-evolving block decimation algorithm for the Bose–Hubbard model

A system of ultracold atoms in an optical lattice represents a powerful experimental setup for testing the fundamentals of quantum mechanics. While its microscopic interaction mechanisms are well understood, the system behavior for a moderate number of particles is difficult to simulate due to a high dimension of its many-body space. This article presents TEBDOL, a parallel implementation of the time-evolving block decimation (TEBD) algorithm that can efficiently simulate time evolution of a one-dimensional chain of atoms in optical lattices. We investigate the parallelization strategy and the strong and weak scaling with the number of processes. Program summaryProgram title: TEBDOLCatalogue identifier: AEYN_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEYN_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public License v3.0No. of lines in distributed program, including test data, etc.: 9060No. of bytes in distributed program, including test data, etc.: 54338Distribution format: tar.gzProgramming language: Common Lisp.Computer: x86-64.Operating system: Linux.Has the code been vectorized or parallelized?: Parallelized using MPIRAM: 1–64 GBClassification: 7.7.External routines: Basic Linear Algebra Subprograms (BLAS), Linear Algebra Package (LAPACK), Message Passing Interface (MPI)Nature of problem: A system of neutral atoms in an optical lattice is a many-body quantum system that can be described using the Bose–Hubbard model. Hilbert space dimensions of many-body quantum models grow exponentially with the number of particles. Simulating time evolution in the Bose–Hubbard model is therefore a hard problem even in case of a moderate number of particles.Solution method: A system state is represented by a tensor network. Its time evolution is then simulated using the time-evolving block decimation (TEBD) algorithm.Restrictions: TEBDOL is limited to one-dimensional systems. The times accessible in the simulations are restricted by the growth of entanglement in the system.Unusual features: Tensor networks in TEBDOL support a global Abelian symmetry, i.e., the program conserves the total number of particles. Models with multiple particle species are supported as well. TEBDOL is implemented in Common Lisp and can run in parallel on a computer cluster.Running time: Running time depends on the lattice size, on the number of particles, and on the maximal allowed tensor dimensions. Simulations of simple models take a few seconds on a single CPU while simulations of large and complex models can take up to a week on a cluster with hundreds of CPUs.

Tentacle: distributed quantification of genes in metagenomes

BackgroundIn metagenomics, microbial communities are sequenced at increasingly high resolution, generating datasets with billions of DNA fragments. Novel methods that can efficiently process the growing volumes of sequence data are necessary for the accurate analysis and interpretation of existing and upcoming metagenomes.FindingsHere we present Tentacle, which is a novel framework that uses distributed computational resources for gene quantification in metagenomes. Tentacle is implemented using a dynamic master-worker approach in which DNA fragments are streamed via a network and processed in parallel on worker nodes. Tentacle is modular, extensible, and comes with support for six commonly used sequence aligners. It is easy to adapt Tentacle to different applications in metagenomics and easy to integrate into existing workflows.ConclusionsEvaluations show that Tentacle scales very well with increasing computing resources. We illustrate the versatility of Tentacle on three different use cases. Tentacle is written for Linux in Python 2.7 and is published as open source under the GNU General Public License (v3). Documentation, tutorials, installation instructions, and the source code are freely available online at: http://bioinformatics.math.chalmers.se/tentacle.Electronic supplementary materialThe online version of this article (doi:10.1186/s13742-015-0078-1) contains supplementary material, which is available to authorized users.

The Power Decoder Simulator for the Evaluation of Pooled shRNA Screen Performance

RNA interference screening using pooled, short hairpin RNA (shRNA) is a powerful, high-throughput tool for determining the biological relevance of genes for a phenotype. Assessing an shRNA pooled screen’s performance is difficult in practice; one can estimate the performance only by using reproducibility as a proxy for power or by employing a large number of validated positive and negative controls. Here, we develop an open-source software tool, the Power Decoder simulator, for generating shRNA pooled screening experiments in silico that can be used to estimate a screen’s statistical power. Using the negative binomial distribution, it models both the relative abundance of multiple shRNAs within a single screening replicate and the biological noise between replicates for each individual shRNA. We demonstrate that this simulator can successfully model the data from an actual laboratory experiment. We then use it to evaluate the effects of biological replicates and sequencing counts on the performance of a pooled screen, without the necessity of gathering additional data. The Power Decoder simulator is written in R and Python and is available for download under the GNU General Public License v3.0.

AWESoMe: A code for the calculation of phase and group velocities of acoustic waves in homogeneous solids

We present AWESoMe, an implementation of a method for the evaluation of acoustic wave velocities in homogeneous solid media. The code computes the phase and group velocities for all the possible propagation directions, as well as some related parameters such as the polarization vectors, the power flow angle and the enhancement factor. The code is conveniently interfaced with Gnuplot, thus offering immediate visualization of the results. AWESoMe is open-source software, available under the GNU General Public License v3. Program summaryProgram title: AWESoMeCatalogue identifier: AEWA_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEWA_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public Licence, version 3No. of lines in distributed program, including test data, etc.: 3104No. of bytes in distributed program, including test data, etc.: 44353Distribution format: tar.gzProgramming language: Fortran90.Computer: Workstations.Operating system: Linux/UNIX/Windows.Classification: 7.8.Nature of problem: Calculation of acoustic wave phase and group velocities in homogeneous solids.Solution method: Solving the Christoffel equation by diagonalization; computing group velocities and enhancement factors by vector operations.Additional comments: The DSYEVJ3 [1] subroutine is included in the AWESoMe code file.Running time: Seconds/minutes

GraphVar: A user-friendly toolbox for comprehensive graph analyses of functional brain connectivity

BackgroundGraph theory provides a powerful and comprehensive formalism of global and local topological network properties of complex structural or functional brain connectivity. Software packages such as the Brain-Connectivity-Toolbox have contributed to graph theory's increasing popularity for characterization of brain networks. However, comparably comprehensive packages are command-line based and require programming experience; this precludes their use by users without a computational background, whose research would otherwise benefit from graph-theoretical methods. New method“GraphVar” is a user-friendly GUI-based toolbox for comprehensive graph-theoretical analyses of brain connectivity, including network construction and characterization, statistical analysis on network topological measures, network based statistics, and interactive exploration of results. ResultsGraphVar provides a comprehensive collection of graph analysis routines for analyses of functional brain connectivity in one single toolbox by combining features across multiple currently available toolboxes, such as the Brain Connectivity Toolbox, the Graph Analysis Toolbox, and the Network Based Statistic Toolbox (BCT, Rubinov and Sporns, 2010; GAT, Hosseini et al., 2012; NBS, Zalesky et al., 2010). GraphVar was developed under the GNU General Public License v3.0 and can be downloaded at www.rfmri.org/graphvar or www.nitrc.org/projects/graphvar. Comparison with existing methodsBy combining together features across multiple toolboxes, GraphVar will allow comprehensive graph-theoretical analyses in one single toolbox without resorting to code. ConclusionsGraphVar will make graph theoretical methods more accessible for a broader audience of neuroimaging researchers.

EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data

The R package EasyStrata facilitates the evaluation and visualization of stratified genome-wide association meta-analyses (GWAMAs) results. It provides (i) statistical methods to test and account for between-strata difference as a means to tackle gene-strata interaction effects and (ii) extended graphical features tailored for stratified GWAMA results. The software provides further features also suitable for general GWAMAs including functions to annotate, exclude or highlight specific loci in plots or to extract independent subsets of loci from genome-wide datasets. It is freely available and includes a user-friendly scripting interface that simplifies data handling and allows for combining statistical and graphical functions in a flexible fashion. EasyStrata is available for free (under the GNU General Public License v3) from our Web site www.genepi-regensburg.de/easystrata and from the CRAN R package repository cran.r-project.org/web/packages/EasyStrata/. Supplementary data are available at Bioinformatics online.

CRISPRseek: a bioconductor package to identify target-specific guide RNAs for CRISPR-Cas9 genome-editing systems.

CRISPR-Cas systems are a diverse family of RNA-protein complexes in bacteria that target foreign DNA sequences for cleavage. Derivatives of these complexes have been engineered to cleave specific target sequences depending on the sequence of a CRISPR-derived guide RNA (gRNA) and the source of the Cas9 protein. Important considerations for the design of gRNAs are to maximize aimed activity at the desired target site while minimizing off-target cleavage. Because of the rapid advances in the understanding of existing CRISPR-Cas9-derived RNA-guided nucleases and the development of novel RNA-guided nuclease systems, it is critical to have computational tools that can accommodate a wide range of different parameters for the design of target-specific RNA-guided nuclease systems. We have developed CRISPRseek, a highly flexible, open source software package to identify gRNAs that target a given input sequence while minimizing off-target cleavage at other sites within any selected genome. CRISPRseek will identify potential gRNAs that target a sequence of interest for CRISPR-Cas9 systems from different bacterial species and generate a cleavage score for potential off-target sequences utilizing published or user-supplied weight matrices with position-specific mismatch penalty scores. Identified gRNAs may be further filtered to only include those that occur in paired orientations for increased specificity and/or those that overlap restriction enzyme sites. For applications where gRNAs are desired to discriminate between two related sequences, CRISPRseek can rank gRNAs based on the difference between predicted cleavage scores in each input sequence. CRISPRseek is implemented as a Bioconductor package within the R statistical programming environment, allowing it to be incorporated into computational pipelines to automate the design of gRNAs for target sequences identified in a wide variety of genome-wide analyses. CRISPRseek is available under the GNU General Public Licence v3.0 at http://www.bioconductor.org.

Pep2Path: Automated Mass Spectrometry-Guided Genome Mining of Peptidic Natural Products

Nonribosomally and ribosomally synthesized bioactive peptides constitute a source of molecules of great biomedical importance, including antibiotics such as penicillin, immunosuppressants such as cyclosporine, and cytostatics such as bleomycin. Recently, an innovative mass-spectrometry-based strategy, peptidogenomics, has been pioneered to effectively mine microbial strains for novel peptidic metabolites. Even though mass-spectrometric peptide detection can be performed quite fast, true high-throughput natural product discovery approaches have still been limited by the inability to rapidly match the identified tandem mass spectra to the gene clusters responsible for the biosynthesis of the corresponding compounds. With Pep2Path, we introduce a software package to fully automate the peptidogenomics approach through the rapid Bayesian probabilistic matching of mass spectra to their corresponding biosynthetic gene clusters. Detailed benchmarking of the method shows that the approach is powerful enough to correctly identify gene clusters even in data sets that consist of hundreds of genomes, which also makes it possible to match compounds from unsequenced organisms to closely related biosynthetic gene clusters in other genomes. Applying Pep2Path to a data set of compounds without known biosynthesis routes, we were able to identify candidate gene clusters for the biosynthesis of five important compounds. Notably, one of these clusters was detected in a genome from a different subphylum of Proteobacteria than that in which the molecule had first been identified. All in all, our approach paves the way towards high-throughput discovery of novel peptidic natural products. Pep2Path is freely available from http://pep2path.sourceforge.net/, implemented in Python, licensed under the GNU General Public License v3 and supported on MS Windows, Linux and Mac OS X.

Bammds: a tool for assessing the ancestry of low-depth whole-genome data using multidimensional scaling (MDS).

Summary: We present bammds, a practical tool that allows visualization of samples sequenced by second-generation sequencing when compared with a reference panel of individuals (usually genotypes) using a multidimensional scaling algorithm. Our tool is aimed at determining the ancestry of unknown samples—typical of ancient DNA data—particularly when only low amounts of data are available for those samples.Availability and implementation: The software package is available under GNU General Public License v3 and is freely available together with test datasets https://savannah.nongnu.org/projects/bammds/. It is using R (http://www.r-project.org/), parallel (http://www.gnu.org/software/parallel/), samtools (https://github.com/samtools/samtools).Contact: bammds-users@nongnu.orgSupplementary information: Supplementary data are available at Bioinformatics online.

SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods

With the advance of experimental technologies, different stable isotope labeling methods have been widely applied to quantitative proteomics. Here, we present an efficient tool named SILVER for processing the stable isotope labeling mass spectrometry data. SILVER implements novel methods for quality control of quantification at spectrum, peptide and protein levels, respectively. Several new quantification confidence filters and indices are used to improve the accuracy of quantification results. The performance of SILVER was verified and compared with MaxQuant and Proteome Discoverer using a large-scale dataset and two standard datasets. The results suggest that SILVER shows high accuracy and robustness while consuming much less processing time. Additionally, SILVER provides user-friendly interfaces for parameter setting, result visualization, manual validation and some useful statistics analyses. SILVER and its source codes are freely available under the GNU General Public License v3.0 at http://bioinfo.hupo.org.cn/silver.