caCORE: a common infrastructure for cancer informatics

MOTIVATION:Sites with substantive bioinformatics operations are challenged to build data processing and delivery infrastructure that provides reliable access and enables data integration. Locally generated data must be processed and stored such that relationships to external data sources can be presented. Consistency and comparability across data sets requires annotation with controlled vocabularies and, further, metadata standards for data representation. Programmatic access to the processed data should be supported to ensure the maximum possible value is extracted. Confronted with these challenges at the National Cancer Institute Center for Bioinformatics, we decided to develop a robust infrastructure for data management and integration that supports advanced biomedical applications. RESULTS: We have developed an interconnected set of software and services called caCORE. Enterprise Vocabulary Services (EVS) provide controlled vocabulary, dictionary and thesaurus services. The Cancer Data Standards Repository (caDSR) provides a metadata registry for common data elements. Cancer Bioinformatics Infrastructure Objects (caBIO) implements an object-oriented model of the biomedical domain and provides Java, Simple Object Access Protocol and HTTP-XML application programming interfaces. caCORE has been used to develop scientific applications that bring together data from distinct genomic and clinical science sources. AVAILABILITY: caCORE downloads and web interfaces can be accessed from links on the caCORE web site (http://ncicb.nci.nih.gov/core). caBIO software is distributed under an open source license that permits unrestricted academic and commercial use. Vocabulary and metadata content in the EVS and caDSR, respectively, is similarly unrestricted, and is available through web applications and FTP downloads. SUPPLEMENTARY INFORMATION: http://ncicb.nci.nih.gov/core/publications contains links to the caBIO 1.0 class diagram and the caCORE 1.0 Technical Guide, which provide detailed information on the present caCORE architecture, data sources and APIs. Updated information appears on a regular basis on the caCORE web site (http://ncicb.nci.nih.gov/core).     

Development of a cloud-based platform for reproducible science: A case study of an IUCN Red List of Ecosystems Assessment

One of the challenges of computational-centric research is to make the research undertaken reproducible in a form that others can repeat and re-use with minimal effort. In addition to the data and tools necessary to re-run analyses, execution environments play crucial roles because of the dependencies of the operating system and software version used. However, some of the challenges of reproducible science can be addressed using appropriate computational tools and cloud computing to provide an execution environment.Here, we demonstrate the use of a Kepler scientific workflow for reproducible science that is sharable, reusable, and re-executable. These workflows reduce barriers to sharing and will save researchers time when undertaking similar research in the future.To provide infrastructure that enables reproducible science, we have developed cloud-based Collaborative Environment for Ecosystem Science Research and Analysis (CoESRA) infrastructure to build, execute and share sophisticated computation-centric research. The CoESRA provides users with a storage and computational platform that is accessible from a web-browser in the form of a virtual desktop. Any registered user can access the virtual desktop to build, execute and share the Kepler workflows. This approach will enable computational scientists to share complete workflows in a pre-configured environment so that others can reproduce the computational research with minimal effort.As a case study, we developed and shared a complete IUCN Red List of Ecosystems Assessment workflow that reproduces the assessments undertaken by Burns et al. (2015) on Mountain Ash forests in the Central Highlands of Victoria, Australia. This workflow provides an opportunity for other researchers and stakeholders to run this assessment with minimal supervision. The workflow also enables researchers to re-evaluate the assessment when additional data becomes available. The assessment can be run in a CoESRA virtual desktop by opening a workflow in a Kepler user interface and pressing a “start” button. The workflow is pre-configured with all the open access datasets and writes results to a pre-configured folder.     

caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid

MOTIVATION: The complexity of cancer is prompting researchers to find new ways to synthesize information from diverse data sources and to carry out coordinated research efforts that span multiple institutions. There is a need for standard applications, common data models, and software infrastructure to enable more efficient access to and sharing of distributed computational resources in cancer research. To address this need the National Cancer Institute (NCI) has initiated a national-scale effort, called the cancer Biomedical Informatics Grid (caBIGtrade mark), to develop a federation of interoperable research information systems. RESULTS: At the heart of the caBIG approach to federated interoperability effort is a Grid middleware infrastructure, called caGrid. In this paper we describe the caGrid framework and its current implementation, caGrid version 0.5. caGrid is a model-driven and service-oriented architecture that synthesizes and extends a number of technologies to provide a standardized framework for the advertising, discovery, and invocation of data and analytical resources. We expect caGrid to greatly facilitate the launch and ongoing management of coordinated cancer research studies involving multiple institutions, to provide the ability to manage and securely share information and analytic resources, and to spur a new generation of research applications that empower researchers to take a more integrative, trans-domain approach to data mining and analysis. AVAILABILITY: The caGrid version 0.5 release can be downloaded from https://cabig.nci.nih.gov/workspaces/Architecture/caGrid/. The operational test bed Grid can be accessed through the client included in the release, or through the caGrid-browser web application http://cagrid-browser.nci.nih.gov.     

Armadillo 1.1: an original workflow platform for designing and conducting phylogenetic analysis and simulations

In this paper we introduce Armadillo v1.1, a novel workflow platform dedicated to designing and conducting phylogenetic studies, including comprehensive simulations. A number of important phylogenetic and general bioinformatics tools have been included in the first software release. As Armadillo is an open-source project, it allows scientists to develop their own modules as well as to integrate existing computer applications. Using our workflow platform, different complex phylogenetic tasks can be modeled and presented in a single workflow without any prior knowledge of programming techniques. The first version of Armadillo was successfully used by professors of bioinformatics at Université du Quebec à Montreal during graduate computational biology courses taught in 2010-11. The program and its source code are freely available at: .     

I3:A Self-organising Learning Workflow for Intuitive Integrative Interpretation of Complex Genetic Data

We propose a computational workflow(I3) for intuitive integrative interpretation of complex genetic data mainly building on the self-organising principle.We illustrate the use in interpreting genetics of gene expression and understanding genetic regulators of protein phenotypes,particularly in conjunction with information from human population genetics and/or evolutionary history of human genes.We reveal that loss-of-function intolerant genes tend to be depleted of tissue-sharing genetics of gene expression in brains,and if highly expressed,have broad effects on the protein phenotypes studied.We suggest that this workflow presents a general solution to the challenge of complex genetic data interpretation.I3 is available at http://suprahex.r-forge.r-project.org/I3.html.     

MADGE: scalable distributed data management software for cDNA microarrays

MOTIVATION: The human genome project and the development of new high-throughput technologies have created unparalleled opportunities to study the mechanism of diseases, monitor the disease progression and evaluate effective therapies. Gene expression profiling is a critical tool to accomplish these goals. The use of nucleic acid microarrays to assess the gene expression of thousands of genes simultaneously has seen phenomenal growth over the past five years. Although commercial sources of microarrays exist, investigators wanting more flexibility in the genes represented on the array will turn to in-house production. The creation and use of cDNA microarrays is a complicated process that generates an enormous amount of information. Effective data management of this information is essential to efficiently access, analyze, troubleshoot and evaluate the microarray experiments. RESULTS: We have developed a distributable software package designed to track and store the various pieces of data generated by a cDNA microarray facility. This includes the clone collection storage data, annotation data, workflow queues, microarray data, data repositories, sample submission information, and project/investigator information. This application was designed using a 3-tier client server model. The data access layer (1st tier) contains the relational database system tuned to support a large number of transactions. The data services layer (2nd tier) is a distributed COM server with full database transaction support. The application layer (3rd tier) is an internet based user interface that contains both client and server side code for dynamic interactions with the user. AVAILABILITY: This software is freely available to academic institutions and non-profit organizations at http://www.genomics.mcg.edu/niddkbtc.     

Transparent mediation-based access to multiple yeast data sources using an ontology driven interface

Background

Over the past decade the workflow system paradigm has evolved as an efficient and user-friendly approach for developing complex bioinformatics applications. Two popular workflow systems that have gained acceptance by the bioinformatics community are Taverna and Galaxy. Each system has a large user-base and supports an ever-growing repository of application workflows. However, workflows developed for one system cannot be imported and executed easily on the other. The lack of interoperability is due to differences in the models of computation, workflow languages, and architectures of both systems. This lack of interoperability limits sharing of workflows between the user communities and leads to duplication of development efforts.

Results

In this paper, we present Tavaxy, a stand-alone system for creating and executing workflows based on using an extensible set of re-usable workflow patterns. Tavaxy offers a set of new features that simplify and enhance the development of sequence analysis applications: It allows the integration of existing Taverna and Galaxy workflows in a single environment, and supports the use of cloud computing capabilities. The integration of existing Taverna and Galaxy workflows is supported seamlessly at both run-time and design-time levels, based on the concepts of hierarchical workflows and workflow patterns. The use of cloud computing in Tavaxy is flexible, where the users can either instantiate the whole system on the cloud, or delegate the execution of certain sub-workflows to the cloud infrastructure.

Conclusions

Tavaxy reduces the workflow development cycle by introducing the use of workflow patterns to simplify workflow creation. It enables the re-use and integration of existing (sub-) workflows from Taverna and Galaxy, and allows the creation of hybrid workflows. Its additional features exploit recent advances in high performance cloud computing to cope with the increasing data size and complexity of analysis. The system can be accessed either through a cloud-enabled web-interface or downloaded and installed to run within the user's local environment. All resources related to Tavaxy are available at http://www.tavaxy.org.     

A Laboratory Information Management System (LIMS) for a high throughput genetic platform aimed at candidate gene mutation screening

High throughput mutation screening in an automated environment generates large data sets that have to be organized and stored reliably. Complex multistep workflows require strict process management and careful data tracking. We have developed a Laboratory Information Management Systems (LIMS) tailored to high throughput candidate gene mutation scanning and resequencing that respects these requirements. Designed with a client/server architecture, our system is platform independent and based on open-source tools from the database to the web application development strategy. Flexible, expandable and secure, the LIMS, by communicating with most of the laboratory instruments and robots, tracks samples and laboratory information, capturing data at every step of our automated mutation screening workflow. An important feature of our LIMS is that it enables tracking of information through a laboratory workflow where the process at one step is contingent on results from a previous step. AVAILABILITY: Script for MySQL database table creation and source code of the whole JSP application are freely available on our website: http://www-gcs.iarc.fr/lims/. SUPPLEMENTARY INFORMATION: System server configuration, database structure and additional details on the LIMS and the mutation screening workflow are available on our website: http://www-gcs.iarc.fr/lims/     

GWIDD: a comprehensive resource for genome-wide structural modeling of protein-protein interactions

We live in an age of access to more information than ever before. This can be a double-edged sword. Increased access to information allows for more informed and empowered researchers, while information overload becomes an increasingly serious risk. Thus, there is a need for intelligent information retrieval systems that can summarize relevant and reliable textual sources to satisfy a user's query. Question answering is a specialized type of information retrieval with the aim of returning precise short answers to queries posed as natural language questions. We present a review and comparison of three biomedical question answering systems: askHERMES (http://www.askhermes.org/), EAGLi (http://eagl.unige.ch/EAGLi/), and HONQA (http://services.hon.ch/cgi-bin/QA10/qa.pl).     

Response Surface Modelling for Performance Analysis of Scientific Workflow Application in Cloud

Scientific workflow applications are used by scientists to carry out research in various domains such as Physics, Chemistry, Astronomy etc. These applications require huge computational resources and currently cloud platform is used for efficiently running these applications. To improve the makespan and cost in workflow execution in cloud platform it requires to identify proper number of Virtual Machines (VM) and choose proper VM type. As cloud platform is dynamic, the available resources and the type of the resources are the two important factors on the cost and makespan of workflow execution. The primary objective of this work is to analyze the relationship among the cloud configuration parameters (Number of VM, Type of VM, VM configurations) for executing scientific workflow applications in cloud platform. In this work, to accurately analyze the influence of cloud platform resource configuration and scheduling polices a new predictive modelling using Box–Behnken design which is one of the modelling technique of Response Surface Methodology (RSM). It is used to build quadratic mathematical models that can be used to analyze relationships among input and output variables. Workflow cost and makespan models were built for real world scientific workflows using ANOVA and it was observed that the models fit well and can be useful in analyzing the performance of scientific workflow applications in cloud

     

An integrated system for high throughput TaqMan based SNP genotyping.

We have developed an integrated laboratory information system that allows the flexible handling of pedigree, phenotype and genotype information. Specifically, it includes client applications for an integrated data import from TaqMan typing files, Mendel checking, data export, handling of pedigree and phenotype information and analysis features. AVAILABILITY: The SQL source code, sources and binaries of the client applications (NT and Windows95/98 platforms) and additional documentation are available at http://www.mucosa.de/.     

Machine learning approaches in MALDI-MSI: clinical applications

Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.

Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing.

Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.     

Service-oriented execution model supporting data sharing and adaptive query processing

To deal with the environment’s heterogeneity, information providers usually offer access to their data by publishing Web services in the domain of pervasive computing. Therefore, to support applications that need to combine data from a diverse range of sources, pervasive computing requires a middleware to query multiple Web services. There exist works that have been investigating on generating optimal query plans. We however in this paper propose a query execution model, called PQModel, to optimize the process of query execution over Web Services. In other words, we attempt to improve query efficiency from the aspect of optimizing the execution processing of query plans.     

SWEET-DB: an attempt to create annotated data collections for carbohydrates

Complex carbohydrates are known as mediators of complex cellular events. Concerning their structural diversity, their potential of information content is several orders of magnitude higher in a short sequence than any other biological macromolecule. SWEET-DB (http://www.dkfz.de/spec2/sweetdb/) is an attempt to use modern web techniques to annotate and/or cross-reference carbohydrate-related data collections which allow glycoscientists to find important data for compounds of interest in a compact and well-structured representation. Currently, reference data taken from three data sources can be retrieved for a given carbohydrate (sub)structure. The sources are CarbBank structures and literature references (linked to NCBI PubMed service), NMR data taken from SugaBase and 3D co-ordinates generated with SWEET-II. The main purpose of SWEET-DB is to enable an easy access to all data stored for one carbohydrate structure entering a complete sequence or parts thereof. Access to SWEET-DB contents is provided with the help of separate input spreadsheets for (sub)structures, bibliographic data, general structural data like molecular weight, NMR spectra and biological data. A detailed online tutorial is available at http://www.dkfz.de/spec2/sweetdb/nar/.     

Automatically Detecting Workflows in PubChem

Public databases that store the data from small-molecule screens are a rich and untapped resource of chemical and biological information. However, screening databases are unorganized, which makes interpreting their data difficult. We propose a method of inferring workflow graphs-which encode the relationships between assays in screening projects-directly from screening data and using these workflows to organize each project's data. On the basis of four heuristics regarding the organization of screening projects, we designed an algorithm that extracts a project's workflow graph from screening data. Where possible, the algorithm is evaluated by comparing each project's inferred workflow to its documentation. In the majority of cases, there are no discrepancies between the two. Most errors can be traced to points in the project where screeners chose additional molecules to test based on structural similarity to promising molecules, a case our algorithm is not yet capable of handling. Nonetheless, these workflows accurately organize most of the data and also provide a method of visualizing a screening project. This method is robust enough to build a workflow-oriented front-end to PubChem and is currently being used regularly by both our lab and our collaborators. A Python implementation of the algorithm is available online, and a searchable database of all PubChem workflows is available at http://swami.wustl.edu/flow.     

An Integrated Workflow for DNA Methylation Analysis

The analysis of cytosine methylation provides a new way to assess and describe epigenetic regulation at a whole-genome level in many eukaryotes. DNA methylation has a demonstrated role in the genome stability and protection, regulation of gene expression and many other aspects of genome function and maintenance. BS-seq is a relatively unbiased method for profiling the DNA methylation, with a resolution capable of measuring methylation at individual cytosines. Here we describe, as an example, a workflow to handle DNA methylation analysis, from BS-seq library preparation to the data visualization. We describe some applications for the analysis and interpretation of these data. Our laboratory provides public access to plant DNA methylation data via visualization tools available at our “Next-Gen Sequence” websites (http://mpss.udel.edu), along with small RNA, RNA-seq and other data types.     

Integrating ARC grid middleware with Taverna workflows

Summary: This work presents two independent approaches for aseamless integration of computational grids with the bioinformaticsworkflow suite Taverna. These are supported by a unique relationaldatabase to link applications with grid resources and presentsthose as workflow elements. A web portal facilitates its collaborativemaintenance. The first approach implements a gateway serviceto handle authentication certificates and all communicationwith the grid. It reads the database to spawn web services forworkflow elements which are in turn used by Taverna. The secondapproach lets Taverna communicate with the grid on its own,by means of a newly developed plug-in. It reads the databaseand executes the needed tasks directly on the grid. While thegateway service is non-intrusive, the plug-in has technicaladvantages, e.g. by allowing data to remain on the grid whilebeing passed between workflow elements. Availability: http://grid.inb.uni-luebeck.de/ Contact: bayer{at}inb.uni-luebeck.de Associate Editor: Alfonso Valencia     

SIMPLEX: Cloud-Enabled Pipeline for the Comprehensive Analysis of Exome Sequencing Data

In recent studies, exome sequencing has proven to be a successful screening tool for the identification of candidate genes causing rare genetic diseases. Although underlying targeted sequencing methods are well established, necessary data handling and focused, structured analysis still remain demanding tasks. Here, we present a cloud-enabled autonomous analysis pipeline, which comprises the complete exome analysis workflow. The pipeline combines several in-house developed and published applications to perform the following steps: (a) initial quality control, (b) intelligent data filtering and pre-processing, (c) sequence alignment to a reference genome, (d) SNP and DIP detection, (e) functional annotation of variants using different approaches, and (f) detailed report generation during various stages of the workflow. The pipeline connects the selected analysis steps, exposes all available parameters for customized usage, performs required data handling, and distributes computationally expensive tasks either on a dedicated high-performance computing infrastructure or on the Amazon cloud environment (EC2). The presented application has already been used in several research projects including studies to elucidate the role of rare genetic diseases. The pipeline is continuously tested and is publicly available under the GPL as a VirtualBox or Cloud image at http://simplex.i-med.ac.at; additional supplementary data is provided at http://www.icbi.at/exome.