Bioinformatics - current issue

On correcting the overestimation of the permutation-based false discovery rate estimator

Jiao, S., Zhang, S. — 2008-07-19

Motivation: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR), which is defined as the expected percentage of the number of false positive genes among the claimed significant genes. As a consequence, the accuracy of the FDR estimators will be important for correctly controlling FDR. Xie et al. found that the standard permutation method of estimating FDR is biased and proposed to delete the predicted differentially expressed (DE) genes in the estimation of FDR for one-sample comparison. However, we notice that the formula of the FDR used in their paper is incorrect. This makes the comparison results reported in their paper unconvincing. Other problems with their method include the biased estimation of FDR caused by over- or under-deletion of DE genes in the estimation of FDR and by the implicit use of an unreasonable estimator of the true proportion of equivalently expressed (EE) genes. Due to the great importance of accurate FDR estimation in microarray data analysis, it is necessary to point out such problems and propose improved methods.

Results: Our results confirm that the standard permutation method overestimates the FDR. With the correct FDR formula, we show the method of Xie et al. always gives biased estimation of FDR: it overestimates when the number of claimed significant genes is small, and underestimates when the number of claimed significant genes is large. To overcome these problems, we propose two modifications. The simulation results show that our estimator gives more accurate estimation.

Contact: szhang3@unl.edu

OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar

Viklund, H., Elofsson, A. — 2008-07-19

Motivation: As -helical transmembrane proteins constitute roughly 25% of a typical genome and are vital parts of many essential biological processes, structural knowledge of these proteins is necessary for increasing our understanding of such processes. Because structural knowledge of transmembrane proteins is difficult to attain experimentally, improved methods for prediction of structural features of these proteins are important.

Results: OCTOPUS, a new method for predicting transmembrane protein topology is presented and benchmarked using a dataset of 124 sequences with known structures. Using a novel combination of hidden Markov models and artificial neural networks, OCTOPUS predicts the correct topology for 94% of the sequences. In particular, OCTOPUS is the first topology predictor to fully integrate modeling of reentrant/membrane-dipping regions and transmembrane hairpins in the topological grammar.

Availability: OCTOPUS is available as a web server at http://octopus.cbr.su.se.

Contact: arne@bioinfo.se

Supplementary information: Supplementary data are available at Bioinformatics online.

Modeling promoter grammars with evolving hidden Markov models

Won, K.-J., Sandelin, A., Marstrand, T. T., Krogh, A. — 2008-07-19

Motivation: Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation.

Results: With the goal of automatically deciphering such regulatory structures, we present a method that iteratively evolves an ensemble of regulatory grammars using a hidden Markov Model (HMM) architecture composed of interconnected blocks representing transcription factor binding sites (TFBSs) and background regions of promoter sequences. The ensemble approach reduces the risk of overfitting and generally improves performance. We apply this method to identify TFBSs and to classify promoters preferentially expressed in macrophages, where it outperforms other methods due to the increased predictive power given by the grammar.

Availability: The software and the datasets are available from http://modem.ucsd.edu/won/eHMM.tar.gz

Contact: krogh@binf.ku.dk

Supplementary information: Supplementary data are available at Bioinformatics online.

Sequence-specific reconstruction from fragmentary databases using seed sequences: implementation and validation on SAGE, proteome and generic sequencing data

Sobreira, T. J. P., Gruber, A. — 2008-07-19

Motivation: DNA assembly programs classically perform an all-against-all comparison of reads to identify overlaps, followed by a multiple sequence alignment and generation of a consensus sequence. If the aim is to assemble a particular segment, instead of a whole genome or transcriptome, a target-specific assembly is a more sensible approach. GenSeed is a Perl program that implements a seed-driven recursive assembly consisting of cycles comprising a similarity search, read selection and assembly. The iterative process results in a progressive extension of the original seed sequence. GenSeed was tested and validated on many applications, including the reconstruction of nuclear genes or segments, full-length transcripts, and extrachromosomal genomes. The robustness of the method was confirmed through the use of a variety of DNA and protein seeds, including short sequences derived from SAGE and proteome projects.

Availability: GenSeed is available under the GNU General Public License at http://www.coccidia.icb.usp.br/genseed/

Contact: argruber@usp.br

Supplementary information: Supplementary data are available at http://www.coccidia.icb.usp.br/genseed/

Predicting protein function from domain content

Forslund, K., Sonnhammer, E. L. L. — 2008-07-19

Motivation: Computational assignment of protein function may be the single most vital application of bioinformatics in the post-genome era. These assignments are made based on various protein features, where one is the presence of identifiable domains. The relationship between protein domain content and function is important to investigate, to understand how domain combinations encode complex functions.

Results: Two different models are presented on how protein domain combinations yield specific functions: one rule-based and one probabilistic. We demonstrate how these are useful for Gene Ontology annotation transfer. The first is an intuitive generalization of the Pfam2GO mapping, and detects cases of strict functional implications of sets of domains. The second uses a probabilistic model to represent the relationship between domain content and annotation terms, and was found to be better suited for incomplete training sets. We implemented these models as predictors of Gene Ontology functional annotation terms. Both predictors were more accurate than conventional best BLAST-hit annotation transfer and more sensitive than a single-domain model on a large-scale dataset. We present a number of cases where combinations of Pfam-A protein domains predict functional terms that do not follow from the individual domains.

Availability: Scripts and documentation are available for download at http://sonnhammer.sbc.su.se/multipfam2go_source_docs.tar

Contact: Kristoffer.Forslund@sbc.su.se

Supplementary information: Supplementary data are available at Bioinformatics online.

Knowledge-based gene expression classification via matrix factorization

2008-07-19

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks.

Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.

Supplementary information: Supplementary data are available at Bioinformatics online.

Contact: elmar.lang@biologie.uni-regensburg.de

Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value

Boulesteix, A.-L., Porzelius, C., Daumer, M. — 2008-07-19

Motivation: In the context of clinical bioinformatics methods are needed for assessing the additional predictive value of microarray data compared to simple clinical parameters alone. Such methods should also provide an optimal prediction rule making use of all potentialities of both types of data: they should ideally be able to catch subtypes which are not identified by clinical parameters alone. Moreover, they should address the question of the additional predictive value of microarray data in a fair framework.

Results: We propose a novel but simple two-step approach based on random forests and partial least squares (PLS) dimension reduction embedding the idea of pre-validation suggested by Tibshirani and colleagues, which is based on an internal cross-validation for avoiding overfitting. Our approach is fast, flexible and can be used both for assessing the overall additional significance of the microarray data and for building optimal hybrid classification rules. Its efficiency is demonstrated through simulations and an application to breast cancer and colorectal cancer data.

Availability: Our method is implemented in the freely available R package ‘MAclinical’ which can be downloaded from http://www.stat.uni-muenchen.de/~socher/MAclinical

Contact: boulesteix@slcmsr.org

FIRMA: a method for detection of alternative splicing from exon array data

2008-07-19

Motivation: Analyses of EST data show that alternative splicing is much more widespread than once thought. The advent of exon and tiling microarrays means that researchers now have the capacity to experimentally measure alternative splicing on a genome wide level. New methods are needed to analyze the data from these arrays.

Results: We present a method, finding isoforms using robust multichip analysis (FIRMA), for detecting differential alternative splicing in exon array data. FIRMA has been developed for Affymetrix exon arrays, but could in principle be extended to other exon arrays, tiling arrays or splice junction arrays. We have evaluated the method using simulated data, and have also applied it to two datasets: a panel of 11 human tissues and a set of 10 pairs of matched normal and tumor colon tissue. FIRMA is able to detect exons in several genes confirmed by reverse transcriptase PCR.

Availability: R code implementing our methods is contributed to the package aroma.affymetrix.

Contact: epurdom@stat.berkeley.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Modeling immune system control of atherogenesis

Pappalardo, F., Musumeci, S., Motta, S. — 2008-07-19

Motivation: Atherosclerosis is a disease that is present in almost all humans, typically beginning in early adolescence. It is a human disease broadly investigated, that is amenable to quantitative analysis. Oxidized low-density lipoproteins (LDLs) and their autoantibodies are involved in the development of atherosclerosis in animal models, but their role in humans is still not clear. Computer models may represent a virtual environment to perform experiments not possible in human volunteers that can provide a useful instrument for monitoring both the evolution of atherosclerotic lesions and to quantify the efficacy of treatments, including vaccines, oriented to reduce the LDLs and their oxidized fraction.

Results: We report the application of an agent-based model to model both the immune response to atherogenesis and the atheromatous plaque progression in a generic artery wall. The level of oxidized LDLs, the immune humoral response with production of autoantibodies, the macrophages activity and the formation of foam cells are in good agreement with available clinical data, including the formation of atheromatous plaques in patients affected by hypercholesterolemia.

Availability: The model is available at http://www.immunogrid.eu/atherogenesis/

Contact: francesco@dmi.unict.it

Ensemble non-negative matrix factorization methods for clustering protein-protein interactions

Greene, D., Cagney, G., Krogan, N., Cunningham, P. — 2008-07-19

Motivation: When working with large-scale protein interaction data, an important analysis task is the assignment of pairs of proteins to groups that correspond to higher order assemblies. Previously a common approach to this problem has been to apply standard hierarchical clustering methods to identify such a groups. Here we propose a new algorithm for aggregating a diverse collection of matrix factorizations to produce a more informative clustering, which takes the form of a ‘soft’ hierarchy of clusters.

Results: We apply the proposed Ensemble non-negative matrix factorization (NMF) algorithm to a high-quality assembly of binary protein interactions derived from two proteome-wide studies in yeast. Our experimental evaluation demonstrates that the algorithm lends itself to discovering small localized structures in this data, which correspond to known functional groupings of complexes. In addition, we show that the algorithm also supports the assignment of putative functions for previously uncharacterized proteins, for instance the protein YNR024W, which may be an uncharacterized component of the exosome.

Contact: derek.greene@ucd.ie

Supplementary information: Supplementary data are available at http://mlg.ucd.ie/nmf.

FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology

2008-07-19

Summary: Next-generation sequencing can provide insight into protein–DNA association events on a genome-wide scale, and is being applied in an increasing number of applications in genomics and meta-genomics research. However, few software applications are available for interpreting these experiments. We present here an efficient application for use with chromatin-immunoprecipitation (ChIP-Seq) experimental data that includes novel functionality for identifying areas of gene enrichment and transcription factor binding site locations, as well as for estimating DNA fragment size distributions in enriched areas. The FindPeaks application can generate UCSC compatible custom ‘WIG’ track files from aligned-read files for short-read sequencing technology. The software application can be executed on any platform capable of running a Java Runtime Environment. Memory requirements are proportional to the number of sequencing reads analyzed; typically 4 GB permits processing of up to 40 million reads.

Availability: The FindPeaks 3.1 package and manual, containing algorithm descriptions, usage instructions and examples, are available at http://www.bcgsc.ca/platform/bioinfo/software/findpeaks Source files for FindPeaks 3.1 are available for academic use.

Contact: afejes@bcgsc.ca

DNAlive: a tool for the physical analysis of DNA at the genomic scale

Goni, J. R., Fenollosa, C., Perez, A., Torrents, D., Orozco, M. — 2008-07-19

Summary: DNAlive is a tool for the analysis and graphical display of structural and physical characteristics of genomic DNA. The web server implements a wide repertoire of metrics to derive physical information from DNA sequences with a powerful interface to derive 3D information on large sequences of both naked and protein-bound DNAs. Furthermore, it implements a mesoscopic Metropolis code which allows the inexpensive study of the dynamic properties of chromatin fibers. In addition, our server also surveys other protein and genomic databases allowing the user to combine and explore the physical properties of selected DNA in the context of functional features annotated on those regions.

Availability: http://mmb.pcb.ub.es/DNAlive/ ; http://www.inab.org/

Contact: modesto@mmb.pcb.ub.es

Supplementary information: Supplementary data are available at Bioinformatics online.

ChemmineR: a compound mining framework for R

Cao, Y., Charisi, A., Cheng, L.-C., Jiang, T., Girke, T. — 2008-07-19

Motivation: Software applications for structural similarity searching and clustering of small molecules play an important role in drug discovery and chemical genomics. Here, we present the first open-source compound mining framework for the popularstatistical programming environment R. The integration with a powerful statistical environment maximizes the flexibility, expandability and programmability of the provided analysis functions.

Results: We discuss the algorithms and compound mining utilities provided by the R package ChemmineR. It contains functions for structural similarity searching, clustering of compound libraries with a wide spectrum of classification algorithms and various utilities for managing complex compound data. It also offers a wide range of visualization functions for compound clusters and chemical structures. The package is well integrated with the online ChemMine environment and allows bidirectional communications between the two services.

Availability: ChemmineR is freely available as an R package from the ChemMine project site: http://bioweb.ucr.edu/ChemMineV2/chemminer

Contact: thomas.girke@ucr.edu

A correction for estimating error when using the Local Pooled Error Statistical Test

Murie, C., Nadon, R. — 2008-07-19

Jain et al. introduced the Local Pooled Error (LPE) statistical test designed for use with small sample size microarray gene-expression data. Based on an asymptotic proof, the test multiplicatively adjusts the standard error for a test of differences between two classes of observations by /2 due to the use of medians rather than means as measures of central tendency. The adjustment is upwardly biased at small sample sizes, however, producing fewer than expected small P-values with a consequent loss of statistical power. We present an empirical correction to the adjustment factor which removes the bias and produces theoretically expected P-values when distributional assumptions are met. Our adjusted LPE measure should prove useful to ongoing methodological studies designed to improve the LPE's; performance for microarray and proteomics applications and for future work for other high-throughput biotechnologies.

Availability: The software is implemented in the R language and can be downloaded from the Bioconductor project website (http://www.bioconductor.org).

Contact: robert.nadon@mcgill.ca

LOT: a tool for linkage analysis of ordinal traits for pedigree data

Zhang, M., Feng, R., Chen, X., Hu, B., Zhang, H. — 2008-07-19

Summary: Existing linkage-analysis methods address binary or quantitative traits. However, many complex diseases and human conditions, particularly behavioral disorders, are rated on ordinal scales. Herein, we introduce, LOT, a tool that performs linkage analysis of ordinal traits for pedigree data. It implements a latent-variable proportional-odds logistic model that relates inheritance patterns to the distribution of the ordinal trait. The likelihood-ratio test is used for testing evidence of linkage.

Availability: The LOT program is available for download at http://c2s2.yale.edu/software/LOT/

Contact: heping.zhang@yale.edu

Optimal vaccination schedules using simulated annealing

Pennisi, M., Catanuto, R., Pappalardo, F., Motta, S. — 2008-07-19

Summary: Since few years the problem of finding optimal solutions for drug or vaccine protocols have been tackled using system biology modeling. These approaches are usually computationally expensive. Our previous experiences in optimizing vaccine or drug protocols using genetic algorithms required the use of a high performance computing infrastructure for a couple of days. In the present article we show that by an appropriate use of a different optimization algorithm, the simulated annealing, we have been able to downsize the computational effort by a factor10². The new algorithm requires computational effort that can be achieved by current generation personal computers.

Availability: Software and additional data can be found at http://www.immunomics.eu/SA/

Contact: francesco@dmi.unict.it

MPIDB: the microbial protein interaction database

Goll, J., Rajagopala, S. V., Shiau, S. C., Wu, H., Lamb, B. T., Uetz, P. — 2008-07-19

Summary: The microbial protein interaction database (MPIDB) aims to collect and provide all known physical microbial interactions. Currently, 22 530 experimentally determined interactions among proteins of 191 bacterial species/strains can be browsed and downloaded. These microbial interactions have been manually curated from the literature or imported from other databases (IntAct, DIP, BIND, MINT) and are linked to 24 060 experimental evidences (PubMed ID, PSI-MI methods). In contrast to these databases, interactions in MPIDB are further supported by 8150 additional evidences based on interaction conservation, co-purification and 3D domain contacts (iPfam, 3did).

Availability: http://www.jcvi.org/mpidb/

Contact: jgoll@jcvi.org