Results: The ModFOLDclustQ method is competitive with leading clustering based MQAPs for the prediction of global model quality, yet it is up to 150 times faster than the previous version of the ModFOLDclust method at comparing models of small proteins (<60 residues) and over 5 times faster at comparing models of large proteins (>800 residues). Furthermore, a significant improvement in accuracy can be gained over the previous clustering based MQAPs by combining the scores from ModFOLDclustQ and ModFOLDclust to form the new ModFOLDclust2 method, with little impact on the overall time taken for each prediction.
Availability: The ModFOLDclustQ and ModFOLDclust2 methods are available to download from:
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Results: We used the effect of miRNAs over their targets for the detection of miRNA activity using mRNAs expression profiles. Here we describe the method, called T-REX (from Targets' Reverse EXpression), compare it to other similar applications, show its effectiveness and apply it to build activity maps. We used six different target predictions from each of four algorithms: TargetScan, PicTar, DIANA-microT and DIANA Union.
First, we proved the sensitivity and specificity of our technique in miRNA over-expression and knock-out animal models. Then, we used whole transcriptome data from acute myeloid leukemia to show that we could identify critical miRNAs in a real life, complex, clinically relevant dataset. Finally, we studied sixty-six different cellular conditions to confirm and extend the current knowledge on the role of miRNAs in cellular physiology and in cancer.
]]>Availability: GATE is available for download and is free for academic use from
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Results: We examined a variety of algorithms that estimate protein differential expression ratios from multiple peptide intensity measurements. Algorithms that account for the variation of measurement error with intensity were found to provide the most accurate estimates of differential abundance. A simple Sum-of-Intensities algorithm provided the best estimates of true protein ratios of all algorithms tested.
]]>Results: We develop a model for predicting nucleic acid secondary structure in the presence of single stranded binding proteins, and implement it as an extension of the Vienna RNA Package. All parameters needed to model nucleic acid secondary structures in the absence of proteins have been previously determined. This leaves the footprint and sequence dependent binding affinity of the protein as adjustable parameters of our model. Using this model we are able to predict the probability of the protein binding at any position in the nucleic acid sequence, the impact of the protein on nucleic acid base pairing, the end-to-end distance distribution for the nucleic acid, and FRET distributions for fluorophores attached to the nucleic acid.
Availability: Source code for our modified version of the Vienna RNA package is freely available at
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Results: We have developed an alternative splicing robust prediction method based on entropy (ARH). We can show that this measure copes with bias inherent in the analysis of alternative splicing such as the dependency of prediction performance on the number of exons or variable exon expression. In order to judge the performance of ARH. we have compared it with eight existing splicing prediction methods using experimental benchmark data and demonstrate that ARH is a well-performing new method for the prediction of splice variants.
Availability and Implementation: ARH is implemented in R and provided in the supplementary material.
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Supplementary Information: The supplementary material provides additional figures and tables, the R implementation of ARH, a basic implementation for the method comparison and the AEdb true positive set.
]]>Availability: SimBoolNet package (with manual) is freely available at
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Availability and Implementation: MATICCE is written in the open-source R language and freely available through the Comprehensive R Archive Network (
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Results: Our extensive experiments on both simulated data and real genome-wide data from Wellcome Trust Case Control Consortium (WTCCC) show that SNPRuler significantly outperforms its recent competitor. To our knowledge, SNPRuler is the first method that guarantees to find the epistatic interactions without exhaustive search. Our results indicate that finding epistatic interactions in GWAS is computationally attainable in practice.
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Results: In a widely used F2 reciprocal mating population for mapping imprinting genes, we herein propose a genomic imprinting model which describes additive, dominance, and imprinting effects of multiple imprinted quantitative trait loci (iQTL) for traits of interest. Depending upon the estimates of the above genetic effects, we categorized imprinting patterns into seven types, which provides a complete classification scheme for describing imprinting patterns. Bayesian model selection was employed to identify iQTL along with many genetic parameters in a computationally efficient manner. To make statistical inference on the imprinting types of iQTL detected, a set of Bayes factors were formulated using the posterior probabilities for the genetic effects being compared. We demonstrated the performance of the proposed method by computer simulation experiments and then applied this method to two real data sets. Our approach can be generally used to identify inheritance modes and determine the contribution of major genes for quantitative variations.
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Availability: Phenopedia and Genopedia can be freely accessed at
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Availability: inGAP is available at
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Supplementary information: Supplementary data are available at Bioinformatics online.
]]>Results: Here we develop such a model selection framework based on approximate Bayesian computation and employing sequential Monte Carlo sampling. We show that our approach can be applied across a wide range of biological scenarios, and we illustrate its use on real data describing influenza dynamics and the JAK-STAT signalling pathway. Bayesian model selection strikes a balance between the complexity of the simulation models and their ability to describe observed data. The present approach enables us to employ the whole formal apparatus to any system that can be (efficiently) simulated, even when exact likelihoods are computationally intractable.
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Supplementary Information: Tutorial on ABC rejection and ABC SMC for parameter estimation and model selection. Derivation of ABC SMC model selection algorithms. Supplementary figures and datasets.
]]>Results: We present MicroRazerS, a tool optimized for mapping small RNAs onto a reference genome. It is an order of magnitude faster than Mega BLAST and comparable in speed to other short read mapping tools. In addition, it is more sensitive and easy to handle and adjust.
Availability: MicroRazerS is part of the SeqAn C++ library and can be downloaded from
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Results: We have implemented a model editor that incorporates model aggregation, and we suggest supporting extensions to the Systems Biology Markup Language (SBML) Level 3. We illustrate aggregation with a model of the eukaryotic cell cycle ‘engine’ created from smaller pieces.
Availability: Java implementations of our SBML proposal are available in the JigCell Aggregation Connector. See
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Results: In this paper, we present GNUMAP (Genomic Nextgeneration Universal MAPper), a program capable of overcoming two major obstacles in the mapping of reads from next-generation sequencing runs. First, we have created an algorithm that probabilistically maps reads to repeat regions in the genome on a quantitative basis. Second, we have developed a probabilistic Needleman-Wunsch algorithm which utilizes _prb.txt and _int.txt files produced in the Solexa/Illumina pipeline to improve the mapping accuracy for lower quality reads and increase the amount of usable data produced in a given experiment.
Availability: The source code for the software can be downloaded from
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Results: We use protein profile similarity screening to identify candidate proteins whose abundances are post-transcriptionally controlled by the Anaphase Promoting Complex (APC/C), a specific E3 ubiquitin ligase that is a master regulator of the cell cycle. Results are compared with an established protein correlation profiling method. The proposed procedure yields a 50.9-fold enrichment of co-regulated protein candidates and a 2.5-fold improvement over the previous method.
Availability: A MATLAB toolbox is available from
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Results: A Bayesian model for estimating the haplotypes and their frequencies from pooled allelic observations is introduced. The model combines an idea of using database information for haplotype estimation with a computationally efficient multinormal approximation. In addition, the model treats the number and structures of the unknown haplotypes as random variables whose joint posterior distribution is estimated. The results on real human data from the HapMap database show that the proposed method provides significant improvements over the existing methods.
Availability: A reversible-jump Markov chain Monte Carlo algorithm for analysing the model is implemented in a program called Hippo. For comparisons, an approximate EM-algorithm that utilises database information about the existing haplotypes is implemented in a program called AEML. The source codes written in C (using Gnu Scientific Library) are available at
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Results: In this paper, we propose a novel annotation quality or confidence scoring scheme, called Annotation Confidence Score (ACS), using a genome comparison approach. The scoring scheme is computed by combining sequence and textual annotation similarity using a modified version of a logistic curve. The most important feature of the proposed scoring scheme is to generate a score that reflects the excellence in annotation quality of genes by automatically adjusting the number of genomes used to compute the score and their phylogenetic distance. Extensive experiments with bacterial genomes showed that the proposed scoring scheme generated scores for annotation quality according to the quality of annotation regardless of the number of reference genomes and their phylogenetic distance.
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Availability: The R package and a quick-start vignette is available at
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Supplementary information: Supplementary data are available at Bioinformatics online.
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Results: Target predictions for the cyanobacterial small RNA Yfr1 were carried out with I
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Results: R users can take advantage of the better performance provided by an Nvidia GPU.
Availability: The package is available from CRAN, the R project's repository of packages, at
More information about our gputools R package is available at
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Results: We apply the CTGDR (Clustering Threshold Gradient Directed Regularization) method to genetic association studies using SNPs, analyze data from an association study of NHL, and identify prognosis signatures for diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL), the two most common subtypes of NHL. With the CTGDR method, we are able to account for the joint effects of multiple genes/SNPs, whereas most existing studies are single-marker based. In addition, we are able to account for the "gene and SNPwithin-gene" hierarchical structure and identify not only predictive genes but also predictive SNPs within identified genes. In contrast, existing studies are limited to either gene or SNP identification, but not both. We propose using resampling methods to evaluate the predictive power and reproducibility of identified genes and SNPs. Simulation study and data analysis suggest satisfactory performance of the CTGDR method.
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Results: We developed an interaction detection method and explored usefulness of various features in automatically identifying PPIs in text. The results show that our approach outperforms other systems using the AImed dataset. In the tests where our system achieves better precision with reduced recall, we discuss possible approaches for improvement. In addition to test datasets, we evaluated performance on interactions from five human-curated databases—BIND, DIP, HPRD, IntAct and MINT—where our system consistently identified evidence for about 60% of interactions when both proteins appear in at least one sentence in the PubMed abstract. We then applied the system to extract articles from PubMed to annotate known, high-throughput and interologous interactions in I2D.
Availability: The data and software are available at:
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Availability: The Ancestors 1.0 is available at <
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Availability: Source code and documentation are available for noncommercial use at
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Supplementary Information: Supplementary data are available at Bioinformatics online.
]]>Results: This paper characterizes reporting bias with several statistics and computes these statistics in a large simulation study using both modeled and real data. The results appear as curves giving the different reporting biases as functions of the number of samples tested when reporting only the best or second best performance. It does this for two classification rules, linear discriminant analysis (LDA) and 3 nearest-neighbor (3NN), and for filter and wrapper feature selection, t-test and sequential forward search. These were chosen on account of their well-studied properties and because they were amenable to the extremely large amount of processing required for the simulations. The results across all the experiments are consistent: there is generally large bias overriding what would be considered a significant performance differential, when reporting the best or second best performing data set. We conclude that there needs to be a database of data sets and that, for those studies depending on real data, results should be reported for all data sets in the database.
Availability: Companion website at
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Results: We introduce a novel evolutionary fuzzy clustering (EFC) algorithm, which is able to deal with overlapping clusterings. Our method assesses these overlaps by using cluster membership degrees, which we use here as a confidence measure for individual samples to be assigned to a given tumor type. We first demonstrate the usefulness of our method using a synthetic aCGH dataset and subsequently show that EFC outperforms existing methods on four real datasets of aCGH tumor profiles involving four different cancer types. We also show that in general best performance is obtained using 1— Pearson correlation coefficient as a distance measure and that extra pre processing steps, such as segmentation and calling, lead to decreased clustering performance.
Availability: The source code of the program is available from
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Results: We present a new algorithm, Roll, implemented in a program named POCASA, which can predict binding sites by detecting pockets and cavities of proteins with a rolling sphere. To evaluate the performance of POCASA, a test with the same data set as used in several existing methods was carried out. POCASA achieved a high success rate of 77%. In addition, the test results indicated that POCASA can predict good shapes of ligand binding sites.
Availability: A web version of POCASA is freely available at
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Availability: G-compass is freely available at
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Availability: CRISPI free access at
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Availability: The software is available at
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Availability: NAViGaTOR is freely available to the research community from
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Results: We propose a new method for efficient protein family classification and for speeding up database searches with pHMMs as is necessary for large scale analysis scenarios. We employ simpler models of protein families called PSSM family models. For fast database search, we combine full text indexing, efficient exact p-value computation of PSSM match scores, and fast fragment chaining. The resulting method is well suited to pre-filter the set of sequences to be searched for subsequent database searches with pHMMs.
We achieved a classification performance only marginally inferior to hmmsearch, yet, results could be obtained in a fraction of runtime with a speedup of more than 64 fold. In experiments addressing the method's ability to pre-filter the sequence space for subsequent database searches with pHMMs, our method reduces the number of sequences to be searched with hmmsearch to only 0.80% of all sequences. The filter is very fast and leads to a total speedup of factor 43 over the unfiltered search while retaining more than 99.5% of the original results. In a lossless filter setup for hmmsearch on UniProtKB/Swiss-Prot, we observed a speedup of factor 92.
Availability: The presented algorithms are implemented in the program PoSSuMsearch2, available for download at
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Results: In an application to acute myeloid leukemia, our procedure is able to identify various regions on different chromosomes with characteristic abnormalities in gene expression and copy number data and shows a higher sensitivity to differences in abnormalities than standard approaches. While the results support various findings of previous studies, some new interesting DNA regions can be identified. In a simulation study, our procedure also shows more reliable results than standard approaches.
Availability: Code and data available as R packages edira and ediraAMLdata from
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Supplementary Information: Supplementary material is available at Bioinformatics online.
]]>Availability: The web server is freely available to non-commercial users at the following address:
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Supplementary Information: Supplementary data are available at
Results: We present an algorithm to extract (
Availability: Version 2 of kr can be tested via a web interface at
It is written in standard C and its source code is available under the GNU General Public License from the same web site.
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Results: We used the granular support vector machines (SVMs)repetitive undersampling method (GSVM-RU) to construct an SVM from luciferase inhibition bioassay data that the imbalance ratio of active/inactive is high (1/377). The best model recognized the active and inactive compounds at the accuracies of 86.60% and 88.89 with a total accuracy of 87.74%, by cross-validation test and blind test. These results demonstrate the robustness of the model in handling the intrinsic imbalance problem in HTS data and it can be used as a virtual screening tool to identify potential interference compounds in luciferase-based HTS experiments. Additionally, this method has also proved computationally efficient by greatly reducing the computational cost and can be easily adopted in the analysis of HTS data for other biological systems.
Availability: Data are publicly available in PubChem with AIDs of 773, 1006 and 1379.
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Supplementary information: Supplementary data are available at Bioinformatics online.
]]>Results: We present the COALESCE system for regulatory module prediction. The algorithm is efficient enough to discover expression biclusters and putative regulatory motifs in metazoan genomes (>20,000 genes) and very large microarray compendia (>10,000 conditions). Using Bayesian data integration, it can also include diverse supporting data types such as evolutionary conservation or nucleosome placement. We validate its performance using a functional evaluation of coclustered genes, known yeast and E. coli transcription factor targets, synthetic data, and various metazoan data compendia. In all cases, COALESCE performs as well or better than current biclustering and motif prediction tools, with high accuracy in functional and TF/target assignments and zero false positives on synthetic data. COALESCE provides an efficient and flexible plat-form within which large, diverse data collections can be integrated to predict metazoan regulatory networks.
Availability: Source code (C++) is available at
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Availability: The web interface is available at
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Supplementary information: Detailed documentation is available on the Exon Array Analyzer web site (
Results: This paper presents a new approach based on building several prediction models for the Bioinformatics journal. These models predict the citation count of an article within four years after publication (global models). To build these models, tokens found in the abstracts of Bioinformatics papers have been used as predictive features, along with other features like the journal sections and two-week post publication periods. To improve the accuracy of the global models, specific models have been built for each Bioinformatics journal section (Data and Text Mining, Databases and Ontologies, Gene Expression, Genetics and Population Analysis, Genome Analysis, Phylogenetics, Sequence Analysis, Structural Bioinformatics and Systems Biology). In these new models, the average success rate for predictions using the naive Bayes and logistic regression supervised classification methods was 89.4% and 91.5%, respectively, within the nine sections and for four-year time horizon.
Availability: Supplementary material on this experimental survey is available at
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Results: We have developed a desktop application, QSVanalyser, which allows high throughput quantification of the proportions of DNA sequences containing single-nucleotide variants. In reconstruction experiments, QSVanalyser accurately estimated the known relative proportions of SNVs. By analysing a large panel of genomic DNA samples, we demonstrate the ability of the software to analyse not only common biallelic SNVs, but also SNVs within a locus at which gene conversion between 4 genomic paralogues operates, and within another that is subject to copy number variation.
Availability and Implementation: QSVAnalyser is freely available at
Results: Here we present a novel Bayesian approach to align the complete spectra. The approach is based on a parametric model which assumes the spectrum and alignment function are Gaussian processes, but the alignment function is monotone. We show how to use the expectation-maximization algorithm to find the posterior mode of the set of alignment functions and the mean spectrum for a patient population. After alignment, we conduct tests while controlling for error attributable to multiple comparisons on the level of the peaks identified from the absolute mean spectra difference of two patient populations.
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Results: We have developed a method for adding full atomic detail to coarse-grain models of RNA 3D structures. Our method (C2A) uses geometries observed in known RNA crystal structures. Our method rebuilds full atomic detail from ideal coarse-grain backbones taken from crystal structures to within 1.87 to 3.31 Å RMSd of the full atomic crystal structure. When starting from coarse-grain models generated by the modeling tool NAST, our method builds full atomic structures that are within 1.00 Å RMSd of the starting structure. The resulting full atomic structures can be used as starting points for higher resolution modeling, thus bridging high and low resolution approaches to modeling RNA 3D structure.
Availability: Code for the C2A method, as well as the examples discussed in this paper, are freely available at
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Results: We designed a novel estimator for multiple alignments of structured RNAs, based on maximizing the expected accuracy of predictions. First, we define the maximum expected accuracy (MEA) estimator for pairwise alignment of RNA sequences. This maximizes the expected sum-of-pairs score (SPS) of a predicted alignment under a probability distribution of alignments given by marginalizing the Sankoff model. Then, by approximating the MEA estimator, we obtain an estimator whose time complexity is O(L3 + c2dL2) where L is the length of input sequences and both c and d are constants independent of L. The proposed estimator can handle uncertainty of secondary structures and alignments that are obstacles in Bioinformatics because it considers all the secondary structures and all the pairwise alignments as input sequences. Moreover, we integrate the probabilistic consistency transformation (PCT) on alignments into the proposed estimator. Computational experiments using six benchmark datasets indicate that the proposed method achieved a favorable SPS and was the fastest of many state–of–the–art tools for multiple alignments of structured RNAs.
Availability: The software called CentroidAlign, which is an implementation of the algorithm in this paper, is freely available on our website:
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Results: We generated sixteen million 35 bp reads from mRNA of each of two HapMap Yoruba individuals. When we mapped these reads to the human genome we found that, at heterozygous SNPs, there was a significant bias towards higher mapping rates of the allele in the reference sequence, compared to the alternative allele. Masking known SNP positions in the genome sequence eliminated the reference bias but, surprisingly, did not lead to more reliable results overall. We find that even after masking, ~5-10% of SNPs still have an inherent bias towards more effective mapping of one allele. Filtering out inherently biased SNPs removes 40% of the top signals of ASE. The remaining SNPs showing ASE are enriched in genes previously known to harbor cis-regulatory variation or known to show uniparental imprinting. Our results have implications for a variety of applications involving detection of alternate alleles from short-read sequence data.
Availability: Scripts, written in Perl and R, for simulating short reads, masking SNP variation in a reference genome, and analyzing the simulation output are available upon request from JFD. Raw short read data were deposited in GEO (
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Results: We have developed HTqPCR, a package for the R statistical computing environment, to enable the processing and analysis of qPCR data across multiple conditions and replicates.
Availability: HTqPCR and user documentation can be obtained from the Bioconductor project, or at
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Results: In this paper we analyse how the Escherichia coli metabolic network responds to pruning in the form of sequential removal of metabolites with highest degree. As expected this leads to network fragmentation, but the process by which it occurs suggest modularity and long range correlations within the network. We find that the pruned networks contain longer paths than the random expectation, and that the paths that survive the pruning also exhibit a lower cost (no. of involved metabolites) compared to random paths in the full metabolic network. Finally we confirm that paths detected by pruning overlap with known metabolic pathways. We conclude that pruning reveals functional pathways in metabolic networks, where currency metabolites may be seen as ingredients in a well-balanced soup in which main metabolic production lines are immersed.
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The release version 1.0 is a freely accessible database available at
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Results: The texture entropy and directionality of the muscle microscopy images were measured using 50 animals on day 0, 2, 4, 6, 8, 10, and 12 of adulthood. Results show that the entropy of the C. elegans pharynx tissues increases as the animal ages, but a sharper increase was measured between day two and day four, and between day eight and day 10. These results are in agreement with gene expression findings, and support the contention that the process of C. elegans aging has several distinct stages. This can indicate that C. elegans aging is driven by developmental pathways, rather than stochastic accumulation of damage.
Availability: The image data are freely available on the internet at
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Results: We introduce the rich internet application Qupe providing comprehensive data management and analysis functions for LCMS/MS experiments. Starting with the import of mass spectra data the system guides the experimenter through the process of protein identification by database search, the calculation of protein abundance ratios, and, in particular, the statistical evaluation of the quantification results including multivariate analysis methods such as analysis of variance or hierarchical cluster analysis. While a data model to store these results has been developed, a well-defined programming interface facilitates the integration of novel approaches. A compute cluster is utilised to distribute computationally intensive calculations, and a web service allows to interchange information with other –omics software applications. To demonstrate that Qupe represents a step forward in quantitative proteomics analysis an application study on Corynebacterium glutamicum has been carried out.
Availability and Implementation: Qupe is implemented in Java utilising Hibernate, Echo2, R and the Spring framework. We encourage the usage of the rich internet application in the sense of the "software as a service" concept, maintained on our servers and accessible at the following location:
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Results: In this paper, we propose a novel multi-agent architecture for the general purpose of functional sequence motif recognition. The approach takes advantage of the synergy provided by multiple agents through the employment of different agents equipped with distinctive problem solving skills and promotes the collaborations among them through decision maker (DM) agents that work as classifier ensembles. A genetic algorithm based fusion strategy is applied which offers evolutionary property to the DM agents. The consistency and robustness of the system are maintained by an evolvable agent that mediates the team of the ensemble agents. The combined effort of a recommendation system (Seer) and the self-learning mediator agent yields a successful identification of the most efficient agent deployment scheme at an early stage of the experimentation process, which has the potential of greatly reducing the computational cost of the system. Two concrete systems are constructed which aim at predicting two important sequence motifs – the translational initiation sites (TISs) and the core promoters. With the incorporation of three distinctive problem solver agents, the TIS predictor consistently outperforms most of the state-of-the-art approaches under investigation. Integrating three existing promoter predictors, our system is able to yield consistently good performance.
Availability: The program (MotifMAS) and the data sets are available upon request.
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Availability: The program is available free of charge from
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Supplementary information: Data used in the paper may be downloaded from
Results: Here we carry out network alignment using a protein functional similarity measure. We show that using functional similarity to map proteins across species improves network alignment in terms of functional coherence and overlap with experimentally verified protein complexes. Moreover, the results from functional similarity based network alignment display little overlap (<15%) with sequence similarity based alignment. Our combined approach integrating sequence and function based network alignment alongside graph clustering properties offers a 200% increase in coverage of experimental datasets and comparable accuracy to current network alignment methods.
Availability: Program binaries and source code is freely available at:
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Supplementary information: Supplementary data are available at Bioinformatics online
]]>Results: In this paper we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine ana-bolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size.
]]>Results: A simple method was developed that could distinguish between these two types with results that matched those made by eye. Applying this automatic method to a large dataset of 888 structures, chains at interfaces were categorized as non-surrounded or surrounded. It was found that the surrounded set had a significantly lower folding tendency using a sequence based measure, than the non-surrounded set. This suggests that before complexation, surrounded chains are relatively unstable and may be involved in "fly-casting". This is supported by the finding that terminal regions are overrepresented in the surrounded set.
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Results: Our analysis shows that homology-based transfer is slightly more discriminating than a support vector machine learner using profiles and predicted secondary structure. We combine these two approaches in a method called LIBRUS. On a benchmark of 885 sequence independent proteins, it achieves an area under the ROC curve (ROC) of 0.83 with 45% precision at 50% recall, a significant improvement over previous sequence-based efforts. On an independent benchmark set, a current method, FINDSITE, based on structure features achieves a 0.81 ROC with 54% precision at 50% recall while LIBRUS achieves a ROC of 0.82 with 39% precision at 50% recall at a smaller computational cost. When LIBRUS and FINDSITE predictions are combined, performance is increased beyond either reaching an ROC of 0.86 and 59% precision at 50% recall.
Availability: Software developed for this study is available at
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Results: We propose a method to improve the sensitivity of HMM domain detection by exploiting the tendency of the domains to appear preferentially with a few other favorite domains in a protein. When sequence information alone is not sufficient to warrant the presence of a particular domain, our method enables its detection on the basis of the presence of other Pfam or InterPro domains. Moreover, a shuffling procedure allows us to estimate the false discovery rate associated with the results. Applied to P. falciparum, our method identifies 585 new Pfam domains (versus the 3 683 already known domains in the Pfam database) with an estimated error rate below 20%. These new domains provide 387 new Gene Ontology annotations to the P. falciparum proteome. Analogous and congruent results are obtained when applying the method to related Plasmodium species (P. vivax and P. yoelii).
Availability: Supplementary Material and a database of the new domains and GO predictions achieved on Plasmodium proteins are available at
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Results: We perform integrative analysis of the 16 DGAP data sets that span multiple tissues, conditions, array types, laboratories, species, genetic backgrounds, and study designs. For each data set, we identify differentially expressed genes compared to control. Then, for the combined data, we rank genes according to the frequency with which they were found to be statistically significant across data sets. This analysis reveals RetSat as a widely shared component of mechanisms involved in insulin resistance and sensitivity and adds to the growing importance of the retinol pathway in diabetes, adipogenesis and insulin resistance. Top candidates obtained from our analysis have been confirmed in recent laboratory studies.
]]>Results: The proposed method was applied to the gene expression profiles and urine samples of 50 breast cancer patients and 50 normal persons. Nine altered metabolic pathways were identified from the breast cancer gene expression signatures. Among these altered metabolic pathways, four metabolic biomarkers (Homovanillate, 4-Hydroxyphenylacetate, 5-Hydroxyindoleacetate, Urea) were identi-fied to be different in cancer and normal subjects (p-value < 0.05). In the case of the predictive performance, the identified biomarkers achieved AUC values of 0.75, 0.79, and 0.79 according to a linear discriminate analysis, a random forest classifier, and on a support vector machine, respectively. Finally, biomarkers which showed consistent significance in pathways' gene expression as well as urine samples were identified.
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Results: We use the Dugundji-Ugi-model for coding of biochemical reactions which is based on electron shift patterns occurring during reactions. Changes of the bonds or of non-bonded valence electrons are expressed by reaction matrices. Our program calculates reaction matrices automatically on the sole basis of substrate and product chemical structures based on a new strategy for maximal common substructure determination, which allows an accurate atom mapping of the substrate and product atoms. The system has been tested for a large set of enzymatic reactions including all sub-subclasses of the EC classification system. Altogether 147 different representative reaction operators were found in the classified enzymes, 121 of which are unique with respect to an EC sub-subclass. The other 26 comprise groups of enzymes with very similar reactions, being identical with respect to the bonds formed and broken.
Conclusion: The analysis and comparison of enzymatic reactions according to their electron shift patterns is defining enzyme groups characterised by unique reaction cores. Our results demonstrate the applicability of the Dugundji-Ugi-model as a reasonable pre-classification system allowing an objective and rational view on biochemical reactions.
Availability: The program to generate reaction matrix descriptors is available upon request.
]]>We extended the analyses led by de Figueiredo and clearly show that the apparent superior performance of their tool (METATOOL) is partly due to the differences in input network sizes. We also see a conceptual problem in the comparison of tools that serve different purposes. In our opinion, metabolic path finding and elementary mode analysis are answering different biological questions, and should be considered as complementary rather than competitive approaches.
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Results: Using known evolutionary relationships among protein superfamilies in SCOP database, Support Vector Machines were trained on four sets of discriminatory features derived from the output of HHsearch. Upon validation, it was shown that the automatic classification of all profile-profile matches was superior to fixed threshold-based annotation in terms of sensitivity and specificity. The effectiveness of this approach was demonstrated by annotating several domains of unknown function from the Pfam database.
Availability: Programs and scripts implementing the methods described in this manuscript are freely available from
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Availability: The package with documentation and examples of usage is available at
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Results: We present an ANOVA periodicity detector and its Bayesian extension that can be used to discover periodic transcripts of arbitrary shapes from replicated gene expression profiles. The models are applicable when the profiles are collected at comparable time points for at least two cycles. We provide an empirical Bayes procedure for estimating parameters of the prior distributions and derive closedform expressions for the posterior probability of periodicity, enabling efficient computation. The model is applied to two data sets profiling circadian regulation in murine liver and skeletal muscle, revealing a substantial number of previously undetected non-sinusoidal periodic transcripts in each.We also apply quantitative real-timePCRto several highly ranked non-sinusoidal transcripts in liver tissue found by the model, providing independent evidence of circadian regulation of these genes.
Availability: Matlab software for estimating prior distributions and performing inference is available for download from
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Results: A Support Vector Machine (SVM) classifier is trained to reproduce the decisions made during manual editing with an accuracy of 95.0%. This implies that manual editing can be made reproducible and applied to large scale analyses. We further demonstrate that it is possible to retrain/extend the training of the classifier by providing examples of MSA annotation. Near optimal training can be achieved with only 1000 annotated sites, or roughly three samples of protein sequence alignments.
Availability: This method is implemented in the software MANUEL, licensed under the GPL. A web-based application for single and batch job is available at
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Results: We develop a general framework to assess how well computationally-derived clusters in physical interactomes overlap functional modules derived via the Gene Ontology (GO). Using this framework, we evaluate six diverse network clustering algorithms using S. cerevisiae and show that (1) the performances of these algorithms can differ substantially when run on the same network and (2) their relative performances change depending upon the topological characteristics of the network under consideration. For the specific task of function prediction in S. cerevisiae, we demonstrate that, surprisingly, a simple non-clustering guilt-by-association approach outperforms widely-used clustering based approaches that annotate a protein with the over-represented biological process and cellular component terms in its cluster; this is true over the range of clustering algorithms considered. Further analysis parameterizes performance based on the number of annotated proteins, and suggests when clustering approaches should be used for interactome functional analyses. Overall our results suggest a re-examination of when and how clustering approaches should be applied to physical interactomes, and establishes guidelines by which novel clustering approaches for biological networks should be justified and evaluated with respect to functional analysis.
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Supplementary information: Supplementary data available at Bioinformatics online.
]]>Here we present i-cite, a browser extension, which implements a client-side model of integration which improves the navigation within the rapidly increasing life science literature and links terms from it to corresponding non-textual data.
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Availability: PoreLogo is accessible as a public web server at
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Results: We developed a methodology to process DNA sequences based on inter-nucleotide distances. Our main contribution is a method to obtain genomic signatures for complete genomes, based on the inter-nucleotide distances, that are able to discriminate between different species. Using these signatures and hierarchical clustering, it is possible to build phylogenetic trees.
Phylogenetic trees lead to genome differentiation and allow the inference of phylogenetic relations. The phylogenetic trees generated in this work display related species close to each other, suggesting that the inter-nucleotide distances are able to capture essential information about the genomes.
To create the genomic signature, we construct a vector which describes the inter-nucleotide distance distribution of a complete genome and compare it with the reference distance distribution, which is the distribution of a sequence where the nucleotides are placed randomly and independently. It is the residual or relative error between the data and the reference distribution that is used to compare the DNA sequences of different organisms.
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Results: We propose criteria that will guarantee identifiability of an isoform deconvolution model on exon and splice junction arrays and in RNA-Seq. We show that up to 97% of 2256 alternatively spliced human genes selected from the RefSeq database lead to identifiable gene models in RNA-seq, with similar results in mouse. However, in the Human Exon array only 26% of these genes lead to identifiable models, and even in the most comprehensive splice junction array only 69% lead to identifiable models.
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