NCL: a C++ class library for interpreting data files in NEXUS format

The NEXUS Class Library (NCL) is a collection of C++ classes designed to simplify interpreting data files written in the NEXUS format used by many computer programs for phylogenetic analyses. The NEXUS format allows different programs to share the same data files, even though none of the programs can interpret all of the data stored therein. Because users are not required to reformat the data file for each program, use of the NEXUS format prevents cut-and-paste errors as well as the proliferation of copies of the original data file. The purpose of making the NCL available is to encourage the use of the NEXUS format by making it relatively easy for programmers to add the ability to interpret NEXUS files in newly developed software. AVAILABILITY: The NCL is freely available under the GNU General Public License from http://hydrodictyon.eeb.uconn.edu/ncl/ Supplementary information: Documentation for the NCL (general information and source code documentation) is available in HTML format at http://hydrodictyon.eeb.uconn.edu/ncl/     

UniProtJAPI: a remote API for accessing UniProt data

Programmatic access to the UniProt Knowledgebase (UniProtKB) is essential for many bioinformatics applications dealing with protein data. We have created a Java library named UniProtJAPI, which facilitates the integration of UniProt data into Java-based software applications. The library supports queries and similarity searches that return UniProtKB entries in the form of Java objects. These objects contain functional annotations or sequence information associated with a UniProt entry. Here, we briefly describe the UniProtJAPI and demonstrate its usage.     

FASTC: a file format for multi-character sequence data

Here, we define a sequence file format that allows for multi-character elements (FASTC). The format is derived from the FASTA format and the custom alphabet format of POY4/5. The format is more general than either of these formats and can represent a broad variety of sequence-type data. This format should be useful for analyses involving datasets encoded as linear streams such as gene synteny, comparative linguistics, temporal gene expression and development, complex animal behaviours, and general biological time-series data.     

ZTR: a new format for DNA sequence trace data

MOTIVATION: To produce an open and extensible file format for DNA trace data which produces compact files suitable for large-scale storage and efficient use of internet bandwidth. RESULTS: We have created an extensible format named ZTR. For a set of data taken from an ABI-3700 the ZTR format produces trace files which require 61.6% of the disk space used by gzipped SCFv3, and which can be written and read at greater speed. The compression algorithms used for the trace amplitudes are used within the National Center for Biotechnology Information (NCBI) trace archive. lmb.cam.ac.uk/pub/staden/io_lib/test_data.     

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     

BIOFFORC: Tool development for biological file format conversion

The use of bioinformatics tools require different sequence formats at various instances. Every tool uses specific set of formats for processing. Sequence in one format is often required in another format. Thus, there is a need for sequence format conversion. A number of such tools are available in the public domain. Here, we describe BIOFFORC as a file format converter. The tool is developed with a graphical user interface in PERL.

Availability

http://www.winningpath.com/biofforc/     

Bio3d: an R package for the comparative analysis of protein structures

An automated procedure for the analysis of homologous protein structures has been developed. The method facilitates the characterization of internal conformational differences and inter-conformer relationships and provides a framework for the analysis of protein structural evolution. The method is implemented in bio3d, an R package for the exploratory analysis of structure and sequence data. AVAILABILITY: The bio3d package is distributed with full source code as a platform-independent R package under a GPL2 license from: http://mccammon.ucsd.edu/~bgrant/bio3d/     

Log::ProgramInfo: A Perl module to collect and log data for bioinformatics pipelines

Background

To reproduce and report a bioinformatics analysis, it is important to be able to determine the environment in which a program was run. It can also be valuable when trying to debug why different executions are giving unexpectedly different results.

Results

Log::ProgramInfo is a Perl module that writes a log file at the termination of execution of the enclosing program, to document useful execution characteristics. This log file can be used to re-create the environment in order to reproduce an earlier execution. It can also be used to compare the environments of two executions to determine whether there were any differences that might affect (or explain) their operation.

Availability

The source is available on CPAN (Macdonald and Boutros, Log-ProgramInfo. http://search.cpan.org/~boutroslb/Log-ProgramInfo/).

Conclusion

Using Log::ProgramInfo in programs creating result data for publishable research, and including the Log::ProgramInfo output log as part of the publication of that research is a valuable method to assist others to duplicate the programming environment as a precursor to validating and/or extending that research.

     

Knime4Bio: a set of custom nodes for the interpretation of next-generation sequencing data with KNIME

SUMMARY: Analysing large amounts of data generated by next-generation sequencing (NGS) technologies is difficult for researchers or clinicians without computational skills. They are often compelled to delegate this task to computer biologists working with command line utilities. The availability of easy-to-use tools will become essential with the generalization of NGS in research and diagnosis. It will enable investigators to handle much more of the analysis. Here, we describe Knime4Bio, a set of custom nodes for the KNIME (The Konstanz Information Miner) interactive graphical workbench, for the interpretation of large biological datasets. We demonstrate that this tool can be utilized to quickly retrieve previously published scientific findings.     

Biclustering algorithms for biological data analysis: a survey

A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.     

A standardized format for handling data on plasmids,viruses and transposons: The PVT database format

The PVT format described here has been designed to store and retrieve genetic data on plasmids, viruses or transposons with special focus on their applications. Both naturally-occurring and engineered elements can be included in it. A variety of data can be accommodated in fields that are grouped in blocks: name and type of element, database administration, element administration, history, propagation, selection and host, biological properties, cloned insert and applications. The number of fields, now 157, can be expanded as required. Most properties can be described in simple logical fields. The format is organized to permit rapid searches and to facilitate communication between database and user; connection with culture and/or DNA collections is also envisaged and adequate fields for these tasks have been provided. The format allows cross-reference with that originated by the Microbial Information Network Europe (MINE) for computer storage and handling of bacterial or fungal strain data.     

Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data

Background  

Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

XEMBL: distributing EMBL data in XML format

Data in the EMBL Nucleotide Sequence Database is traditionally available in a flat file format that has a number of known shortcomings. With XML rapidly emerging as a standard data exchange format that can address some problems of flat file formats by defining data structure and syntax, there is now a demand to distribute EMBL data in an XML format. XEMBL is a service tool that employs CORBA servers to access EMBL data, and distributes the data in XML format via a number of mechanisms. AVAILABILITY: Use of the XEMBL service is free of charge at http://www.ebi.ac.uk/xembl/, and can be accessed via web forms, CGI, and a SOAP-enabled service. SUPPLEMENTARY INFORMATION: Information on the EMBL Nucleotide Sequence Database is available at http://www.ebi.ac.uk/embl/. The EMBL Object Model is available at http://corba.ebi.ac.uk/models/. Information on the EMBL CORBA servers is at http://corba.ebi.ac.uk/     

ProServer: a simple, extensible Perl DAS server

SUMMARY: The increasing size and complexity of biological databases has led to a growing trend to federate rather than duplicate them. In order to share data between federated databases, protocols for the exchange mechanism must be developed. One such data exchange protocol that is widely used is the Distributed Annotation System (DAS). For example, DAS has enabled small experimental groups to integrate their data into the Ensembl genome browser. We have developed ProServer, a simple, lightweight, Perl-based DAS server that does not depend on a separate HTTP server. The ProServer package is easily extensible, allowing data to be served from almost any underlying data model. Recent additions to the DAS protocol have enabled both structure and alignment (sequence and structural) data to be exchanged. ProServer allows both of these data types to be served. AVAILABILITY: ProServer can be downloaded from http://www.sanger.ac.uk/proserver/ or CPAN http://search.cpan.org/~rpettett/. Details on the system requirements and installation of ProServer can be found at http://www.sanger.ac.uk/proserver/.