CellProfiler: image analysis software for identifying and quantifying cell phenotypes

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).     

Automatic analysis of silver-stained comets by CellProfiler software

The comet assay is one of the most widely used methods to evaluate DNA damage and repair in eukaryotic cells. The comets can be measured by software, in a semi-automatic or automatic process. In this paper, we apply the CellProfiler open-source software for automatic analysis of comets from digitized images, reporting the percentage of tail DNA. A side-by-side comparison of CellProfiler with CASP software demonstrated good agreement between the two packages. Our work demonstrates that automatic measurement of silver-stained comets with open-source software is possible, providing significant time savings.     

CellProfiler: free, versatile software for automated biological image analysis

Careful visual examination of biological samples is quite powerful, but many visual analysis tasks done in the laboratory are repetitive, tedious, and subjective. Here we describe the use of the open-source software, CellProfiler, to automatically identify and measure a variety of biological objects in images. The applications demonstrated here include yeast colony counting and classifying, cell microarray annotation, yeast patch assays, mouse tumor quantification, wound healing assays, and tissue topology measurement. The software automatically identifies objects in digital images, counts them, and records a full spectrum of measurements for each object, including location within the image, size, shape, color intensity, degree of correlation between colors, texture (smoothness), and number of neighbors. Small numbers of images can be processed automatically on a personal computer and hundreds of thousands can be analyzed using a computing cluster. This free, easy-to-use software enables biologists to comprehensively and quantitatively address many questions that previously would have required custom programming, thereby facilitating discovery in a variety of biological fields of study.     

Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software

There is a strong and growing need in the biology research community for accurate, automated image analysis. Here, we describe CellProfiler 2.0, which has been engineered to meet the needs of its growing user base. It is more robust and user friendly, with new algorithms and features to facilitate high-throughput work. ImageJ plugins can now be run within a CellProfiler pipeline. AVAILABILITY AND IMPLEMENTATION: CellProfiler 2.0 is free and open source, available at http://www.cellprofiler.org under the GPL v. 2 license. It is available as a packaged application for Macintosh OS X and Microsoft Windows and can be compiled for Linux. CONTACT: anne@broadinstitute.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Mollib: a molecular and NMR data analysis software

Mollib is a software framework for the analysis of molecular structures, properties and data with an emphasis on data collected by NMR. It uses an open source model and a plugin framework to promote community-driven development of new and enhanced features. Mollib includes tools for the automatic retrieval and caching of protein databank (PDB) structures, the hydrogenation of biomolecules, the analysis of backbone dihedral angles and hydrogen bonds, and the fitting of residual dipolar coupling (RDC) and residual anisotropic chemical shift (RACS) data. In this article, we release version 1.0 of mollib and demonstrate its application to common molecular and NMR data analyses.     

GGT 2.0: versatile software for visualization and analysis of genetic data

Ever since its first release in 1999, the free software package for visualization of molecular marker data, graphical genotype (GGT), has been constantly adapted and improved. The GGT package was developed in a plant-breeding context and thus focuses on plant genetic data but was not intended to be limited to plants only. The current version has many options for genetic analysis of populations including diversity analyses and simple association studies. A second release of the GGT package, GGT 2.0 (available through http://www.plantbreeding.wur.nl), is therefore presented in this paper. An overview of existing and new features that are available within GGT 2.0, and a case study in which GGT 2.0 is applied to analyze an existing set of plant genetic data, are presented and discussed.     

SNPTools: a software tool for visualization and analysis of microarray data

SUMMARY: We have created a software tool, SNPTools, for analysis and visualization of microarray data, mainly SNP array data. The software can analyse and find differences in intensity levels between groups of arrays and identify segments of SNPs (genes, clones), where the intensity levels differ significantly between the groups. In addition, SNPTools can show jointly loss-of-heterozygosity (LOH) data (derived from genotypes) and intensity data for paired samples of tumour and normal arrays. The output graphs can be manipulated in various ways to modify and adjust the layout. A wizard allows options and parameters to be changed easily and graphs replotted. All output can be saved in various formats, and also re-opened in SNPTools for further analysis. For explorative use, SNPTools allows various genome information to be loaded onto the graphs. AVAILABILITY: The software, example data sets and tutorials are freely available from http://www.birc.au.dk/snptools     

DAMBE: software package for data analysis in molecular biology and evolution

DAMBE (data analysis in molecular biology and evolution) is an integrated software package for converting, manipulating, statistically and graphically describing, and analyzing molecular sequence data with a user-friendly Windows 95/98/2000/NT interface. DAMBE is free and can be downloaded from http://web.hku.hk/~xxia/software/software.htm. The current version is 4.0.36.     

T-REX: software for the processing and analysis of T-RFLP data

Background  

Despite increasing popularity and improvements in terminal restriction fragment length polymorphism (T-RFLP) and other microbial community fingerprinting techniques, there are still numerous obstacles that hamper the analysis of these datasets. Many steps are required to process raw data into a format ready for analysis and interpretation. These steps can be time-intensive, error-prone, and can introduce unwanted variability into the analysis. Accordingly, we developed T-REX, free, online software for the processing and analysis of T-RFLP data.     

BAMarray™: Java software for Bayesian analysis of variance for microarray data

Background  

DNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also poses a challenge to data analysis. Inferential problems become even more pronounced as experimental designs used to collect data become more complex. An important example is multigroup data collected over different experimental groups, such as data collected from distinct stages of a disease process. We have developed a method specifically addressing these issues termed Bayesian ANOVA for microarrays (BAM). The BAM approach uses a special inferential regularization known as spike-and-slab shrinkage that provides an optimal balance between total false detections and total false non-detections. This translates into more reproducible differential calls. Spike and slab shrinkage is a form of regularization achieved by using information across all genes and groups simultaneously.     

Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis

In metabolomics, the rapid identification of quantitative differences between multiple biological samples remains a major challenge. While capillary electrophoresis–mass spectrometry (CE–MS) is a powerful tool to simultaneously quantify charged metabolites, reliable and easy-to-use software that is well suited to analyze CE–MS metabolic profiles is still lacking. Optimized software tools for CE–MS are needed because of the sometimes large variation in migration time between runs and the wider variety of peak shapes in CE–MS data compared with LC–MS or GC–MS. Therefore, we implemented a stand-alone application named JDAMP (Java application for Differential Analysis of Metabolite Profiles), which allows users to identify the metabolites that vary between two groups. The main features include fast calculation modules and a file converter using an original compact file format, baseline subtraction, dataset normalization and alignment, visualization on 2D plots (m/z and time axis) with matching metabolite standards, and the detection of significant differences between metabolite profiles. Moreover, it features an easy-to-use graphical user interface that requires only a few mouse-actions to complete the analysis. The interface also enables the analyst to evaluate the semiautomatic processes and interactively tune options and parameters depending on the input datasets. The confirmation of findings is available as a list of overlaid electropherograms, which is ranked using a novel difference-evaluation function that accounts for peak size and distortion as well as statistical criteria for accurate difference-detection. Overall, the JDAMP software complements other metabolomics data processing tools and permits easy and rapid detection of significant differences between multiple complex CE–MS profiles.     

IOCBIO Kinetics: An open-source software solution for analysis of data traces

Biological measurements frequently involve measuring parameters as a function of time, space, or frequency. Later, during the analysis phase of the study, the researcher splits the recorded data trace into smaller sections, analyzes each section separately by finding a mean or fitting against a specified function, and uses the analysis results in the study. Here, we present the software that allows to analyze these data traces in a manner that ensures repeatability of the analysis and simplifies the application of FAIR (findability, accessibility, interoperability, and reusability) principles in such studies. At the same time, it simplifies the routine data analysis pipeline and gives access to a fast overview of the analysis results. For that, the software supports reading the raw data, processing the data as specified in the protocol, and storing all intermediate results in the laboratory database. The software can be extended by study- or hardware-specific modules to provide the required data import and analysis facilities. To simplify the development of the data entry web interfaces, that can be used to enter data describing the experiments, we released a web framework with an example implementation of such a site. The software is covered by open-source license and is available through several online channels.     

Open source software for the analysis of microarray data

DNA microarray assays represent the first widely used application that attempts to build upon the information provided by genome projects in the study of biological questions. One of the greatest challenges with working with microarrays is collecting, managing, and analyzing data. Although several commercial and noncommercial solutions exist, there is a growing body of freely available, open source software that allows users to analyze data using a host of existing techniques and to develop their own and integrate them within the system. Here we review three of the most widely used and comprehensive systems, the statistical analysis tools written in R through the Bioconductor project (http://www.bioconductor.org), the Java-based TM4 software system available from The Institute for Genomic Research (http://www.tigr.org/software), and BASE, the Web-based system developed at Lund University (http://base.thep.lu.se).     

proseq: A software for preparation and evolutionary analysis of DNA sequence data sets

proseq is an integrated user‐friendly windows based program for convenient sequence editing and evolutionary analysis. It is designed to simplify preparation and analysis of DNA sequence data sets in population genetic, phylogenetic and molecular ecology studies. Sequence editor features include editing of chromatogram files, contig assembly, sequence alignment, translation and other utilities. Analysis features include calculation of genetic diversity, divergence, population subdivision and gene flow with permutation‐based significance testing and various tests of neutrality. A tool for coalescent simulations implements models with intragenic recombination, population subdivision and population growth.     

iLAP: a workflow-driven software for experimental protocol development, data acquisition and analysis

Background  

In recent years, the genome biology community has expended considerable effort to confront the challenges of managing heterogeneous data in a structured and organized way and developed laboratory information management systems (LIMS) for both raw and processed data. On the other hand, electronic notebooks were developed to record and manage scientific data, and facilitate data-sharing. Software which enables both, management of large datasets and digital recording of laboratory procedures would serve a real need in laboratories using medium and high-throughput techniques.     

HTS-Corrector: software for the statistical analysis and correction of experimental high-throughput screening data

MOTIVATION: High-throughput screening (HTS) plays a central role in modern drug discovery, allowing for testing of >100,000 compounds per screen. The aim of our work was to develop and implement methods for minimizing the impact of systematic error in the analysis of HTS data. To the best of our knowledge, two new data correction methods included in HTS-Corrector are not available in any existing commercial software or freeware. RESULTS: This paper describes HTS-Corrector, a software application for the analysis of HTS data, detection and visualization of systematic error, and corresponding correction of HTS signals. Three new methods for the statistical analysis and correction of raw HTS data are included in HTS-Corrector: background evaluation, well correction and hit-sigma distribution procedures intended to minimize the impact of systematic errors. We discuss the main features of HTS-Corrector and demonstrate the benefits of the algorithms.     

Development of public toxicogenomics software for microarray data management and analysis

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarray, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack. ArrayTrack is Minimum Information About a Microarray Experiment (MIAME) supportive for storing both microarray data and experiment parameters associated with a toxicogenomics study. A quality control mechanism is implemented to assure the fidelity of entered expression data. ArrayTrack also provides a rich collection of functional information about genes, proteins and pathways drawn from various public biological databases for facilitating data interpretation. In addition, several data analysis and visualization tools are available with ArrayTrack, and more tools will be available in the next released version. Importantly, gene expression data, functional information and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. ArrayTrack is publicly available online and the prospective user can also request a local installation version by contacting the authors.     

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

Various approaches are used to analyze high-throughput phenotyping data and tools can be developed and assessed using available image-based datasets.