AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery.

AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier maps.

Workflows for microarray data processing in the Kepler environment.

BACKGROUND: Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data sources. Bioinformatics pipelines are usually custom built for different applications, making them typically difficult to modify, extend and repurpose. Scientific workflow systems are intended to address these issues by providing general-purpose frameworks in which to develop and execute such pipelines. The Kepler workflow environment is a well-established system under continual development that is employed in several areas of scientific research. Kepler provides a flexible graphical interface, featuring clear display of parameter values, for design and modification of workflows. It has capabilities for developing novel computational components in the R, Python, and Java programming languages, all of which are widely used for bioinformatics algorithm development, along with capabilities for invoking external applications and using web services. RESULTS: We developed a series of fully functional bioinformatics pipelines addressing common tasks in microarray processing in the Kepler workflow environment. These pipelines consist of a set of tools for GFF file processing of NimbleGen chromatin immunoprecipitation on microarray (ChIP-chip) datasets and more comprehensive workflows for Affymetrix gene expression microarray bioinformatics and basic primer design for PCR experiments, which are often used to validate microarray results. Although functional in themselves, these workflows can be easily customized, extended, or repurposed to match the needs of specific projects and are designed to be a toolkit and starting point for specific applications. These workflows illustrate a workflow programming paradigm focusing on local resources (programs and data) and therefore are close to traditional shell scripting or R/BioConductor scripting approaches to pipeline design. Finally, we suggest that microarray data processing task workflows may provide a basis for future example-based comparison of different workflow systems. CONCLUSIONS: We provide a set of tools and complete workflows for microarray data analysis in the Kepler environment, which has the advantages of offering graphical, clear display of conceptual steps and parameters and the ability to easily integrate other resources such as remote data and web services.

Sole-Search: an integrated analysis program for peak detection and functional annotation using ChIP-seq data.

Next-generation sequencing is revolutionizing the identification of transcription factor binding sites throughout the human genome. However, the bioinformatics analysis of large datasets collected using chromatin immunoprecipitation and high-throughput sequencing is often a roadblock that impedes researchers in their attempts to gain biological insights from their experiments. We have developed integrated peak-calling and analysis software (Sole-Search) which is available through a user-friendly interface and (i) converts raw data into a format for visualization on a genome browser, (ii) outputs ranked peak locations using a statistically based method that overcomes the significant problem of false positives, (iii) identifies the gene nearest to each peak, (iv) classifies the location of each peak relative to gene structure, (v) provides information such as the number of binding sites per chromosome and per gene and (vi) allows the user to determine overlap between two different experiments. In addition, the program performs an analysis of amplified and deleted regions of the input genome. This software is web-based and automated, allowing easy and immediate access to all investigators. We demonstrate the utility of our software by collecting, analyzing and comparing ChIP-seq data for six different human transcription factors/cell line combinations.

Introducing W.A.T.E.R.S.: a workflow for the alignment, taxonomy, and ecology of ribosomal sequences.

BACKGROUND: For more than two decades microbiologists have used a highly conserved microbial gene as a phylogenetic marker for bacteria and archaea. The small-subunit ribosomal RNA gene, also known as 16 S rRNA, is encoded by ribosomal DNA, 16 S rDNA, and has provided a powerful comparative tool to microbial ecologists. Over time, the microbial ecology field has matured from small-scale studies in a select number of environments to massive collections of sequence data that are paired with dozens of corresponding collection variables. As the complexity of data and tool sets have grown, the need for flexible automation and maintenance of the core processes of 16 S rDNA sequence analysis has increased correspondingly. RESULTS: We present WATERS, an integrated approach for 16 S rDNA analysis that bundles a suite of publicly available 16 S rDNA analysis software tools into a single software package. The "toolkit" includes sequence alignment, chimera removal, OTU determination, taxonomy assignment, phylogentic tree construction as well as a host of ecological analysis and visualization tools. WATERS employs a flexible, collection-oriented 'workflow' approach using the open-source Kepler system as a platform. CONCLUSIONS: By packaging available software tools into a single automated workflow, WATERS simplifies 16 S rDNA analyses, especially for those without specialized bioinformatics, programming expertise. In addition, WATERS, like some of the newer comprehensive rRNA analysis tools, allows researchers to minimize the time dedicated to carrying out tedious informatics steps and to focus their attention instead on the biological interpretation of the results. One advantage of WATERS over other comprehensive tools is that the use of the Kepler workflow system facilitates result interpretation and reproducibility via a data provenance sub-system. Furthermore, new "actors" can be added to the workflow as desired and we see WATERS as an initial seed for a sizeable and growing repository of interoperable, easy-to-combine tools for asking increasingly complex microbial ecology questions.

New paradigm for macromolecular crystallography experiments at SSRL: automated crystal screening and remote data collection.

Complete automation of the macromolecular crystallography experiment has been achieved at SSRL through the combination of robust mechanized experimental hardware and a flexible control system with an intuitive user interface. These highly reliable systems have enabled crystallography experiments to be carried out from the researchers' home institutions and other remote locations while retaining complete control over even the most challenging systems. A breakthrough component of the system, the Stanford Auto-Mounter (SAM), has enabled the efficient mounting of cryocooled samples without human intervention. Taking advantage of this automation, researchers have successfully screened more than 200 000 samples to select the crystals with the best diffraction quality for data collection as well as to determine optimal crystallization and cryocooling conditions. These systems, which have been deployed on all SSRL macromolecular crystallography beamlines and several beamlines worldwide, are used by more than 80 research groups in remote locations, establishing a new paradigm for macromolecular crystallography experimentation.