Open-Source Syringe Pump Library

This article explores a new open-source method for developing and manufacturing high-quality scientific equipment suitable for use in virtually any laboratory. A syringe pump was designed using freely available open-source computer aided design (CAD) software and manufactured using an open-source RepRap 3-D printer and readily available parts. The design, bill of materials and assembly instructions are globally available to anyone wishing to use them. Details are provided covering the use of the CAD software and the RepRap 3-D printer. The use of an open-source Rasberry Pi computer as a wireless control device is also illustrated. Performance of the syringe pump was assessed and the methods used for assessment are detailed. The cost of the entire system, including the controller and web-based control interface, is on the order of 5% or less than one would expect to pay for a commercial syringe pump having similar performance. The design should suit the needs of a given research activity requiring a syringe pump including carefully controlled dosing of reagents, pharmaceuticals, and delivery of viscous 3-D printer media among other applications.     

Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping

High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays ‘Fernandez’) plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.Plant bioinformatics faces the challenge of integrating information from the related “omics” fields to elucidate the functional relationship between genotype and observed phenotype (Edwards and Batley, 2004), known as the genotype-phenotype map (Houle et al., 2010). One of the main obstacles is our currently limited ability of systemic depiction and quantification of plant phenotypes, representing the so-called phenotyping bottleneck phenomenon (Furbank and Tester, 2011). To get a comprehensive genotype-phenotype map, more accurate and precise phenotyping strategies are required to empower high-resolution linkage mapping and genome-wide association studies in order to uncover underlying genetic variants associated with complex phenotypic traits, which aim to improve the efficiency, effectiveness, and economy of cultivars in plant breeding (Cobb et al., 2013). In the era of phenomics, automatic high-throughput phenotyping in a noninvasive manner is applied to identify and quantify plant phenotypic traits. Plants are bred in fully automated greenhouses under predefined environmental conditions with controlled temperature, watering, and humidity. To meet the demand of data access, exchange, and sharing, several phenomics-related projects in the context of several consortia have been launched, such as the International Plant Phenotyping Network (http://www.plantphenomics.com/), the European Plant Phenotyping Network (http://www.plant-phenotyping-network.eu/), and the German Plant Phenotyping Network (http://www.dppn.de/).Thanks to the development of new imaging and transport systems, various automated or semiautomated high-throughput plant phenotyping systems are being developed and used to examine plant function and performance under controlled conditions. PHENOPSIS (Granier et al., 2006) is one of the pioneering platforms that was developed to dissect genotype-environment effects on plant growth in Arabidopsis (Arabidopsis thaliana). GROWSCREEN (Walter et al., 2007; Biskup et al., 2009; Jansen et al., 2009; Nagel et al., 2012) was designed for rapid optical phenotyping of different plant species with respect to different biological aspects. Other systems in the context of high-throughput phenotyping include Phenodyn/Phenoarch (Sadok et al., 2007), TraitMill (Reuzeau et al., 2005; Reuzeau, 2007), Phenoscope (Tisné et al., 2013), RootReader3D (Clark et al., 2011), GROW Map (http://www.fz-juelich.de/ibg/ibg-2/EN/methods_jppc/methods_node.html), and LemnaTec Scanalyzer 3D. These developments enable the phenotyping of specific organs (e.g. leaf, root, and shoot) or of whole plants. Some of them are even used for three-dimensional plant analysis (Clark et al., 2011). Consequently, several specific software applications (a comprehensive list can be found at http://www.phenomics.cn/links.php), such as HYPOTrace (Wang et al., 2009), HTPheno (Hartmann et al., 2011), LAMINA (Bylesjö et al., 2008), PhenoPhyte (Green et al., 2012), Rosette Tracker (De Vylder et al., 2012), LeafAnalyser (Weight et al., 2008), RootNav (Pound et al., 2013), SmartGrain (Tanabata et al., 2012), and LemnaGrid, were designed to extract a wide range of measurements, such as height/length, width, shape, projected area, digital volume, compactness, relative growth rate, and colorimetric analysis.The huge amount of generated image data from various phenotyping systems requires appropriate data management as well as an appropriate analytical framework for data interpretation (Fiorani and Schurr, 2013). However, most of the developed image-analysis tools are designed for a specific task, for specific plant species, or are not freely available to the research community. They lack flexibility in terms of needed adaptations to meet new analysis requirements. For example, it would be desirable that a system could handle imaging data from different sources (either from fully automated high-throughput phenotyping systems or from setups where images are acquired manually), different imaging modalities (fluorescence, near-infrared, and thermal imaging), and/or different species (wheat [Triticum aestivum], barley [Hordeum vulgare], maize [Zea mays], and Arabidopsis).In this work, we present Integrated Analysis Platform (IAP), a scalable open-source framework, for high-throughput plant phenotyping data processing. IAP handles different image sources and helps to organize phenotypic data by retaining the metadata from the input in the result data set. In order to measure phenotypic traits in new or modified setups, users can easily create new analysis pipelines or modify the predefined ones. IAP provides various user-friendly interfaces at different system levels to meet the demands of users (e.g. software developers, bioinformaticians, and biologists) with different experiences in software programming.     

powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions

Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible.     

The OpenPicoAmp: An Open-Source Planar Lipid Bilayer Amplifier for Hands-On Learning of Neuroscience

Understanding the electrical biophysical properties of the cell membrane can be difficult for neuroscience students as it relies solely on lectures of theoretical models without practical hands on experiments. To address this issue, we developed an open-source lipid bilayer amplifier, the OpenPicoAmp, which is appropriate for use in introductory courses in biophysics or neurosciences at the undergraduate level, dealing with the electrical properties of the cell membrane. The amplifier is designed using the common lithographic printed circuit board fabrication process and off-the-shelf electronic components. In addition, we propose a specific design for experimental chambers allowing the insertion of a commercially available polytetrafluoroethylene film. We provide a complete documentation allowing to build the amplifier and the experimental chamber. The students hand-out giving step-by step instructions to perform a recording is also included. Our experimental setup can be used in basic experiments in which students monitor the bilayer formation by capacitance measurement and record unitary currents produced by ionic channels like gramicidin A dimers. Used in combination with a low-cost data acquisition board this system provides a complete solution for hands-on lessons, therefore improving the effectiveness in teaching basic neurosciences or biophysics.     

An Evidence-Based Combining Classifier for Brain Signal Analysis

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.     

STORMSeq: An Open-Source,User-Friendly Pipeline for Processing Personal Genomics Data in the Cloud

The increasing public availability of personal complete genome sequencing data has ushered in an era of democratized genomics. However, read mapping and variant calling software is constantly improving and individuals with personal genomic data may prefer to customize and update their variant calls. Here, we describe STORMSeq (Scalable Tools for Open-Source Read Mapping), a graphical interface cloud computing solution that does not require a parallel computing environment or extensive technical experience. This customizable and modular system performs read mapping, read cleaning, and variant calling and annotation. At present, STORMSeq costs approximately $2 and 5–10 hours to process a full exome sequence and $30 and 3–8 days to process a whole genome sequence. We provide this open-access and open-source resource as a user-friendly interface in Amazon EC2.     

An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries

Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this work, we outline the steps for a naïve user to approach the Geometry-preserving Adaptive MeshER software version 2, a mesh generation code written in C++ designed to convert structural data sets to realistic geometric meshes while preserving the underlying shapes. We present two example cases: 1) mesh generation at the subcellular scale as informed by electron tomography and 2) meshing a protein with a structure from x-ray crystallography. We further demonstrate that the meshes generated by the Geometry-preserving Adaptive MeshER software are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data.     

缅甸蟒线粒体基因组全序列测定与分析

采用LA-PCR(long and accurate PCR)、巢式PCR及TA克隆测序技术,首次获得缅甸蟒Python bivittatus线粒体基因组全序列(GenBank登录号NC_021479)。分析结果表明:缅甸蟒线粒体全长17 617 bp,与其它多数蛇类线粒体基因组结构相似,由13个蛋白编码区、2个rRNA、22个tRNA和双控区组成,基因间排列紧凑;与蟒属其它物种相比,缅甸蟒线粒体在氨基酸数目上存在增减现象;tRNA中tRNA-Cys长度最短,只有57 bp,二氢尿嘧啶环无配对的茎区;缅甸蟒在两个控制区各存在3个相同的串联重复,可能是造成个体间相差87~89 bp的原因。     

The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI

Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses. Our pipeline is written in Python and is freely available as open-source at www.cmtk.org.     

TLM-Quant: An Open-Source Pipeline for Visualization and Quantification of Gene Expression Heterogeneity in Growing Microbial Cells

Gene expression heterogeneity is a key driver for microbial adaptation to fluctuating environmental conditions, cell differentiation and the evolution of species. This phenomenon has therefore enormous implications, not only for life in general, but also for biotechnological applications where unwanted subpopulations of non-producing cells can emerge in large-scale fermentations. Only time-lapse fluorescence microscopy allows real-time measurements of gene expression heterogeneity. A major limitation in the analysis of time-lapse microscopy data is the lack of fast, cost-effective, open, simple and adaptable protocols. Here we describe TLM-Quant, a semi-automatic pipeline for the analysis of time-lapse fluorescence microscopy data that enables the user to visualize and quantify gene expression heterogeneity. Importantly, our pipeline builds on the open-source packages ImageJ and R. To validate TLM-Quant, we selected three possible scenarios, namely homogeneous expression, highly ‘noisy’ heterogeneous expression, and bistable heterogeneous expression in the Gram-positive bacterium Bacillus subtilis. This bacterium is both a paradigm for systems-level studies on gene expression and a highly appreciated biotechnological ‘cell factory’. We conclude that the temporal resolution of such analyses with TLM-Quant is only limited by the numbers of recorded images.     

Open-Source Photometric System for Enzymatic Nitrate Quantification

Nitrate, the most oxidized form of nitrogen, is regulated to protect people and animals from harmful levels as there is a large over abundance due to anthropogenic factors. Widespread field testing for nitrate could begin to address the nitrate pollution problem, however, the Cadmium Reduction Method, the leading certified method to detect and quantify nitrate, demands the use of a toxic heavy metal. An alternative, the recently proposed Environmental Protection Agency Nitrate Reductase Nitrate-Nitrogen Analysis Method, eliminates this problem but requires an expensive proprietary spectrophotometer. The development of an inexpensive portable, handheld photometer will greatly expedite field nitrate analysis to combat pollution. To accomplish this goal, a methodology for the design, development, and technical validation of an improved open-source water testing platform capable of performing Nitrate Reductase Nitrate-Nitrogen Analysis Method. This approach is evaluated for its potential to i) eliminate the need for toxic chemicals in water testing for nitrate and nitrite, ii) reduce the cost of equipment to perform this method for measurement for water quality, and iii) make the method easier to carryout in the field. The device is able to perform as well as commercial proprietary systems for less than 15% of the cost for materials. This allows for greater access to the technology and the new, safer nitrate testing technique.     

DStat: A Versatile,Open-Source Potentiostat for Electroanalysis and Integration

Most electroanalytical techniques require the precise control of the potentials in an electrochemical cell using a potentiostat. Commercial potentiostats function as “black boxes,” giving limited information about their circuitry and behaviour which can make development of new measurement techniques and integration with other instruments challenging. Recently, a number of lab-built potentiostats have emerged with various design goals including low manufacturing cost and field-portability, but notably lacking is an accessible potentiostat designed for general lab use, focusing on measurement quality combined with ease of use and versatility. To fill this gap, we introduce DStat (http://microfluidics.utoronto.ca/dstat), an open-source, general-purpose potentiostat for use alone or integrated with other instruments. DStat offers picoampere current measurement capabilities, a compact USB-powered design, and user-friendly cross-platform software. DStat is easy and inexpensive to build, may be modified freely, and achieves good performance at low current levels not accessible to other lab-built instruments. In head-to-head tests, DStat’s voltammetric measurements are much more sensitive than those of “CheapStat” (a popular open-source potentiostat described previously), and are comparable to those of a compact commercial “black box” potentiostat. Likewise, in head-to-head tests, DStat’s potentiometric precision is similar to that of a commercial pH meter. Most importantly, the versatility of DStat was demonstrated through integration with the open-source DropBot digital microfluidics platform. In sum, we propose that DStat is a valuable contribution to the “open source” movement in analytical science, which is allowing users to adapt their tools to their experiments rather than alter their experiments to be compatible with their tools.     

ScrumPy: metabolic modelling with Python

ScrumPy is a software package used for the definition and analysis of metabolic models. It is written using the Python programming language that is also used as a user interface. ScrumPy has features for both kinetic and structural modelling, but the emphasis is on structural modelling and those features of most relevance to analysis of large (genome-scale) models. The aim is at describing ScrumPy's functionality to readers with some knowledge of metabolic modelling, but implementation, programming and other computational details are omitted. ScrumPy is released under the Gnu Public Licence, and available for download from http://mudshark.brookes.ac.uk/ ScrumPy.     

Sarment: Python modules for HMM analysis and partitioning of sequences

Sarment is a package of Python modules for easy building and manipulation of sequence segmentations. It provides efficient implementation of usual algorithms for hidden Markov Model computation, as well as for maximal predictive partitioning. Owing to its very large variety of criteria for computing segmentations, Sarment can handle many kinds of models. Because of object-oriented programming, the results of the segmentation are very easy tomanipulate.     

Track-A-Worm,An Open-Source System for Quantitative Assessment of C. elegans Locomotory and Bending Behavior

A major challenge of neuroscience is to understand the circuit and gene bases of behavior. C. elegans is commonly used as a model system to investigate how various gene products function at specific tissue, cellular, and synaptic foci to produce complicated locomotory and bending behavior. The investigation generally requires quantitative behavioral analyses using an automated single-worm tracker, which constantly records and analyzes the position and body shape of a freely moving worm at a high magnification. Many single-worm trackers have been developed to meet lab-specific needs, but none has been widely implemented for various reasons, such as hardware difficult to assemble, and software lacking sufficient functionality, having closed source code, or using a programming language that is not broadly accessible. The lack of a versatile system convenient for wide implementation makes data comparisons difficult and compels other labs to develop new worm trackers. Here we describe Track-A-Worm, a system rich in functionality, open in source code, and easy to use. The system includes plug-and-play hardware (a stereomicroscope, a digital camera and a motorized stage), custom software written to run with Matlab in Windows 7, and a detailed user manual. Grayscale images are automatically converted to binary images followed by head identification and placement of 13 markers along a deduced spline. The software can extract and quantify a variety of parameters, including distance traveled, average speed, distance/time/speed of forward and backward locomotion, frequency and amplitude of dominant bends, overall bending activities measured as root mean square, and sum of all bends. It also plots worm travel path, bend trace, and bend frequency spectrum. All functionality is performed through graphical user interfaces and data is exported to clearly-annotated and documented Excel files. These features make Track-A-Worm a good candidate for implementation in other labs.     

SNPer: An R Library for Quantitative Variant Analysis on Single Nucleotide Polymorphisms among Influenza Virus Populations

Influenza virus (IFV) can evolve rapidly leading to genetic drifts and shifts resulting in human and animal influenza epidemics and pandemics. The genetic shift that gave rise to the 2009 influenza A/H1N1 pandemic originated from a triple gene reassortment of avian, swine and human IFVs. More minor genetic alterations in genetic drift can lead to influenza drug resistance such as the H274Y mutation associated with oseltamivir resistance. Hence, a rapid tool to detect IFV mutations and the potential emergence of new virulent strains can better prepare us for seasonal influenza outbreaks as well as potential pandemics. Furthermore, identification of specific mutations by closely examining single nucleotide polymorphisms (SNPs) in IFV sequences is essential to classify potential genetic markers associated with potentially dangerous IFV phenotypes. In this study, we developed a novel R library called “SNPer” to analyze quantitative variants in SNPs among IFV subpopulations. The computational SNPer program was applied to three different subpopulations of published IFV genomic information. SNPer queried SNPs data and grouped the SNPs into (1) universal SNPs, (2) likely common SNPs, and (3) unique SNPs. SNPer outperformed manual visualization in terms of time and labor. SNPer took only three seconds with no errors in SNP comparison events compared with 40 hours with errors using manual visualization. The SNPer tool can accelerate the capacity to capture new and potentially dangerous IFV strains to mitigate future influenza outbreaks.