Abstract

Within the framework of SENSKIN EU project (grant No. 635844), we have developed a soft capacitive sensor using silicone dielectric elastomer for Structural health monitoring (SHM). This sensor has a thin layer of stretchable silicone dielectric film and soft silicone electrodes as the active layer, which transduces strain signal into the easy-monitored capacitance change. The sensor output is measured as a ration of the sensor’s capacitance to the known value of the reference capacitor. The capacitive output of the sensor showed a linear response upon stretching in the strain range of 4.5 % to 5.5% with a pre-strain of 5.0 %. The sensor output increased by 0.01 when the strain was increased by 1.0 %. The sensor was exposed to a temperature change from -35 oC to 55 oC (the supposed working temperature range) with a ramping of 0.2 oC/min and, as a result, it showed a linear temperature dependence behavior with a slope of -0.001 sensor output per oC. Therefore, with the help of the embedded Platinum-1000 temperature sensor, the thermal expansion effect on the capacitive output can be excluded from the actual strain-induced capacitance change. These results provide practical reference to the application of the silicon-elastomer sensor for the SHM systems.

Abstract

Background: The massive amounts of data from next generation sequencing (NGS) methods pose various challenges with respect to data security, storage and metadata management. While there is a broad range of data analysis pipelines, these challenges remain largely unaddressed to date. Results: We describe the integration of the open-source metadata management system iRODS (Integrated Rule-Oriented Data System) with a cancer genome analysis pipeline in a high performance computing environment. The system allows for customized metadata attributes as well as fine-grained protection rules and is augmented by a user-friendly front-end for metadata input. This results in a robust, efficient end-to-end workflow under consideration of data security, central storage and unified metadata information. Conclusions: Integrating iRODS with an NGS data analysis pipeline is a suitable method for addressing the challenges of data security, storage and metadata management in NGS environments. Document type: Article

Abstract

Background: The analysis of next generation sequencing (NGS) has become a standard task for many laboratories in the life sciences. Though there exists several tools to support users in the manipulation of such datasets on various levels, few are built on the basis of vertical integration. Here, we present the NExt generation Analysis Toolbox (NEAT) that allows non-expert users including wet-lab scientists to comprehensively build, run and analyze NGS data through double-clickable executables without the need of any programming experience. Results: In comparison to many publicly available tools including Galaxy, NEAT provides three main advantages: (1) Through the development of double-clickable executables, NEAT is efficient (completes within "24 hours), easy to implement and intuitive; (2) Storage space, maximum number of job submissions, wall time and cluster-specific parameters can be customized as NEAT is run on the institution’s cluster; (3) NEAT allows users to visualize and summarize NGS data rapidly and efficiently using various built-in exploratory data analysis tools including metagenomic and differentially expressed gene analysis. To simplify the control of the workflow, NEAT projects are built around a unique and centralized file containing sample names, replicates, conditions, antibodies, alignment-, filtering- and peak calling parameters as well as cluster-specific paths and settings. Moreover, the small-sized files produced by NEAT allow users to easily manipulate, consolidate and share datasets from different users and institutions. Conclusions: NEAT provides biologists and bioinformaticians with a robust, efficient and comprehensive tool for the analysis of massive NGS datasets. Frameworks such as NEAT not only allow novice users to overcome the increasing number of technical hurdles due to the complexity of manipulating large datasets, but provide more advance users with tools that ensure high reproducibility standards in the NGS era. NEAT is publically available at https://github.com/pschorderet/NEAT. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0902-3) contains supplementary material, which is available to authorized users. Document type: Article

Abstract

Abstract"/p" "p"Background"/p" "p"In bioinformatics, it is important to build extensible and low-maintenance systems that are able to deal with the new tools and data formats that are constantly being developed. The traditional and simplest implementation of pipelines involves hardcoding the execution steps into programs or scripts. This approach can lead to problems when a pipeline is expanding because the incorporation of new tools is often error prone and time consuming. Current approaches to pipeline development such as workflow management systems focus on analysis tasks that are systematically repeated without significant changes in their course of execution, such as genome annotation. However, more dynamism on the pipeline composition is necessary when each execution requires a different combination of steps."/p" "p"Results"/p" "p"We propose a graph-based approach to implement extensible and low-maintenance pipelines that is suitable for pipeline applications with multiple functionalities that require different combinations of steps in each execution. Here pipelines are composed automatically by compiling a specialised set of tools on demand, depending on the functionality required, instead of specifying every sequence of tools in advance. We represent the connectivity of pipeline components with a directed graph in which components are the graph edges, their inputs and outputs are the graph nodes, and the paths through the graph are pipelines. To that end, we developed special data structures and a pipeline system algorithm. We demonstrate the applicability of our approach by implementing a format conversion pipeline for the fields of population genetics and genetic epidemiology, but our approach is also helpful in other fields where the use of multiple software is necessary to perform comprehensive analyses, such as gene expression and proteomics analyses. The project code, documentation and the Java executables are available under an open source license at "url"http://code.google.com/p/dynamic-pipeline"/url". The system has been tested on Linux and Windows platforms."/p" "p"Conclusions"/p" "p"Our graph-based approach enables the automatic creation of pipelines by compiling a specialised set of tools on demand, depending on the functionality required. It also allows the implementation of extensible and low-maintenance pipelines and contributes towards consolidating openness and collaboration in bioinformatics systems. It is targeted at pipeline developers and is suited for implementing applications with sequential execution steps and combined functionalities. In the format conversion application, the automatic combination of conversion tools increased both the number of possible conversions available to the user and the extensibility of the system to allow for future updates with new file formats. Document type: Article

Abstract

Summary"/jats:title""jats:p"We designed a PyQt graphical user interface – Sequanix – aiming at democratizing the use of Snakemake pipelines. Although the primary goal of Sequanix was to facilitate the execution of NGS Snakemake pipelines available in the Sequana project ("jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://sequana.readthedocs.io""http://sequana.readthedocs.io"/jats:ext-link"), it can also handle any Snakemake pipelines. Therefore, Sequanix should be useful to all Snakemake developers willing to expose their pipelines to a wider audience."/jats:p""/jats:sec""jats:sec""jats:title"Availability"/jats:title""jats:p"Source code available on "jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://github.com/sequana/sequana""http://github.com/sequana/sequana"/jats:ext-link" and standalone on "jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://bioconda.github.io""http://bioconda.github.io"/jats:ext-link" (sequana package)."/jats:p""/jats:sec Document type: Article

Abstract

Background Recent advancements in high-throughput sequencing technologies have generated an unprecedented amount of genomic data that must be stored, processed, and transmitted over the network for sharing. Lossy genomic data compression, especially of the base quality values of sequencing data, is emerging as an efficient way to handle this challenge due to its superior compression performance compared to lossless compression methods. Many lossy compression algorithms have been developed for and evaluated using DNA sequencing data. However, whether these algorithms can be used on RNA sequencing (RNA-seq) data remains unclear. Results In this study, we evaluated the impacts of lossy quality value compression on common RNA-seq data analysis pipelines including expression quantification, transcriptome assembly, and short variants detection using RNA-seq data from different species and sequencing platforms. Our study shows that lossy quality value compression could effectively improve RNA-seq data compression. In some cases, lossy algorithms achieved up to 1.2-3 times further reduction on the overall RNA-seq data size compared to existing lossless algorithms. However, lossy quality value compression could affect the results of some RNA-seq data processing pipelines, and hence its impacts to RNA-seq studies cannot be ignored in some cases. Pipelines using HISAT2 for alignment were most significantly affected by lossy quality value compression, while the effects of lossy compression on pipelines that do not depend on quality values, e.g., STAR-based expression quantification and transcriptome assembly pipelines, were not observed. Moreover, regardless of using either STAR or HISAT2 as the aligner, variant detection results were affected by lossy quality value compression, albeit to a lesser extent when STAR-based pipeline was used. Our results also show that the impacts of lossy quality value compression depend on the compression algorithms being used and the compression levels if the algorithm supports setting of multiple compression levels. Conclusions Lossy quality value compression can be incorporated into existing RNA-seq analysis pipelines to alleviate the data storage and transmission burdens. However, care should be taken on the selection of compression tools and levels based on the requirements of the downstream analysis pipelines to avoid introducing undesirable adverse effects on the analysis results. Document type: Article

Abstract

"jats:sec" "jats:title"Motivation"/jats:title" "jats:p"With its capacity for high-resolution data output in one region of interest, chromosome conformation capture combined with high-throughput sequencing (4C-seq) is a state-of-the-art next-generation sequencing technique that provides epigenetic insights, and regularly advances current medical research. However, 4C-seq data are complex and prone to biases, and while specialized programs exist, an unbiased, extensive benchmarking is still lacking. Furthermore, neither substantial datasets with fully characterized ground truth, nor simulation programs for realistic 4C-seq data have been published."/jats:p" "/jats:sec" "jats:sec" "jats:title"Results"/jats:title" "jats:p"We conducted a benchmarking study on 66 4C-seq samples from 20 datasets, and developed a novel 4C-seq simulation software, Basic4CSim, to allow for detailed comparisons of 4C-seq algorithms on 50 simulated datasets with 10–120 samples each. Simulations and benchmarking were adapted to address different characteristics of 4C-seq data. Simulated data were compared with published samples to validate simulation settings. We identified differences between 4C-seq algorithms in terms of precision, recall, interaction structure, and run time, and observed general trends. Novel differential pipeline versions of single-sample based 4C-seq algorithms were included in the benchmarking. While no single tool was optimally suited for both near-cis and far-cis, and both single-sample and differential analyses, choosing a high-performing algorithm variant did improve results considerably. For near-cis scenarios, r3Cseq, peakC and FourCSeq offered high precision, while fourSig demonstrated high overall F1 scores in far-cis analyses. Finally, 4C-seq simulations may aid in the development of improved analysis algorithms."/jats:p" "/jats:sec" "jats:sec" "jats:title"Availability and implementation"/jats:title" "jats:p"Basic4CSim is available at https://github.com/walter–ca/Basic4CSim."/jats:p" "/jats:sec" "jats:sec" "jats:title"Supplementary information"/jats:title" "jats:p"Supplementary data are available at Bioinformatics online."/jats:p" "/jats:sec Document type: Article

Abstract

The observation that hemodynamic forces play an important role in the pathophysiology of the cardiovascular system has led to the need for characterizing in vivo hemodynamics on a patient-specific basis. However, the introduction of computational hemodynamics in clinical research contexts is bound to the availability of integrated workflows for analyses on large populations. Since such workflows must rely on automated geometry-driven mesh generation methods, the availability of robust solvers featuring adaptive mesh refinement strategies is essential to ensure that the approach can be adopted on a large scale. In this paper, we present an open-source solver for the incompressible Navier–Stokes equations based on the libMesh finite elements library, featuring adaptive mesh refinement and parallelization. The solution scheme is a second-order velocity correction in rotational form. By presenting numerical tests on benchmark cases, we demonstrate that the coupling of this solution strategy with adaptive mesh refinement leads to a solver with good accuracy characteristics despite the relative simplicity of the scheme adopted. The availability of this solver within the Vascular Modeling Toolkit project leads to a widely available, seamless pipeline from images to simulation ready to be applied in clinical research environments. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract

Motivation Next Generation Sequencing (NGS) technology enables identification of microbial genomes from massive amount of human microbiomes more rapidly and cheaper than ever before. However, the traditional sequential genome analysis algorithms, tools, and platforms are inefficient for performing large-scale metagenomic studies on ever-growing sample data volumes. Currently, there is an urgent need for scalable analysis pipelines that enable harnessing all the power of parallel computation in computing clusters and in cloud computing environments. We propose ViraPipe, a scalable metagenome analysis pipeline that is able to analyze thousands of human microbiomes in parallel in tolerable time. The pipeline is tuned for analyzing viral metagenomes and the software is applicable for other metagenomic analyses as well. ViraPipe integrates parallel BWA-MEM read aligner, MegaHit De novo assembler, and BLAST and HMMER3 sequence search tools. We show the scalability of ViraPipe by running experiments on mining virus related genomes from NGS datasets in a distributed Spark computing cluster. Results ViraPipe analyses 768 human samples in 210 minutes on a Spark computing cluster comprising 23 nodes and 1288 cores in total. The speedup of ViraPipe executed on 23 nodes was 11x compared to the sequential analysis pipeline executed on a single node. The whole process includes parallel decompression, read interleaving, BWA-MEM read alignment, filtering and normalizing of non-human reads, De novo contigs assembling, and searching of sequences with BLAST and HMMER3 tools. Contact ilari.maarala@aalto.fi. Availability and implementation https://github.com/NGSeq/ViraPipe.

Abstract

Summary ATAC-seq is rapidly emerging as one of the major experimental approaches to probe chromatin accessibility genome-wide. Here, we present ‘esATAC’, a highly integrated easy-to-use R/Bioconductor package, for systematic ATAC-seq data analysis. It covers essential steps for full analyzing procedure, including raw data processing, quality control and downstream statistical analysis such as peak calling, enrichment analysis and transcription factor footprinting. esATAC supports one command line execution for preset pipelines and provides flexible interfaces for building customized pipelines. Availability and implementation esATAC package is open source under the GPL-3.0 license. It is implemented in R and C++. Source code and binaries for Linux, MAC OS X and Windows are available through Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/esATAC.html). Supplementary information Supplementary data are available at Bioinformatics online. Document type: Article