Chronomics analysis toolkit (CATkit)

Chronomics, and the study of biological rhythms, is a rapidly growing area of study. As biological researchers transition to awareness of the impact biological oscillators have on experimental outcomes, and the need to design experiments around genetic, molecular, physiological, and behavioral rhythms, there is increasing need to identify and characterize rhythmicity. There is a corresponding increase in demand for accessible software tools to analyze data for rhythms, both macroscopically and quantitatively. The chronomics analysis toolkit (CATkit), an R package for analysis of periodicities in time series, is a free and open source suite of rhythm analysis tools that runs on UNIX, Windows, and Macintosh platforms. It is particularly well suited to the often scarce, frequently noisy, biological data, and is also applicable to long and/or denser records. CATkit includes a variety of tools, providing visualization and inspection tools to aid in determining whether data meet key assumptions for analysis; as well as quantitative assessment, by cosinor, of mean, amplitude and phase at an assumed period (or periods), with a measure of uncertainty for each parameter.     

Online tools to support literature-based discovery in the life sciences

In biomedical research, the amount of experimental data and published scientific information is overwhelming and ever increasing, which may inhibit rather than stimulate scientific progress. Not only are text-mining and information extraction tools needed to render the biomedical literature accessible but the results of these tools can also assist researchers in the formulation and evaluation of novel hypotheses. This requires an additional set of technological approaches that are defined here as literature-based discovery (LBD) tools. Recently, several LBD tools have been developed for this purpose and a few well-motivated, specific and directly testable hypotheses have been published, some of which have even been validated experimentally. This paper presents an overview of recent LBD research and discusses methodology, results and online tools that are available to the scientific community.     

ALLERDB database and integrated bioinformatic tools for assessment of allergenicity and allergic cross-reactivity

Databases and computational tools are increasingly important in the study of allergies, particularly in the assessment of allergenicity and allergic cross-reactivity. ALLERDB database contains sequences of allergens and information on reported cross-reactivity between allergens. It focuses on analysis of allergenicity and allergic cross-reactivity of clinically relevant protein allergens. The official IUIS allergen data were extracted from the IUIS Allergen Nomenclature Sub-Committee website, and their sequence information from the public databases, and reference publications. The analysis tools assist allergen data analysis and retrieval, and include keyword searching, BLAST, prediction of allergenicity, modification of BLAST that displays cross-reactive allergens, and graphics representation of cross-reactivity data. ALLERDB is new brand of allergen databases with a rich set of tools for sequence comparison, pattern identification, and visualization of results. It is accessible at http://research.i2r.a-star.edu.sg/Templar/DB/Allergen.     

PRISM: a data management system for high-throughput proteomics

Advanced proteomic research efforts involving areas such as systems biology or biomarker discovery are enabled by the use of high level informatics tools that allow the effective analysis of large quantities of differing types of data originating from various studies. Performing such analyses on a large scale is not feasible without a computational platform that performs data processing and management tasks. Such a platform must be able to provide high-throughput operation while having sufficient flexibility to accommodate evolving data analysis tools and methodologies. The Proteomics Research Information Storage and Management system (PRISM) provides a platform that serves the needs of the accurate mass and time tag approach developed at Pacific Northwest National Laboratory. PRISM incorporates a diverse set of analysis tools and allows a wide range of operations to be incorporated by using a state machine that is accessible to independent, distributed computational nodes. The system has scaled well as data volume has increased over several years, while allowing adaptability for incorporating new and improved data analysis tools for more effective proteomics research.     

Integrating Pathways of Parkinson's Disease in a Molecular Interaction Map

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map.     

LOX-DB-- database on lipoxygenases

SUMMARY: Lipoxygenases are a family of enzymes involved in a variety of human diseases like inflammation, asthma, artherosclerosis and cancer. The lipoxygenases database (LOX-DB) aims to be a web accessible compendium of information in particular on the mammalian members of this multigene family. This resource includes molecular structures, reference data, tools for structural and computational analysis as well as links to related information maintained by others. The data can be retrieved by the use of various search options and analyzed applying publicly available visualization tools. AVAILABILITY: LOX-DB is available at http://www.dkfz-heidelberg.de/spec/lox-db/     

FLAVIdB: A data mining system for knowledge discovery in flaviviruses with direct applications in immunology and vaccinology

The flavivirus genus is unusually large, comprising more than 70 species, of which more than half are known human pathogens. It includes a set of clinically relevant infectious agents such as dengue, West Nile, yellow fever, and Japanese encephalitis viruses. Although these pathogens have been studied exten-sively, safe and efficient vaccines lack for the majority of the flaviviruses. We have assembled a database that combines antigenic data of flaviviruses, specialized analysis tools, and workflows for automated complex analyses focusing on applications in immunology and vaccinology. FLAVIdB contains 12,858 entries of flavivirus antigen sequences, 184 verified T-cell epitopes, 201 verified B-cell epitopes, and 4 representative molecular structures of the dengue virus envelope protein. FLAVIdB was assembled by collection, annotation, and integration of data from GenBank, GenPept, UniProt, IEDB, and PDB. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). Further annotation of selected functionally relevant features was performed by organizing information extracted from the literature. The database was incorporated into a web-accessible data mining system, combining specialized data analysis tools for integrated analysis of relevant data categories (protein sequences, macromolecular structures, and immune epitopes). The data mining system includes tools for variability and conservation analysis, T-cell epitope prediction, and characterization of neutralizing components of B-cell epitopes. FLAVIdB is accessible at cvc.dfci.harvard.edu/flavi/ FLAVIdB represents a new generation of databases in which data and tools are integrated into a data min-ing infrastructures specifically designed to aid rational vaccine design by discovery of vaccine targets.     

GeneOrder: comparing the order of genes in small genomes

MOTIVATION: The recent rapid rise in the availability of whole genome DNA sequence data has led to bottlenecks in their complete analysis. Specifically, there is a need for software tools that will allow mining of gene and putative gene data at a whole genome level. These new tools will complement the current set already in use for studying specific aspects of individual genes and putative genes in detail. A key software challenge is to make them user-friendly, without losing their flexibility and capability for use in research. RESULTS: The creation of GeneOrder-a web-based interactive, computational tool-allows researchers to compare the order of genes in two genomes. It has been tested on full genome sequence data for viruses, mitochondria and chloroplasts that were obtained from the NCBI GenBank database. It is accessible at http://www.bif.atcc.org/GENEOrder/index.html. GeneOrder prepares the comparison in table form, listing the order of similar genes. Hyperlinks are provided from this output; these lead to the 'Protein Coding Regions' in the NCBI database.     

EUROCarbDB: An open-access platform for glycoinformatics

The EUROCarbDB project is a design study for a technical framework, which provides sophisticated, freely accessible, open-source informatics tools and databases to support glycobiology and glycomic research. EUROCarbDB is a relational database containing glycan structures, their biological context and, when available, primary and interpreted analytical data from high-performance liquid chromatography, mass spectrometry and nuclear magnetic resonance experiments. Database content can be accessed via a web-based user interface. The database is complemented by a suite of glycoinformatics tools, specifically designed to assist the elucidation and submission of glycan structure and experimental data when used in conjunction with contemporary carbohydrate research workflows. All software tools and source code are licensed under the terms of the Lesser General Public License, and publicly contributed structures and data are freely accessible. The public test version of the web interface to the EUROCarbDB can be found at http://www.ebi.ac.uk/eurocarb.     

landgenreport: a new r function to simplify landscape genetic analysis using resistance surface layers

We describe functions recently added to the r package popgenreport that can be used to perform a landscape genetic analysis (LGA) based on landscape resistance surfaces, which aims to detect the effect of landscape features on gene flow. These functions for the first time implement a LGA in a single framework. Although the approach has been shown to be a valuable tool to study gene flow in landscapes, it has not been widely used to date, despite the type of data being widely available. In part, this is likely due to the necessity to use several software packages to perform landscape genetic analyses. To apply LGA functions, two types of data sets are required: a data set with spatially referenced and genotyped individuals, and a resistance layer representing the effect of the landscape. The function outputs three pairwise distance matrices from these data: a genetic distance matrix, a cost distance matrix and a Euclidean distance matrix. Statistical tests are performed to test whether the cost matrix contributes to the understanding of the observed population structure. A full report on the analysis and outputs in the form of plots and tables of all intermediate steps of the LGA is produced. It is possible to customize the LGA to allow for different cost path approaches and measures of genetic distances. The package is written in the r language and is available through the Comprehensive r Archive. Comprehensive tutorials and information on how to install and use the package are provided at the authors’ website ( www.popgenreport.org ).     

The World-2DPAGE Constellation to promote and publish gel-based proteomics data through the ExPASy server

Since it was launched in 1993, the ExPASy server has been and is still a reference in the proteomics world. ExPASy users access various databases, many dedicated tools, and lists of resources, among other services. A significant part of resources available is devoted to two-dimensional electrophoresis data. Our latest contribution to the expansion of the pool of on-line proteomics data is the World-2DPAGE Constellation, accessible at http://world-2dpage.expasy.org/. It is composed of the established WORLD-2DPAGE List of 2-D PAGE database servers, the World-2DPAGE Portal that queries simultaneously world-wide proteomics databases, and the recently created World-2DPAGE Repository. The latter component is a public standards-compliant repository for gel-based proteomics data linked to protein identifications published in the literature. It has been set up using the Make2D-DB package, a software tool that helps building SWISS-2DPAGE-like databases on one's own Web site. The lack of necessary informatics infrastructure to build and run a dedicated website is no longer an obstacle to make proteomics data publicly accessible on the Internet.     

GenePattern flow cytometry suite

Background

Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.

Results

In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines.

Conclusions

GenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.

     

Impact of geographic area level on measuring socioeconomic disparities in cancer survival in New South Wales,Australia: A period analysis

BackgroundArea-based socioeconomic measures are widely used in health research. In theory, the larger the area used the more individual misclassification is introduced, thus biasing the association between such area level measures and health outcomes. In this study, we examined the socioeconomic disparities in cancer survival using two geographic area-based measures to see if the size of the area matters.MethodsWe used population-based cancer registry data for patients diagnosed with one of 10 major cancers in New South Wales (NSW), Australia during 2004–2008. Patients were assigned index measures of socioeconomic status (SES) based on two area-level units, census Collection District (CD) and Local Government Area (LGA) of their address at diagnosis. Five-year relative survival was estimated using the period approach for patients alive during 2004–2008, for each socioeconomic quintile at each area-level for each cancer. Poisson-regression modelling was used to adjust for socioeconomic quintile, sex, age-group at diagnosis and disease stage at diagnosis. The relative excess risk of death (RER) by socioeconomic quintile derived from this modelling was compared between area-units.ResultsWe found extensive disagreement in SES classification between CD and LGA levels across all socioeconomic quintiles, particularly for more disadvantaged groups. In general, more disadvantaged patients had significantly lower survival than the least disadvantaged group for both CD and LGA classifications. The socioeconomic survival disparities detected by CD classification were larger than those detected by LGA. Adjusted RER estimates by SES were similar for most cancers when measured at both area levels.ConclusionsWe found that classifying patient SES by the widely used Australian geographic unit LGA results in underestimation of survival disparities for several cancers compared to when SES is classified at the geographically smaller CD level. Despite this, our RER of death estimates derived from these survival estimates were generally similar for both CD and LGA level analyses, suggesting that LGAs remain a valuable spatial unit for use in Australian health and social research, though the potential for misclassification must be considered when interpreting research. While data confidentiality concerns increase with the level of geographical precision, the use of smaller area-level health and census data in the future, with appropriate allowance for confidentiality     

Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space

Abstract

The use of modern data science has recently emerged as a promising new path to tackling the complex challenges involved in the creation of next-generation chemistry and materials. However, despite the appeal of this potentially transformative development, the chemistry community has yet to incorporate it as a central tool in every-day work. Our research program is designed to enable and advance this emerging research approach. It is centred around the creation of a software ecosystem that brings together physics-based modelling, high-throughput in silico screening and data analytics (i.e. the use of machine learning and informatics for the validation, mining and modelling of chemical data). This cyberinfrastructure is devised to offer a comprehensive set of data science techniques and tools as well as a general-purpose scope to make it as versatile and widely applicable as possible. It also emphasises user-friendliness to make it accessible to the community at large. It thus provides the means for the large-scale exploration of chemical space and for a better understanding of the hidden mechanisms that determine the properties of complex chemical systems. Such insights can dramatically accelerate, streamline and ultimately transform the way chemical research is conducted. Aside from serving as a production-level tool, our cyberinfrastructure is also designed to facilitate and assess methodological innovation. Both the software and method development work are driven by concrete molecular design problems, which also allow us to assess the efficacy of the overall cyberinfrastructure.     

GranatumX: A Community-engaging,Modularized, and Flexible Webtool for Single-cell Data Analysis

We present GranatumX, a next-generation software environment for single-cell RNA sequencing (scRNA-seq) data analysis. GranatumX is inspired by the interactive webtool Granatum. GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, which wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.     

FisOmics: A portal of fish genomic resources

An online portal, accessible at URL: http://mail.nbfgr.res.in/FisOmics/, was developed that features different genomic databases and tools. The portal, named as FisOmics, acts as a platform for sharing fish genomic sequences and related information in addition to facilitating the access of high-performance computational resources for genome and proteome data analyses. It provides the ability for quarrying, analysing and visualizing genomic sequences and related information. The featured databases in FisOmics are in the World Wide Web domain already. The aim to develop portal was to provide a nodal point to access the featured databases and work conveniently. Presently, FisOmics includes databases on barcode sequences, microsatellite markers, mitogenome sequences, hypoxia-responsive genes and karyology of fishes. Besides, it has a link to other molecular resources and reports on the on-going activities and research achievements.     

Plant systems biology: insights,advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this  review, we discuss the basics of systems biology including the various ‘omics’ approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.