SpA: web-accessible spectratype analysis: data management, statistical analysis and visualization

SUMMARY: SpA is a web-accessible system for the management, visualization and statistical analysis of T-cell receptor spectratype data. Users upload data from their spectratype analyzers to SpA, which saves the raw data and user-defined supplementary covariates to a secure database. The statistical engine performs several data analyses and statistical summaries. The visualization engine displays spectratype histograms in a Java applet and in an image file suitable for download. All of these results are also saved to the database and remain accessible to the user. Additional statistical tools specific to the analysis of multiple spectratypes are also available through the SpA interface. AVAILABILITY: The service is freely accessible via the web at http://www.duke.edu/~kepler/spa.html. Additional technical support and specialized statistical analysis and consultation are available by arrangement with the authors and, depending on the service requested, may be subject to fee.     

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

ABSTRACT: BACKGROUND: Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state of the art pathway-based sets. RESULTS: The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. This semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets. CONCLUSIONS: The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods.     

Bioinformatics visualization and integration with open standards: the Bluejay genomic browser

We have created a new Java-based integrated computational environment for the exploration of genomic data, called Bluejay. The system is capable of using almost any XML file related to genomic data. Non-XML data sources can be accessed via a proxy server. Bluejay has several features, which are new to Bioinformatics, including an unlimited semantic zoom capability, coupled with Scalable Vector Graphics (SVG) outputs; an implementation of the XLink standard, which features access to MAGPIE Genecards as well as any BioMOBY service accessible over the Internet; and the integration of gene chip analysis tools with the functional assignments. The system can be used as a signed web applet, Web Start, and a local stand-alone application, with or without connection to the Internet. It is available free of charge and as open source via http://bluejay.ucalgary.ca.     

Addition of Escherichia coli K-12 Growth Observation and Gene Essentiality Data to the EcoCyc Database

The sets of compounds that can support growth of an organism are defined by the presence of transporters and metabolic pathways that convert nutrient sources into cellular components and energy for growth. A collection of known nutrient sources can therefore serve both as an impetus for investigating new metabolic pathways and transporters and as a reference for computational modeling of known metabolic pathways. To establish such a collection for Escherichia coli K-12, we have integrated data on the growth or nongrowth of E. coli K-12 obtained from published observations using a variety of individual media and from high-throughput phenotype microarrays into the EcoCyc database. The assembled collection revealed a substantial number of discrepancies between the high-throughput data sets, which we investigated where possible using low-throughput growth assays on soft agar and in liquid culture. We also integrated six data sets describing 16,119 observations of the growth of single-gene knockout mutants of E. coli K-12 into EcoCyc, which are relevant to antimicrobial drug design, provide clues regarding the roles of genes of unknown function, and are useful for validating metabolic models. To make this information easily accessible to EcoCyc users, we developed software for capturing, querying, and visualizing cellular growth assays and gene essentiality data.     

UniqueProt: Creating representative protein sequence sets

UniqueProt is a practical and easy to use web service designed to create representative, unbiased data sets of protein sequences. The largest possible representative sets are found through a simple greedy algorithm using the HSSP-value to establish sequence similarity. UniqueProt is not a real clustering program in the sense that the 'representatives' are not at the centres of well-defined clusters since the definition of such clusters is problem-specific. Overall, UniqueProt is a reasonable fast solution for bias in data sets. The service is accessible at http://cubic.bioc.columbia.edu/services/uniqueprot; a command-line version for Linux is downloadable from this web site.     

A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.     

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.     

The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized,curated gateway to Arabidopsis biology,research materials and community

Arabidopsis thaliana is the most widely-studied plant today. The concerted efforts of over 11 000 researchers and 4000 organizations around the world are generating a rich diversity and quantity of information and materials. This information is made available through a comprehensive on-line resource called the Arabidopsis Information Resource (TAIR) (http://arabidopsis.org), which is accessible via commonly used web browsers and can be searched and downloaded in a number of ways. In the last two years, efforts have been focused on increasing data content and diversity, functionally annotating genes and gene products with controlled vocabularies, and improving data retrieval, analysis and visualization tools. New information include sequence polymorphisms including alleles, germplasms and phenotypes, Gene Ontology annotations, gene families, protein information, metabolic pathways, gene expression data from microarray experiments and seed and DNA stocks. New data visualization and analysis tools include SeqViewer, which interactively displays the genome from the whole chromosome down to 10 kb of nucleotide sequence and AraCyc, a metabolic pathway database and map tool that allows overlaying expression data onto the pathway diagrams. Finally, we have recently incorporated seed and DNA stock information from the Arabidopsis Biological Resource Center (ABRC) and implemented a shopping-cart style on-line ordering system.     

A RESTful API for Accessing Microbial Community Data for MG-RAST

Metagenomic sequencing has produced significant amounts of data in recent years. For example, as of summer 2013, MG-RAST has been used to annotate over 110,000 data sets totaling over 43 Terabases. With metagenomic sequencing finding even wider adoption in the scientific community, the existing web-based analysis tools and infrastructure in MG-RAST provide limited capability for data retrieval and analysis, such as comparative analysis between multiple data sets. Moreover, although the system provides many analysis tools, it is not comprehensive. By opening MG-RAST up via a web services API (application programmers interface) we have greatly expanded access to MG-RAST data, as well as provided a mechanism for the use of third-party analysis tools with MG-RAST data. This RESTful API makes all data and data objects created by the MG-RAST pipeline accessible as JSON objects. As part of the DOE Systems Biology Knowledgebase project (KBase, http://kbase.us) we have implemented a web services API for MG-RAST. This API complements the existing MG-RAST web interface and constitutes the basis of KBase''s microbial community capabilities. In addition, the API exposes a comprehensive collection of data to programmers. This API, which uses a RESTful (Representational State Transfer) implementation, is compatible with most programming environments and should be easy to use for end users and third parties. It provides comprehensive access to sequence data, quality control results, annotations, and many other data types. Where feasible, we have used standards to expose data and metadata. Code examples are provided in a number of languages both to show the versatility of the API and to provide a starting point for users. We present an API that exposes the data in MG-RAST for consumption by our users, greatly enhancing the utility of the MG-RAST service.     

Interfaces to PeptideAtlas: a case study of standard data access systems

Access to public data sets is important to the scientific community as a resource to develop new experiments or validate new data. Projects such as the PeptideAtlas, Ensembl and The Cancer Genome Atlas (TCGA) offer both access to public data and a repository to share their own data. Access to these data sets is often provided through a web page form and a web service API. Access technologies based on web protocols (e.g. http) have been in use for over a decade and are widely adopted across the industry for a variety of functions (e.g. search, commercial transactions, and social media). Each architecture adapts these technologies to provide users with tools to access and share data. Both commonly used web service technologies (e.g. REST and SOAP), and custom-built solutions over HTTP are utilized in providing access to research data. Providing multiple access points ensures that the community can access the data in the simplest and most effective manner for their particular needs. This article examines three common access mechanisms for web accessible data: BioMart, caBIG, and Google Data Sources. These are illustrated by implementing each over the PeptideAtlas repository and reviewed for their suitability based on specific usages common to research. BioMart, Google Data Sources, and caBIG are each suitable for certain uses. The tradeoffs made in the development of the technology are dependent on the uses each was designed for (e.g. security versus speed). This means that an understanding of specific requirements and tradeoffs is necessary before selecting the access technology.     

REGANOR

With >1,000 prokaryotic genome sequencing projects ongoing or already finished, comprehensive comparative analysis of the gene content of these genomes has become viable. To allow for a meaningful comparative analysis, gene prediction of the various genomes should be as accurate as possible. It is clear that improving the state of genome annotation requires automated gene identification methods to cope with the influence of artifacts, such as genomic GC content. There is currently still room for improvement in the state of annotations. We present a web server and a database of high-quality gene predictions. The web server is a resource for gene identification in prokaryote genome sequences. It implements our previously described, accurate gene finding method REGANOR. We also provide novel gene predictions for 241 complete, or almost complete, prokaryotic genomes. We demonstrate how this resource can easily be utilised to identify promising candidates for currently missing genes from genome annotations with several examples. All data sets are available online. AVAILABILITY: The gene finding server is accessible via https://www.cebitec.uni-bielefeld.de/groups/brf/software/reganor/cgi-bin/reganor_upload.cgi. The server software is available with the GenDB genome annotation system (version 2.2.1 onwards) under the GNU general public license. The software can be downloaded from https://sourceforge.net/projects/gendb/. More information on installing GenDB and REGANOR and the system requirements can be found on the GenDB project page http://www.cebitec.uni-bielefeld.de/groups/brf/software/wiki/GenDBWiki/AdministratorDocumentation/GenDBInstallation     

Andromeda: a peptide search engine integrated into the MaxQuant environment

A key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Here we describe Andromeda, a novel peptide search engine using a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used commercial search engine, as judged by sensitivity and specificity analysis based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform and both are freely available at www.maxquant.org. The combination enables analysis of large data sets in a simple analysis workflow on a desktop computer. For searching individual spectra Andromeda is also accessible via a web server. We demonstrate the flexibility of the system by implementing the capability to identify cofragmented peptides, significantly improving the total number of identified peptides.     

Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis

MOTIVATION: Pathway knowledge in public databases enables us to examine how individual metabolites are connected via chemical reactions and what genes are implicated in those processes. For two given (sets of) compounds, the number of possible paths between them in a metabolic network can be intractably large. It would be informative to rank these paths in order to differentiate between them. RESULTS: Focusing on adjacent pairwise coexpression, we developed an algorithm which, for a specified k, efficiently outputs the top k paths based on a probabilistic scoring mechanism, using a given metabolic network and microarray datasets. Our idea of using adjacent pairwise coexpression is supported by recent studies that local coregulation is predominant in metabolism. We first evaluated this idea by examining to what extent highly correlated gene pairs are adjacent and how often they are consecutive in a metabolic network. We then applied our algorithm to two examples of path ranking: the paths from glucose to pyruvate in the entire metabolic network of yeast and the paths from phenylalanine to sinapyl alcohol in monolignols pathways of arabidopsis under several different microarray conditions, to confirm and discuss the performance analysis of our method.     

Decoupling dynamical systems for pathway identification from metabolic profiles

RATIONALE: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, time-dense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear magnetic resonance or protein kinase phosphorylation. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations. RESULTS: We show that, for the purpose of structure identification, the substitution of differentials with estimated slopes in non-linear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially. The estimation of slopes for each time series of the metabolic or proteomic profile is accomplished with a 'universal function' that is computed directly from the data by cross-validated training of an artificial neural network (ANN). CONCLUSIONS: Without preprocessing, the inverse problem of determining structure from metabolic or proteomic profile data is challenging and computationally expensive. The combination of system decoupling and data fitting with universal functions simplifies this inverse problem very significantly. Examples show successful estimations and current limitations of the method. AVAILABILITY: A preliminary Web-based application for ANN smoothing is accessible at http://bioinformatics.musc.edu/webmetabol/. S-systems can be interactively analyzed with the user-friendly freeware PLAS (http://correio.cc.fc.ul.pt/~aenf/plas.html) or with the MATLAB module BSTLab (http://bioinformatics.musc.edu/bstlab/), which is currently being beta-tested.     

Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering

Rational metabolic engineering requires powerful theoretical methods such as pathway analysis, in which the topology of metabolic networks is considered. All metabolic capabilities in steady states are composed of elementary flux modes, which are minimal sets of enzymes that can each generate valid steady states. The modes of the fructose-2,6-bisphosphate cycle, the combined tricarboxylic-acid-glyoxylate-shunt system and tryptophan synthesis are used here for illustration. This approach can be used for many biotechnological applications such as increasing the yield of a product, channelling a product into desired pathways and in functional reconstruction from genomic data.     

Characteristic substructures and properties in chemical carcinogens studied by the cascade model

MOTIVATION: Chemical carcinogenicity is an important subject in health and environmental sciences, and a reliable method is expected to identify characteristic factors for carcinogenicity. The predictive toxicology challenge (PTC) 2000-2001 has provided the opportunity for various data mining methods to evaluate their performance. The cascade model, a data mining method developed by the author, has the capability to mine for local correlations in data sets with a large number of attributes. The current paper explores the effectiveness of the method on the problem of chemical carcinogenicity. RESULTS: Rodent carcinogenicity of 417 compounds examined by the National Toxicology Program (NTP) was used as the training set. The analysis by the cascade model, for example, could obtain a rule 'Highly flexible molecules are carcinogenic, if they have no hydrogen bond acceptors in halogenated alkanes and alkenes'. Resulting rules are applied to predict the activity of 185 compounds examined by the FDA. The ROC analysis performed by the PTC organizers has shown that the current method has excellent predictive power for the female rat data. AVAILABILITY: The binary program of DISCAS 2.1 and samples of input data sets on Windows PC are available at http://www.clab.kwansei.ac.jp/mining/discas/discas.html upon request from the author. SUPPLEMENTARY INFORMATION: Summary of prediction results and cross validations is accessible via http://www.clab.kwansei.ac.jp/~okada/BIJ/BIJsupple.htm. Used rules and the prediction results for each molecule are also provided.     

Gene set analysis in the cloud

Cloud computing offers low cost and highly flexible opportunities in bioinformatics. Its potential has already been demonstrated in high-throughput sequence data analysis. Pathway-based or gene set analysis of expression data has received relatively less attention. We developed a gene set analysis algorithm for biomarker identification in the cloud. The resulting tool, YunBe, is ready to use on Amazon Web Services. Moreover, here we compare its performance to those obtained with desktop and computing cluster solutions. AVAILABILITY AND IMPLEMENTATION: YunBe is open-source and freely accessible within the Amazon Elastic MapReduce service at s3n://lrcv-crp-sante/app/yunbe.jar. Source code and user's guidelines can be downloaded from http://tinyurl.com/yunbedownload.