Bioinformatics - current issue

Estimating DNA coverage and abundance in metagenomes using a gamma approximation

Tue, 02 Feb 2010 07:37:59 PST

Motivation: Shotgun sequencing generates large numbers of short DNA reads from either an isolated organism or, in the case of metagenomics projects, from the aggregate genome of a microbial community. These reads are then assembled based on overlapping sequences into larger, contiguous sequences (contigs). The feasibility of assembly and the coverage achieved (reads per nucleotide or distinct sequence of nucleotides) depend on several factors: the number of reads sequenced, the read length and the relative abundances of their source genomes in the microbial community. A low coverage suggests that most of the genomic DNA in the sample has not been sequenced, but it is often difficult to estimate either the extent of the uncaptured diversity or the amount of additional sequencing that would be most efficacious. In this work, we regard a metagenome as a population of DNA fragments (bins), each of which may be covered by one or more reads. We employ a gamma distribution to model this bin population due to its flexibility and ease of use. When a gamma approximation can be found that adequately fits the data, we may estimate the number of bins that were not sequenced and that could potentially be revealed by additional sequencing. We evaluated the performance of this model using simulated metagenomes and demonstrate its applicability on three recent metagenomic datasets.

Contact: sean.d.hooper@genpat.uu.se

Supplementary information: Supplementary data are available at Bioinformatics online.

HIGEDA: a hierarchical gene-set genetics based algorithm for finding subtle motifs in biological sequences

Le, T., Altman, T., Gardiner, K. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: Identification of motifs in biological sequences is a challenging problem because such motifs are often short, degenerate, and may contain gaps. Most algorithms that have been developed for motif-finding use the expectation-maximization (EM) algorithm iteratively. Although EM algorithms can converge quickly, they depend strongly on initialization parameters and can converge to local sub-optimal solutions. In addition, they cannot generate gapped motifs. The effectiveness of EM algorithms in motif finding can be improved by incorporating methods that choose different sets of initial parameters to enable escape from local optima, and that allow gapped alignments within motif models.

Results: We have developed HIGEDA, an algorithm that uses the hierarchical gene-set genetic algorithm (HGA) with EM to initiate and search for the best parameters for the motif model. In addition, HIGEDA can identify gapped motifs using a position weight matrix and dynamic programming to generate an optimal gapped alignment of the motif model with sequences from the dataset. We show that HIGEDA outperforms MEME and other motif-finding algorithms on both DNA and protein sequences.

Availability and implementation: Source code and test datasets are available for download at http://ouray.cudenver.edu/~tnle/, implemented in C++ and supported on Linux and MS Windows.

Contact: katheleen.gardiner@ucdenver.edu

Globally, unrelated protein sequences appear random

Lavelle, D. T., Pearson, W. R. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: To test whether protein folding constraints and secondary structure sequence preferences significantly reduce the space of amino acid words in proteins, we compared the frequencies of four- and five-amino acid word clumps (independent words) in proteins to the frequencies predicted by four random sequence models.

Results: While the human proteome has many overrepresented word clumps, these words come from large protein families with biased compositions (e.g. Zn-fingers). In contrast, in a non-redundant sample of Pfam-AB, only 1% of four-amino acid word clumps (4.7% of 5mer words) are 2-fold overrepresented compared with our simplest random model [MC(0)], and 0.1% (4mers) to 0.5% (5mers) are 2-fold overrepresented compared with a window-shuffled random model. Using a false discovery rate q-value analysis, the number of exceptional four- or five-letter words in real proteins is similar to the number found when comparing words from one random model to another. Consensus overrepresented words are not enriched in conserved regions of proteins, but four-letter words are enriched 1.18- to 1.56-fold in -helical secondary structures (but not β-strands). Five-residue consensus exceptional words are enriched for -helix 1.43- to 1.61-fold. Protein word preferences in regular secondary structure do not appear to significantly restrict the use of sequence words in unrelated proteins, although the consensus exceptional words have a secondary structure bias for -helix. Globally, words in protein sequences appear to be under very few constraints; for the most part, they appear to be random.

Contact: wrp@virginia.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Optimization of minimum set of protein-DNA interactions: a quasi exact solution with minimum over-fitting

Temiz, N. A., Trapp, A., Prokopyev, O. A., Camacho, C. J. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: A major limitation in modeling protein interactions is the difficulty of assessing the over-fitting of the training set. Recently, an experimentally based approach that integrates crystallographic information of C2H2 zinc finger–DNA complexes with binding data from 11 mutants, 7 from EGR finger I, was used to define an improved interaction code (no optimization). Here, we present a novel mixed integer programming (MIP)-based method that transforms this type of data into an optimized code, demonstrating both the advantages of the mathematical formulation to minimize over- and under-fitting and the robustness of the underlying physical parameters mapped by the code.

Results: Based on the structural models of feasible interaction networks for 35 mutants of EGR–DNA complexes, the MIP method minimizes the cumulative binding energy over all complexes for a general set of fundamental protein–DNA interactions. To guard against over-fitting, we use the scalability of the method to probe against the elimination of related interactions. From an initial set of 12 parameters (six hydrogen bonds, five desolvation penalties and a water factor), we proceed to eliminate five of them with only a marginal reduction of the correlation coefficient to 0.9983. Further reduction of parameters negatively impacts the performance of the code (under-fitting). Besides accurately predicting the change in binding affinity of validation sets, the code identifies possible context-dependent effects in the definition of the interaction networks. Yet, the approach of constraining predictions to within a pre-selected set of interactions limits the impact of these potential errors to related low-affinity complexes.

Contact: ccamacho@pitt.edu; droleg@pitt.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence

Garbuzynskiy, S. O., Lobanov, M. Yu., Galzitskaya, O. V. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: Amyloidogenic regions in polypeptide chains are very important because such regions are responsible for amyloid formation and aggregation. It is useful to be able to predict positions of amyloidogenic regions in protein chains.

Results: Two characteristics (expected probability of hydrogen bonds formation and expected packing density of residues) have been introduced by us to detect amyloidogenic regions in a protein sequence. We demonstrate that regions with high expected probability of the formation of backbone–backbone hydrogen bonds as well as regions with high expected packing density are mostly responsible for the formation of amyloid fibrils. Our method (FoldAmyloid) has been tested on a dataset of 407 peptides (144 amyloidogenic and 263 non-amyloidogenic peptides) and has shown good performance in predicting a peptide status: amyloidogenic or non-amyloidogenic. The prediction based on the expected packing density classified correctly 75% of amyloidogenic peptides and 74% of non-amyloidogenic ones. Two variants (averaging by donors and by acceptors) of prediction based on the probability of formation of backbone–backbone hydrogen bonds gave a comparable efficiency. With a hybrid-scale constructed by merging the above three scales, our method is correct for 80% of amyloidogenic peptides and for 72% of non-amyloidogenic ones. Prediction of amyloidogenic regions in proteins where positions of amyloidogenic regions are known from experimental data has also been done. In the proteins, our method correctly finds 10 out of 11 amyloidogenic regions.

Availability: The FoldAmyloid server is available at http://antares.protres.ru/fold-amyloid/

Contact: ogalzit@vega.protres.ru

Biomarker detection in the integration of multiple multi-class genomic studies

Lu, S., Li, J., Song, C., Shen, K., Tseng, G. C. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: Systematic information integration of multiple-related microarray studies has become an important issue as the technology becomes mature and prevalent in the past decade. The aggregated information provides more robust and accurate biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multi-class studies and considering expression pattern concordance are rarely explored.

Results: In this article, we develop three integration methods for biomarker detection in multiple multi-class microarray studies: ANOVA-maxP, min-MCC and OW-min-MCC. We first consider a natural extension of combining P-values from the traditional ANOVA model. Since P-values from ANOVA do not guarantee to reflect the concordant expression pattern information across studies, we propose a multi-class correlation (MCC) measure to specifically seek for biomarkers of concordant inter-class patterns across a pair of studies. For both ANOVA and MCC approaches, we use extreme order statistics to identify biomarkers differentially expressed (DE) in all studies (i.e. ANOVA-maxP and min-MCC). The min-MCC method is further extended to identify biomarkers DE in partial studies by incorporating a recently developed optimally weighted (OW) technique (OW-min-MCC). All methods are evaluated by simulation studies and by three meta-analysis applications to multi-tissue mouse metabolism datasets, multi-condition mouse trauma datasets and multi-malignant-condition human prostate cancer datasets. The results show complementary strength of the three methods for different biological purposes.

Availability: http://www.biostat.pitt.edu/bioinfo/

Contact: ctseng@pitt.edu

Supplementary information: Supplementary data is available at Bioinformatics online.

Dynamically weighted clustering with noise set

Shen, Y., Sun, W., Li, K.-C. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: Various clustering methods have been applied to microarray gene expression data for identifying genes with similar expression profiles. As the biological annotation data accumulated, more and more genes have been organized into functional categories. Functionally related genes may be regulated by common cellular signals, thus likely to be co-expressed. Consequently, utilizing the rapidly increasing functional annotation resources such as Gene Ontology (GO) to improve the performance of clustering methods is of great interest. On the opposite side of clustering, there are genes that have distinct expression profiles and do not co-express with other genes. Identification of these scattered genes could enhance the performance of clustering methods.

Results: We developed a new clustering algorithm, Dynamically Weighted Clustering with Noise set (DWCN), which makes use of gene annotation information and allows for a set of scattered genes, the noise set, to be left out of the main clusters. We tested the DWCN method and contrasted its results with those obtained using several common clustering techniques on a simulated dataset as well as on two public datasets: the Stanford yeast cell-cycle gene expression data, and a gene expression dataset for a group of genetically different yeast segregants.

Conclusion: Our method produces clusters with more consistent functional annotations and more coherent expression patterns than existing clustering techniques.

Contact: yshen@stat.ucla.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

A new gene selection procedure based on the covariance distance

Hu, R., Qiu, X., Glazko, G. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: Very little attention has been given to gene selection procedures based on intergene correlation structure, which is often neglected in the context of differential gene expression analysis. We propose a statistical procedure to select genes that have different associations with others across different phenotypes. This procedure is based on a new gene association score, called the covariance distance.

Results: We apply the proposed method, along with two alternative methods, to several simulated datasets and find out that our method is much more powerful than the other two. For biological data, we demonstrate that the analysis of differentially associated genes complements the analysis of differentially expressed genes. Combining both procedures provides a more comprehensive functional interpretation of the experimental results.

Availability: The code is downloadable from http://www.urmc.rochester.edu/biostat/people/faculty/hu.cfm

Contact: huruizg@hotmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana

Tue, 02 Feb 2010 07:37:59 PST

Motivation: Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose. However, gene regulatory events may induce complex temporal features in a gene expression profile, including time delays, inversions and transient correlations, which are not well accounted for by current clustering methods. As the cost of microarray experiments continues to fall, the temporal resolution of time course studies is increasing. This has led to a need to take account of detailed temporal features of this kind. Thus, while standard clustering methods are both widely used and much studied, their shared shortcomings with respect to such temporal features motivates the work presented here.

Results: Here, we introduce a temporal clustering approach for high-dimensional gene expression data which takes account of time delays, inversions and transient correlations. We do so by exploiting a recently introduced, message-passing-based algorithm called Affinity Propagation (AP). We take account of temporal features of interest following an approximate but efficient dynamic programming approach due to Qian et al. The resulting approach is demonstrably effective in its ability to discern non-obvious temporal features, yet efficient and robust enough for routine use as an exploratory tool. We show results on validated transcription factor–target pairs in yeast and on gene expression data from a study of Arabidopsis thaliana under pathogen infection. The latter reveals a number of biologically striking findings.

Availability: Matlab code for our method is available at http://www.wsbc.warwick.ac.uk/stevenkiddle/tcap.html.

Contact: s.j.kiddle@warwick.ac.uk; s.n.mukherjee@warwick.ac.uk

On the beta-binomial model for analysis of spectral count data in label-free tandem mass spectrometry-based proteomics

Pham, T. V., Piersma, S. R., Warmoes, M., Jimenez, C. R. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: Spectral count data generated from label-free tandem mass spectrometry-based proteomic experiments can be used to quantify protein's abundances reliably. Comparing spectral count data from different sample groups such as control and disease is an essential step in statistical analysis for the determination of altered protein level and biomarker discovery. The Fisher's exact test, the G-test, the t-test and the local-pooled-error technique (LPE) are commonly used for differential analysis of spectral count data. However, our initial experiments in two cancer studies show that the current methods are unable to declare at 95% confidence level a number of protein markers that have been judged to be differential on the basis of the biology of the disease and the spectral count numbers. A shortcoming of these tests is that they do not take into account within- and between-sample variations together. Hence, our aim is to improve upon existing techniques by incorporating both the within- and between-sample variations.

Result: We propose to use the beta-binomial distribution to test the significance of differential protein abundances expressed in spectral counts in label-free mass spectrometry-based proteomics. The beta-binomial test naturally normalizes for total sample count. Experimental results show that the beta-binomial test performs favorably in comparison with other methods on several datasets in terms of both true detection rate and false positive rate. In addition, it can be applied for experiments with one or more replicates, and for multiple condition comparisons. Finally, we have implemented a software package for parameter estimation of two beta-binomial models and the associated statistical tests.

Availability and implementation: A software package implemented in R is freely available for download at http://www.oncoproteomics.nl/.

Contact: t.pham@vumc.nl

Supplementary information: Supplementary data are available at Bioinformatics online.

Mixtures of regression models for time course gene expression data: evaluation of initialization and random effects

Scharl, T., Gru, B., Leisch, F. — Tue, 02 Feb 2010 07:37:59 PST

Summary: Finite mixture models are routinely applied to time course microarray data. Due to the complexity and size of this type of data, the choice of good starting values plays an important role. So far initialization strategies have only been investigated for data from a mixture of multivariate normal distributions. In this work several initialization procedures are evaluated for mixtures of regression models with and without random effects in an extensive simulation study on different artificial datasets. Finally, these procedures are also applied to a real dataset from Escherichia coli.

Availability: The latest release versions of R packages flexmix, gcExplorer and kernlab are always available from CRAN (http://cran.r-project.org/).

Contact: theresa.scharl@ci.tuwien.ac.at

Supplementary information: Supplementary data are available at Bioinformatics online.

Quantifying the biological significance of gene ontology biological processes--implications for the analysis of systems-wide data

Chagoyen, M., Pazos, F. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: Gene Ontology (GO), the de facto standard for representing protein functional aspects, is being used beyond the primary goal for which it is designed: protein functional annotation. It is increasingly used to evaluate large sets of relationships between proteins, e.g. protein–protein interactions or mRNA co-expression, under the assumption that related proteins tend to have the same or similar GO terms. Nevertheless, this assumption only holds for terms representing functional groups with biological significance (‘classes’), and not for the ones representing human-imposed aggregations or conceptualizations lacking a biological rationale (‘categories’).

Results: Using a data-driven approach based on a set of high-quality functional associations, we quantify the functional coherence of GO biological process (GO:BP) terms as well as their explicit and implicit relationships, trying to distinguish classes and categories. We show that the quantification used is in agreement with the distinction one would intuitively make between these two concepts. As not all GO:BP terms and relationships are equally supported by current functional associations, any detailed validation of new experimental data using GO:BP, beyond whole-system statistics, should take such unbalance into account.

Contact: pazos@cnb.csic.es

Supplementary information: Supplementary data are available at Bioinformatics online.

Protein complex prediction based on simultaneous protein interaction network

Jung, S. H., Hyun, B., Jang, W.-H., Hur, H.-Y., Han, D.-S. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: The increase in the amount of available protein–protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions.

Results: In this article, a method of refining PPIN is proposed that uses the structural interface data of protein pairs for protein complex predictions. A simultaneous protein interaction network (SPIN) is introduced to specify mutually exclusive interactions (MEIs) as indicated from the overlapping interfaces and to exclude competition from MEIs that arise during the detection of protein complexes. After constructing SPINs, naive clustering algorithms are applied to the SPINs for protein complex predictions. The evaluation results show that the proposed method outperforms the simple PPIN-based method in terms of removing false positive proteins in the formation of complexes. This shows that excluding competition between MEIs can be effective for improving prediction accuracy in general computational approaches involving protein interactions.

Availability: http://code.google.com/p/simultaneous-pin/

Contact: dshan@kaist.ac.kr

Supplementary information: Supplementary data are available at Bioinformatics online.

Robust biomarker identification for cancer diagnosis with ensemble feature selection methods

Abeel, T., Helleputte, T., Van de Peer, Y., Dupont, P., Saeys, Y. — Tue, 02 Feb 2010 07:37:59 PST

Motivation: Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high-dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of biomarkers is an important issue, as it may greatly influence subsequent biological validations. In addition, a more robust set of markers may strengthen the confidence of an expert in the results of a selection method.

Results: Our first contribution is a general framework for the analysis of the robustness of a biomarker selection algorithm. Secondly, we conducted a large-scale analysis of the recently introduced concept of ensemble feature selection, where multiple feature selections are combined in order to increase the robustness of the final set of selected features. We focus on selection methods that are embedded in the estimation of support vector machines (SVMs). SVMs are powerful classification models that have shown state-of-the-art performance on several diagnosis and prognosis tasks on biological data. Their feature selection extensions also offered good results for gene selection tasks. We show that the robustness of SVMs for biomarker discovery can be substantially increased by using ensemble feature selection techniques, while at the same time improving upon classification performances. The proposed methodology is evaluated on four microarray datasets showing increases of up to almost 30% in robustness of the selected biomarkers, along with an improvement of ~15% in classification performance. The stability improvement with ensemble methods is particularly noticeable for small signature sizes (a few tens of genes), which is most relevant for the design of a diagnosis or prognosis model from a gene signature.

Contact: yvan.saeys@psb.ugent.be

Supplementary information: Supplementary data are available at Bioinformatics online.

sORF finder: a program package to identify small open reading frames with high coding potential

Hanada, K., Akiyama, K., Sakurai, T., Toyoda, T., Shinozaki, K., Shiu, S.-H. — Tue, 02 Feb 2010 07:37:59 PST

Summary: sORF finder is a program package for identifying small open reading frames (sORFs) with high-coding potential. This application allows the identification of coding sORFs according to the nucleotide composition bias among coding sequences and the potential functional constraint at the amino acid level through evaluation of synonymous and non-synonymous substitution rates.

Availability: Online tools and source codes are freely available at http://evolver.psc.riken.jp/

Contact: kohanada@psc.riken.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

Tablet--next generation sequence assembly visualization

Tue, 02 Feb 2010 07:37:59 PST

Summary: Tablet is a lightweight, high-performance graphical viewer for next-generation sequence assemblies and alignments. Supporting a range of input assembly formats, Tablet provides high-quality visualizations showing data in packed or stacked views, allowing instant access and navigation to any region of interest, and whole contig overviews and data summaries. Tablet is both multi-core aware and memory efficient, allowing it to handle assemblies containing millions of reads, even on a 32-bit desktop machine.

Availability: Tablet is freely available for Microsoft Windows, Apple Mac OS X, Linux and Solaris. Fully bundled installers can be downloaded from http://bioinf.scri.ac.uk/tablet in 32- and 64-bit versions.

Contact: tablet@scri.ac.uk

QDD: a user-friendly program to select microsatellite markers and design primers from large sequencing projects

Tue, 02 Feb 2010 07:37:59 PST

Summary: QDD is an open access program providing a user-friendly tool for microsatellite detection and primer design from large sets of DNA sequences. The program is designed to deal with all steps of treatment of raw sequences obtained from pyrosequencing of enriched DNA libraries, but it is also applicable to data obtained through other sequencing methods, using FASTA files as input. The following tasks are completed by QDD: tag sorting, adapter/vector removal, elimination of redundant sequences, detection of possible genomic multicopies (duplicated loci or transposable elements), stringent selection of target microsatellites and customizable primer design. It can treat up to one million sequences of a few hundred base pairs in the tag-sorting step, and up to 50 000 sequences in a single input file for the steps involving estimation of sequence similarity.

Availability: QDD is freely available under the GPL licence for Windows and Linux from the following web site: http://www.univ-provence.fr/gsite/Local/egee/dir/meglecz/QDD.html

Contact: emese.meglecz@univ-provence.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

Tmod: toolbox of motif discovery

Sun, H., Yuan, Y., Wu, Y., Liu, H., Liu, J. S., Xie, H. — Tue, 02 Feb 2010 07:38:00 PST

Summary: Motif discovery is an important topic in computational transcriptional regulation studies. In the past decade, many researchers have contributed to the field and many de novo motif-finding tools have been developed, each may have a different strength. However, most of these tools do not have a user-friendly interface and their results are not easily comparable. We present a software called Toolbox of Motif Discovery (Tmod) for Windows operating systems. The current version of Tmod integrates 12 widely used motif discovery programs: MDscan, BioProspector, AlignACE, Gibbs Motif Sampler, MEME, CONSENSUS, MotifRegressor, GLAM, MotifSampler, SeSiMCMC, Weeder and YMF. Tmod provides a unified interface to ease the use of these programs and help users to understand the tuning parameters. It allows plug-in motif-finding programs to run either separately or in a batch mode with predetermined parameters, and provides a summary comprising of outputs from multiple programs. Tmod is developed in C++ with the support of Microsoft Foundation Classes and Cygwin. Tmod can also be easily expanded to include future algorithms.

Availability: Tmod is available for download at http://www.fas.harvard.edu/~junliu/Tmod/

Contact: xhwei65@nudt.edu.cn; jliu@stat.harvard.edu

webMGR: an online tool for the multiple genome rearrangement problem

Lin, C. H., Zhao, H., Lowcay, S. H., Shahab, A., Bourque, G. — Tue, 02 Feb 2010 07:38:00 PST

Summary: The algorithm MGR enables the reconstruction of rearrangement phylogenies based on gene or synteny block order in multiple genomes. Although MGR has been successfully applied to study the evolution of different sets of species, its utilization has been hampered by the prohibitive running time for some applications. In the current work, we have designed new heuristics that significantly speed up the tool without compromising its accuracy. Moreover, we have developed a web server (webMGR) that includes elaborate web output to facilitate navigation through the results.

Availability: webMGR can be accessed via http://www.gis.a-star.edu.sg/~bourque. The source code of the improved standalone version of MGR is also freely available from the web site.

Contact: bourque@gis.a-star.edu.sg

Supplementary information: Supplementary data are available at Bioinformatics online.

PCIT: an R package for weighted gene co-expression networks based on partial correlation and information theory approaches

Watson-Haigh, N. S., Kadarmideen, H. N., Reverter, A. — Tue, 02 Feb 2010 07:38:00 PST

Summary: We make the PCIT algorithm, used for detecting meaningful gene–gene associations in co-expression networks, available as an R package. Automatic detection of a suitable parallel environment is used such that scripts are portable between parallel and non-parallel environments with no modification of the script.

Availability and implementation: Source code and binaries freely available (under GPL-3) for download via CRAN at http://cran.r-project.org/package=PCIT, implemented in R and supported on Linux and MS Windows.

Contact: nathan.watson-haigh@csiro.au

DR-Integrator: a new analytic tool for integrating DNA copy number and gene expression data

Salari, K., Tibshirani, R., Pollack, J. R. — Tue, 02 Feb 2010 07:38:00 PST

Summary: DNA copy number alterations (CNA) frequently underlie gene expression changes by increasing or decreasing gene dosage. However, only a subset of genes with altered dosage exhibit concordant changes in gene expression. This subset is likely to be enriched for oncogenes and tumor suppressor genes, and can be identified by integrating these two layers of genome-scale data. We introduce DNA/RNA-Integrator (DR-Integrator), a statistical software tool to perform integrative analyses on paired DNA copy number and gene expression data. DR-Integrator identifies genes with significant correlations between DNA copy number and gene expression, and implements a supervised analysis that captures genes with significant alterations in both DNA copy number and gene expression between two sample classes.

Availability: DR-Integrator is freely available for non-commercial use from the Pollack Lab at http://pollacklab.stanford.edu/ and can be downloaded as a plug-in application to Microsoft Excel and as a package for the R statistical computing environment. The R package is available under the name ‘DRI’ at http://cran.r-project.org/. An example analysis using DR-Integrator is included as supplemental material.

Contact: ksalari@stanford.edu; pollack1@stanford.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Validation of double digest selective label database for sequenced prokaryotic genomes

Tue, 02 Feb 2010 07:38:00 PST

Summary: A database for simulation of double digest selective label (DDSL) typing technique has been created and validated against a sequenced strain (Salmonella enterica serovar Typhimurium strain LT2). In silico bands were in agreement with experimental, and the technique was able to discriminate among strains belonging to the same species. When compared with other strain discrimination techniques, DDSL showed a higher discriminatory power. The database contains precomputed data which may be searched to retrieve experimental conditions for typing all up-to-dated sequenced prokaryotic microorganisms.

Availability: This is a new resource for molecular biology freely available on the Internet at http://insilico.ehu.es/DDSL

Contact: joseba.bikandi@ehu.es

pegas: an R package for population genetics with an integrated-modular approach

Paradis, E. — Tue, 02 Feb 2010 07:38:00 PST

Summary: pegas (Population and Evolutionary Genetics Analysis System) is a new package for the analysis of population genetic data. It is written in R and is integrated with two other existing R packages (ape and adegenet). pegas provides functions for standard population genetic methods, as well as low-level functions for developing new methods. The flexible and efficient graphical capabilities of R are used for plotting haplotype networks as well as for other functionalities. pegas emphasizes the need to further develop an integrated–modular approach for software dedicated to the analysis of population genetic data.

Availability: pegas is distributed through the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/pegas/index.html Further information may be found at: http://ape.mpl.ird.fr/pegas/

Contact: emmanuel.paradis@ird.fr

Annotation and merging of SBML models with semanticSBML

Krause, F., Uhlendorf, J., Lubitz, T., Schulz, M., Klipp, E., Liebermeister, W. — Tue, 02 Feb 2010 07:38:00 PST

Summary: Systems Biology Markup Language (SBML) is the leading exchange format for mathematical models in Systems Biology. Semantic annotations link model elements with external knowledge via unique database identifiers and ontology terms, enabling software to check and process models by their biochemical meaning. Such information is essential for model merging, one of the key steps towards the construction of large kinetic models. SemanticSBML is a tool that helps users to check and edit MIRIAM annotations and SBO terms in SBML models. Using a large collection of biochemical names and database identifiers, it supports modellers in finding the right annotations and in merging existing models. Initially, an element matching is derived from the MIRIAM annotations and conflicting element attributes are categorized and highlighted. Conflicts can then be resolved automatically or manually, allowing the user to control the merging process in detail.

Availability: SemanticSBML comes as a free software written in Python and released under the GPL 3. A Debian package, a source package for other Linux distributions, a Windows installer and an online version of semanticSBML with limited functionality are available at http://www.semanticsbml.org. A preinstalled version can be found on the Linux live DVD SB.OS, available at http://www.sbos.eu.

Contact: wolfram.liebermeister@biologie.hu-berlin.de

Supplementary information: Supplementary data are available at Bioinformatics online.

PathGen: a transitive gene pathway generator

Tue, 02 Feb 2010 07:38:00 PST

Summary: Many online sources of gene interaction networks supply rich visual data regarding gene pathways that can aid in the study of biological processes, disease research and drug discovery. PathGen incorporates data from several sources to create transitive connections that span multiple gene interaction databases. Results are displayed in a comprehensible graphical format, showing gene interaction type and strength, database source and microarray expression data. These features make PathGen a valuable tool for in silico discovery of novel gene interaction pathways, which can be experimentally tested and verified. The usefulness of PathGen interaction analyses was validated using genes connected to the altered facial development related to Down syndrome.

Availability: http://dna.cs.byu.edu/pathgen

Contact: clement@cs.byu.edu

Supplementary Information: Supplementary data are available at Bioinformatics online. Further information is available at http://dna.cs.byu.edu/pathgen/PathGenSupplemental.pdf

CellMC--a multiplatform model compiler for the Cell Broadband Engine and x86

Caulfield, E., Hellander, A. — Tue, 02 Feb 2010 07:38:00 PST

Motivation: Gillespie's stochastic simulation algorithm (SSA) is often the most tractable method to study stochastic models of biochemical systems. The algorithm itself is very simple and a natural target for implementation on specialized architectures such as the Cell Broadband Engine (Cell/BE). We have developed CellMC, a multiplatform SBML model compiler implementing a vectorized version of SSA for use on Cell/BE or x86 PCs.

Availability: The code is freely available from http://www.cellmc.org. It will run on a wide variety of x86 computers running Linux/MacOSX (Darwin) and on Cell/BE computers such as the Sony PlayStation3 (PS3) and the IBM BladeCenter QS22. CellMC requires gcc, libxml2 and libxslt, all of which are installed by default on most of the supported platforms.

Contact: info@www.cellmc.org; andreas.hellander@it.uu.se

Supplementary information: Supplementary data are available at Bioinformatics online.

ChiBE: interactive visualization and manipulation of BioPAX pathway models

Babur, O., Dogrusoz, U., Demir, E., Sander, C. — Tue, 02 Feb 2010 07:38:00 PST

Summary: Representing models of cellular processes or pathways in a graphically rich form facilitates interpretation of biological observations and generation of new hypotheses. Solving biological problems using large pathway datasets requires software that can combine data mapping, querying and visualization as well as providing access to diverse data resources on the Internet. ChiBE is an open source software application that features user-friendly multi-view display, navigation and manipulation of pathway models in BioPAX format. Pathway views are rendered in a feature-rich format, and may be laid out and edited with state-of-the-art visualization methods, including compound or nested structures for visualizing cellular compartments and molecular complexes. Users can easily query and visualize pathways through an integrated Pathway Commons query tool and analyze molecular profiles in pathway context.

Availability: http://www.bilkent.edu.tr/%7Ebcbi/chibe.html

Contact: ugur@cs.bilkent.edu.tr

Supplementary information: Supplementary data are available at Bioinformatics online.

DataPflex: a MATLAB-based tool for the manipulation and visualization of multidimensional datasets

Hendriks, B. S., Espelin, C. W. — Tue, 02 Feb 2010 07:38:00 PST

Motivation: DataPflex is a MATLAB-based application that facilitates the manipulation and visualization of multidimensional datasets. The strength of DataPflex lies in the intuitive graphical user interface for the efficient incorporation, manipulation and visualization of high-dimensional data that can be generated by multiplexed protein measurement platforms including, but not limited to Luminex or Meso-Scale Discovery. Such data can generally be represented in the form of multidimensional datasets [for example (time x stimulation x inhibitor x inhibitor concentration x cell type x measurement)]. For cases where measurements are made in a combinational fashion across multiple dimensions, there is a need for a tool to efficiently manipulate and reorganize such data for visualization. DataPflex accepts data consisting of up to five arbitrary dimensions in addition to a measurement dimension. Data are imported from a simple .xls format and can be exported to MATLAB or .xls. Data dimensions can be reordered, subdivided, merged, normalized and visualized in the form of collections of line graphs, bar graphs, surface plots, heatmaps, IC50’s and other custom plots. Open source implementation in MATLAB enables easy extension for custom plotting routines and integration with more sophisticated analysis tools.

Availability: DataPflex is distributed under the GPL license (http://www.gnu.org/licenses/) together with documentation, source code and sample data files at: http://code.google.com/p/datapflex.

Contact: DataPflexinfo@gmail.com; bhendriks@merrimackpharma.com

Supplementary information: Supplementary data available at Bioinformatics online.

The Locus Lookup tool at MaizeGDB: identification of genomic regions in maize by integrating sequence information with physical and genetic maps

Tue, 02 Feb 2010 07:38:00 PST

Summary: Methods to automatically integrate sequence information with physical and genetic maps are scarce. The Locus Lookup tool enables researchers to define windows of genomic sequence likely to contain loci of interest where only genetic or physical mapping associations are reported. Using the Locus Lookup tool, researchers will be able to locate specific genes more efficiently that will ultimately help them develop a better maize plant. With the availability of the well-documented source code, the tool can be easily adapted to other biological systems.

Availability: The Locus Lookup tool is available on the web at http://maizegdb.org/cgi-bin/locus_lookup.cgi. It is implemented in PHP, Oracle and Apache, with all major browsers supported. Source code is freely available for download at http://ftp.maizegdb.org/open_source/locus_lookup/.

Contact: taner.sen@ars.usda.gov

Over-optimism in bioinformatics research

Boulesteix, A.-L. — Tue, 02 Feb 2010 07:38:00 PST

Pitfalls of supervised feature selection

Smialowski, P., Frishman, D., Kramer, S. — Tue, 02 Feb 2010 07:38:00 PST