Applications of Tree-Maps to hierarchical biological data

SUMMARY: A brief overview of Tree-Maps provides the basis for understanding two new implementations of Tree-Map methods. TreeMapClusterView provides a new way to view microarray gene expression data, and GenePlacer provides a view of gene ontology annotation data. We also discuss the benefits of Tree-Maps to visualize complex hierarchies in functional genomics. AVAILABILITY: Java class files are freely available at http://mendel.mc.duke.edu/bioinformatics/ CONTACT: mccon012@mc.duke.edu SUPPLEMENTARY INFORMATION: For more information on TreeMapClusterView (see http://mendel.mc.duke.edu/bioinformatics/software/boxclusterview/), and http://mendel.mc.duke.edu/bioinformatics/software/geneplacer/).     

YETI: Yeast Exploration Tool Integrator

Yeast Exploration Tool Integrator (YETI) is a novel bioinformatics tool for the integrated visualization and analysis of functional genomic data sets from the budding yeast Saccharomyces cerevisiae. AVAILABILITY: YETI is freely available for use over the WWW, or download under license, at http://www.bru.ed.ac.uk/~orton/yeti.html     

GRIMM: genome rearrangements web server

SUMMARY: Genome Rearrangements In Man and Mouse (GRIMM) is a tool for analyzing rearrangements of gene orders in pairs of unichromosomal and multichromosomal genomes, with either signed or unsigned gene data. Although there are several programs for analyzing rearrangements in unichromosomal genomes, this is the first to analyze rearrangements in multichromosomal genomes. GRIMM also provides a new algorithm for analyzing comparative maps for which gene directions are unknown. AVAILABILITY: A web server, with instructions and sample data, is available at http://www-cse.ucsd.edu/groups/bioinformatics/GRIMM.     

FLOSYS--a web-accessible workflow system for protocol-driven biomolecular sequence analysis.

FLOSYS is an interactive web-accessible bioinformatics workflow system designed to assist biologists in multi-step data analyses. FLOSYS allows the user to create complex analysis pathways (protocols) graphically, similar to drawing a flowchart: icons representing particular bioinformatics tools are dragged and dropped onto a canvas and lines connecting those icons are drawn to specify the relationships between the tools. In addition, FLOSYS permits to select input-data, execute the protocol and store the results in a personal workspace. The three-tier architecture of FLOSYS has been implemented in Java and uses a relational database system together with new technologies for distributed and web computing such as CORBA, RMI, JSP and JDBC. The prototype of FLOSYS, which is part of the bioinformatics workbench AnaBench, is accessible on-line at http://malawimonas.bcm.umontreal.ca: 8091/anabench. The entire package is available on request to academic groups who wish to have a customized local analysis environment for research or teaching.     

Local sequence determinants of two in-frame triplet deletion/duplication hotspots in the RHD/RHCE genes

A genetic association study is a complicated process that involves collecting phenotypic data, generating genotypic data, analyzing associations between genotypic and phenotypic data, and interpreting genetic biomarkers identified. SNPTrack is an integrated bioinformatics system developed by the US Food and Drug Administration (FDA) to support the review and analysis of pharmacogenetics data resulting from FDA research or submitted by sponsors. The system integrates data management, analysis, and interpretation in a single platform for genetic association studies. Specifically, it stores genotyping data and single-nucleotide polymorphism (SNP) annotations along with study design data in an Oracle database. It also integrates popular genetic analysis tools, such as PLINK and Haploview. SNPTrack provides genetic analysis capabilities and captures analysis results in its database as SNP lists that can be cross-linked for biological interpretation to gene/protein annotations, Gene Ontology, and pathway analysis data. With SNPTrack, users can do the entire stream of bioinformatics jobs for genetic association studies. SNPTrack is freely available to the public at http://www.fda.gov/ScienceResearch/BioinformaticsTools/SNPTrack/default.htm.     

BioWeka--extending the Weka framework for bioinformatics

Given the growing amount of biological data, data mining methods have become an integral part of bioinformatics research. Unfortunately, standard data mining tools are often not sufficiently equipped for handling raw data such as e.g. amino acid sequences. One popular and freely available framework that contains many well-known data mining algorithms is the Waikato Environment for Knowledge Analysis (Weka). In the BioWeka project, we introduce various input formats for bioinformatics data and bioinformatics methods like alignments to Weka. This allows users to easily combine them with Weka's classification, clustering, validation and visualization facilities on a single platform and therefore reduces the overhead of converting data between different data formats as well as the need to write custom evaluation procedures that can deal with many different programs. We encourage users to participate in this project by adding their own components and data formats to BioWeka. Availability: The software, documentation and tutorial are available at http://www.bioweka.org.     

Computational cluster validation in post-genomic data analysis

MOTIVATION: The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge--whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics. RESULTS: This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation. AVAILABILITY: The software used in the experiments is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/. SUPPLEMENTARY INFORMATION: Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.     

Shedding light on black boxes in protein identification

Performing a well thought‐out proteomics data analysis can be a daunting task, especially for newcomers to the field. Even researchers experienced in the proteomics field can find it challenging to follow existing publication guidelines for MS‐based protein identification and characterization in detail. One of the primary goals of bioinformatics is to enable any researcher to interpret the vast amounts of data generated in modern biology, by providing user‐friendly and robust end‐user applications, clear documentation, and corresponding teaching materials. In that spirit, we here present an extensive tutorial for peptide and protein identification, available at http://compomics.com/bioinformatics‐for‐proteomics . The material is completely based on freely available and open‐source tools, and has already been used and refined at numerous international courses over the past 3 years. During this time, it has demonstrated its ability to allow even complete beginners to intuitively conduct advanced bioinformatics workflows, interpret the results, and understand their context. This tutorial is thus aimed at fully empowering users, by removing black boxes in the proteomics informatics pipeline.     

An ontology for bioinformatics applications.

MOTIVATION: An ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. RESULTS: This paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontology's design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships. AVAILABILITY: The TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL.     

The MiSink Plugin: Cytoscape as a graphical interface to the Database of Interacting Proteins

The MiSink Plugin converts Cytoscape, an open-source bioinformatics platform for network visualization, to a graphical interface for the database of interacting proteins (DIP: http://dip.doe-mbi.ucla.edu). Seamless integration is possible by providing bi-directional communication between Cytoscape and any Web site supplying data in XML or tab-delimited format. Availability: MiSink is freely available for download at http://dip.doe-mbi.ucla.edu/Software.cgi.     

Applications of integrative OMICs approaches to gene regulation studies

Background: Functional genomics employs dozens of OMICs technologies to explore the functions of DNA, RNA and protein regulators in gene regulation processes. Despite each of these technologies being powerful tools on their own, like the parable of blind men and an elephant, any one single technology has a limited ability to depict the complex regulatory system. Integrative OMICS approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to study gene regulations.Results: This article reviews current popular OMICs technologies, OMICs data integration strategies, and bioinformatics tools used for multi-dimensional data integration. We highlight the advantages of these methods, particularly in elucidating molecular basis of biological regulatory mechanisms. Conclusions: To better understand the complexity of biological processes, we need powerful bioinformatics tools to integrate these OMICs data. Integrating multi-dimensional OMICs data will generate novel insights into system-level gene regulations and serves as a foundation for further hypothesis-driven research.     

A suite of Perl modules for handling microarray data

We describe PerlMAT, a Perl microarray toolkit providing easy to use object-oriented methods for the simplified manipulation, management and analysis of microarray data. The toolkit provides objects for the encapsulation of microarray spots and reporters, several common microarray data file formats and GAL files. In addition, an analysis object provides methods for data processing, and an image object enables the visualisation of microarray data. This important addition to the Perl developer's library will facilitate more widespread use of Perl for microarray application development within the bioinformatics community. The coherent interface and well-documented code enables rapid analysis by even inexperienced Perl developers. AVAILABILITY: Software is available at http://sourceforge.net/projects/perlmat     

Genotypechecker: an interactive tool for checking the inheritance consistency of genotyped pedigrees

Datapoint errors in pedigree genotype data sets are difficult to identify and adversely affect downstream genetic analyses. We present GenotypeChecker, a desktop software tool for assisting data cleansing. The application identifies likely data errors in pedigree/genotype data sets by performing an inheritance-checking algorithm for each marker across the pedigree, and highlights inconsistently inherited genotypes in an exploratory user interface. By 'masking' suspect datapoints and rechecking inheritance consistency, erroneous datapoints can be confirmed and cleansed from the data set. The software, examples and documentation are freely available at http://bioinformatics.roslin.ac.uk/genotypechecker.     

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.     

A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

ABSTRACT: BACKGROUND: Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers. RESULTS: We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/. CONCLUSIONS: The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.