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PubMed2005

libSRES: a C library for stochastic ranking evolution strategy for parameter estimation.

SUMMARY: Estimation of kinetic parameters in a biochemical pathway or network represents a common problem in systems studies of biological processes. We have implemented a C library, named libSRES, to facilitate a fast implementation of computer software for study of non-linear biochemical pathways. This library implements a (mu, lambda)-ES evolutionary optimization algorithm that uses stochastic ranking as the constraint handling technique. Considering the amount of computing time it might require to solve a parameter-estimation problem, an MPI version of libSRES is provided for parallel implementation, as well as a simple user interface. libSRES is freely available and could be used directly in any C program as a library function. We have extensively tested the performance of libSRES on various pathway parameter-estimation problems and found its performance to be satisfactory. AVAILABILITY: The source code (in C) is free for academic users at http://csbl.bmb.uga.edu/~jix/science/libSRES/

Algorithms↗

PubMed2005

ClaNC: point-and-click software for classifying microarrays to nearest centroids.

SUMMARY: ClaNC (classification to nearest centroids) is a simple and an accurate method for classifying microarrays. This document introduces a point-and-click interface to the ClaNC methodology. The software is available as an R package. AVAILABILITY: ClaNC is freely available from http://students.washington.edu/adabney/clanc

Algorithms↗

PubMed2005

PCP: a program for supervised classification of gene expression profiles.

UNLABELLED: PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns (vectors of measurements). The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression. PCP implements leading pattern classification and gene selection algorithms and incorporates cross-validation estimation of classifier performance. Importantly, the implementation integrates gene selection and class prediction stages, which is vital for computing reliable performance estimates in small-sample scenarios. Additionally, the program includes automated and efficient model selection (optimization of parameters) for support vector machine (SVM) classifier. The distribution includes Linux and Windows/Cygwin binaries. The program can easily be ported to other platforms. AVAILABILITY: Free download at http://pcp.sourceforge.net

Algorithms↗

PubMed2005

LogoBar: bar graph visualization of protein logos with gaps.

SUMMARY: LogoBar is a Java application to display protein sequence logos. In our software gaps are accounted for when calculating the information content present at each residue position in a multiple alignment. The resulting logo is displayed as a graph consisting of bars, although traditional letter representation is also possible. Amino acids are displayed from the bottom up with decreasing frequencies i.e. the most abundant residue is placed at the bottom of the logo. The bars can be color-coded according to user specifications. Gaps in the alignment are also displayed, either on top or at the bottom of the logo. Furthermore, residues can either be arranged according to their relative abundance or grouped according to user criteria to emphasize the conserved nature of particular positions. AVAILABILITY: LogoBar and further documentation is available at http://www.biosci.ki.se/groups/tbu/logobar/

Algorithms↗

PubMed2005

WindowMasker: window-based masker for sequenced genomes.

MOTIVATION: Matches to repetitive sequences are usually undesirable in the output of DNA database searches. Repetitive sequences need not be matched to a query, if they can be masked in the database. RepeatMasker/Maskeraid (RM), currently the most widely used software for DNA sequence masking, is slow and requires a library of repetitive template sequences, such as a manually curated RepBase library, that may not exist for newly sequenced genomes. RESULTS: We have developed a software tool called WindowMasker (WM) that identifies and masks highly repetitive DNA sequences in a genome, using only the sequence of the genome itself. WM is orders of magnitude faster than RM because WM uses a few linear-time scans of the genome sequence, rather than local alignment methods that compare each library sequence with each piece of the genome. We validate WM by comparing BLAST outputs from large sets of queries applied to two versions of the same genome, one masked by WM, and the other masked by RM. Even for genomes such as the human genome, where a good RepBase library is available, searching the database as masked with WM yields more matches that are apparently non-repetitive and fewer matches to repetitive sequences. We show that these results hold for transcribed regions as well. WM also performs well on genomes for which much of the sequence was in draft form at the time of the analysis. AVAILABILITY: WM is included in the NCBI C++ toolkit. The source code for the entire toolkit is available at ftp://ftp.ncbi.nih.gov/toolbox/ncbi_tools++/CURRENT/. Once the toolkit source is unpacked, the instructions for building WindowMasker application in the UNIX environment can be found in file src/app/winmasker/README.build. SUPPLEMENTARY INFORMATION: Supplementary data are available at ftp://ftp.ncbi.nlm.nih.gov/pub/agarwala/windowmasker/windowmasker_suppl.pdf

Algorithms↗

PubMed2005

apTreeshape: statistical analysis of phylogenetic tree shape.

apTreeshape is a R package dedicated to simulation and analysis of phylogenetic tree topologies using statistical imbalance measures. It is a companion library of the R package 'ape', which provides additional functions for reading, plotting, manipulating phylogenetic trees and for connecting to public phylogenetic tree databases. One strength of the package is to include appropriate corrections of classical shape statistics as well as new tests based on the statistical theory of likelihood ratios.

Algorithms↗

PubMed2005

An extensible automated protein annotation tool: standardizing input and output using validated XML.

MOTIVATION: There is a frequent need to apply a large range of local or remote prediction and annotation tools to one or more sequences. We have created a tool able to dispatch one or more sequences to assorted services by defining a consistent XML format for data and annotations. RESULTS: By analyzing annotation tools, we have determined that annotations can be described using one or more of the six forms of data: numeric or textual annotation of residues, domains (residue ranges) or whole sequences. With this in mind, XML DTDs have been designed to store the input and output of any server. Plug-in wrappers to a number of services have been written which are called from a master script. The resulting APATML is then formatted for display in HTML. Alternatively further tools may be written to perform post-analysis.

Amino Acid Sequence↗

PubMed2005

GenomeDiagram: a python package for the visualization of large-scale genomic data.

UNLABELLED: We present GenomeDiagram, a flexible, open-source Python module for the visualization of large-scale genomic, comparative genomic and other data with reference to a single chromosome or other biological sequence. GenomeDiagram may be used to generate publication-quality vector graphics, rastered images and in-line streamed graphics for webpages. The package integrates with datatypes from the BioPython project, and is available for Windows, Linux and Mac OS X systems. AVAILABILITY: GenomeDiagram is freely available as source code (under GNU Public License) at http://bioinf.scri.ac.uk/lp/programs.html, and requires Python 2.3 or higher, and recent versions of the ReportLab and BioPython packages. SUPPLEMENTARY INFORMATION: A user manual, example code and images are available at http://bioinf.scri.ac.uk/lp/programs.html.

Chromosome Mapping↗

PubMed2006

Maximum likelihood estimates of allele frequencies and error rates from samples of related individuals by gene counting.

SUMMARY: Graphical modeling is used to extend the gene counting method to compute maximum likelihood estimates of allele frequencies for samples of individuals related in extended pedigrees. Genotypes may be missing or partially observed, and error rates can be simultaneously estimated. AVAILABILITY: The Java classes and Javadocs pages for \mathsf\hbox GeneCountAlleles can be obtained from bioinformatics.med.utah.edu/~alun, which also has more information on its use and file formats.

Biological Evolution↗

PubMed2006

The MGED Ontology: a resource for semantics-based description of microarray experiments.

MOTIVATION: The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. RESULTS: Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. AVAILABILITY: The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). CONTACT: Stoeckrt@pcbi.upenn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational Biology↗

PubMed2006

Paloverde: an OpenGL 3D phylogeny browser.

UNLABELLED: Paloverde is a new program designed to help visualize the phylogenetic structure of moderately large trees--trees on the scale of 100-2500 leaf nodes. The program embeds the user in an interactive virtual 3D world in which a large tree presented in various layouts can be manipulated through a mouse interface. The program implements radial 2D layouts, and true 3D spiral, conical and hemispherical (i.e. truly 'tree'-like) layouts. Subclades can be defined in the input file (using standard node-based definitions) and displayed collapsed as new leaf nodes, or left intact but annotated with names around the periphery of the tree. A search tool lets the user zoom to any selected leaf node. Paloverde is an open source project written in ANSI C using the OpenGL library for 3D visualization. AVAILABILITY: Source code, makefiles for Mac OS X and Linux and a compiled binary for Mac OS X are available at http://ginger.ucdavis.edu/paloverde/paloverde.html, along with a sample dataset.

Computer Graphics↗

PubMed2006

Pedigree-drawing with R and graphviz.

UNLABELLED: Two functions for pedigree-drawing available in R (http://www.r-project.org): plot.pedigree in kinship and pedtodot in gap are described. The latter requires graphviz (http://www.graphviz.org). They can produce many pedigree diagrams quickly into a single file, serving as alternatives to programs that only offer interactive use. AVAILABILITY: Packages kinship and gap are available from http://cran.r-project.org.

Computer Graphics↗

PubMed2006

GALGO: an R package for multivariate variable selection using genetic algorithms.

SUMMARY: The development of statistical models linking the molecular state of a cell to its physiology is one of the most important tasks in the analysis of Functional Genomics data. Because of the large number of variables measured a comprehensive evaluation of variable subsets cannot be performed with available computational resources. It follows that an efficient variable selection strategy is required. However, although software packages for performing univariate variable selection are available, a comprehensive software environment to develop and evaluate multivariate statistical models using a multivariate variable selection strategy is still needed. In order to address this issue, we developed GALGO, an R package based on a genetic algorithm variable selection strategy, primarily designed to develop statistical models from large-scale datasets.

Algorithms↗

PubMed2006

Analysis of internal loops within the RNA secondary structure in almost quadratic time.

MOTIVATION: Evaluating all possible internal loops is one of the key steps in predicting the optimal secondary structure of an RNA molecule. The best algorithm available runs in time O(L(3)), L is the length of the RNA. RESULTS: We propose a new algorithm for evaluating internal loops, its run-time is O(M(*)log(2)L), M < L(2) is a number of possible nucleotide pairings. We created a software tool Afold which predicts the optimal secondary structure of RNA molecules of lengths up to 28 000 nt, using a computer with 2 Gb RAM. We also propose algorithms constructing sets of conditionally optimal multi-branch loop free (MLF) structures, e.g. the set that for every possible pairing (x, y) contains an optimal MLF structure in which nucleotides x and y form a pair. All the algorithms have run-time O(M(*)log(2)L).

Algorithms↗

PubMed2006

Finding regions of significance in SELDI measurements for identifying protein biomarkers.

MOTIVATION: There is a well-recognized potential of protein expression profiling using the surface-enhanced laser desorption and ionization technology for discovering biomarkers that can be applied in clinical diagnosis, prognosis and therapy prediction. The pre-processing of the raw data, however, is still problematic. METHODS: We focus on the peak detection step, where the standard method is marked by poor specificity. Currently, scientists need to inspect individual spectra visually and laboriously in order to verify that spectral peaks identified by the standard method are real. Motivated by this multi-spectral process, we investigate an analytical approach-called RS for 'regions of significance'-that reduces the data to a single spectrum of F-statistics capturing significant variability between spectra. To account for multiple testing, we use a false discovery rate criterion for identifying potentially interesting proteins. RESULTS: We show that RS has better operating characteristics than several existing methods and demonstrate routine applications on a number of large datasets.

Algorithms↗

PubMed2006

SScore: an R package for detecting differential gene expression without gene expression summaries.

SUMMARY: SScore is an R package that facilitates the comparison of gene expression between Affymetrix GeneChips using the S-score algorithm. The S-score algorithm uses probe level data directly to assess differences in gene expression, without requiring a preliminary separate step of probe set expression summary estimation. Therefore, the algorithm avoids introduction of error associated with the expression summary estimation process and has been demonstrated to improve the accuracy of identifying differentially expressed genes. The S-score produces accurate results even when few or no replicates are available. AVAILABILITY: The R package SScore is available from Bioconductor at http://www.bioconductor.org

Algorithms↗

PubMed2006

GenRGenS: software for generating random genomic sequences and structures.

SUMMARY: GenRGenS is a software tool dedicated to randomly generating genomic sequences and structures. It handles several classes of models useful for sequence analysis, such as Markov chains, hidden Markov models, weighted context-free grammars, regular expressions and PROSITE expressions. GenRGenS is the only program that can handle weighted context-free grammars, thus allowing the user to model and to generate structured objects (such as RNA secondary structures) of any given desired size. GenRGenS also allows the user to combine several of these different models at the same time.

Computational Biology↗

PubMed2006

A dynamic programming algorithm for binning microbial community profiles.

MOTIVATION: A number of community profiling approaches have been widely used to study the microbial community composition and its variations in environmental ecology. Automated Ribosomal Intergenic Spacer Analysis (ARISA) is one such technique. ARISA has been used to study microbial communities using 16S-23S rRNA intergenic spacer length heterogeneity at different times and places. Owing to errors in sampling, random mutations in PCR amplification, and probably mostly variations in readings from the equipment used to analyze fragment sizes, the data read directly from the fragment analyzer should not be used for down stream statistical analysis. No optimal data preprocessing methods are available. A commonly used approach is to bin the reading lengths of the 16S-23S intergenic spacer. We have developed a dynamic programming algorithm based binning method for ARISA data analysis which minimizes the overall differences between replicates from the same sampling location and time. RESULTS: In a test example from an ocean time series sampling program, data preprocessing identified several outliers which upon re-examination were found to be because of systematic errors. Clustering analysis of the ARISA from different times based on the dynamic programming algorithm binned data revealed important features of the biodiversity of the microbial communities.

Algorithms↗